SparkCreateSimRels:

- Create dedup blocks from the complete queue of records matching cluster key instead of truncating the results
- Clean titles once before clustering and similarity comparisons
- Added support for filtered fields in model
- Added support for sorting List fields in model
- Added new JSONListClustering and numAuthorsTitleSuffixPrefixChain clustering functions
- Added new maxLengthMatch comparator function
- Use reduced complexity Levenshtein with threshold in levensteinTitle
- Use reduced complexity AuthorsMatch with threshold early-quit
- Use incremental Connected Component to decrease comparisons in similarity match in BlockProcessor
- Use new clusterings configuration in Dedup tests

SparkWhitelistSimRels: use left semi join for clarity and performance

SparkCreateMergeRels:
- Use new connected component algorithm that converge faster than Spark GraphX provided algorithm
- Refactored to use Windowing sorting rather than groupBy to reduce memory pressure
- Use historical pivot table to generate singleton rels, merged rels and keep continuity with dedupIds used in the past
- Comparator for pivot record selection now uses "tomorrow" as filler for missing or incorrect date instead of "2000-01-01"
- Changed generation of ids of type dedup_wf_001 to avoid collisions

DedupRecordFactory: use reduceGroups instead of mapGroups to decrease memory pressure

dhp-pace-core/src/main/java/eu/dnetlib/pace/clustering/AbstractClusteringFunction.java

@@ -14,9 +14,9 @@ import eu.dnetlib.pace.config.Config;
 
 public abstract class AbstractClusteringFunction extends AbstractPaceFunctions implements ClusteringFunction {
 
-	protected Map<String, Integer> params;
+	protected Map<String, Object> params;
 
-	public AbstractClusteringFunction(final Map<String, Integer> params) {
+	public AbstractClusteringFunction(final Map<String, Object> params) {
 		this.params = params;
 	}
 
@@ -27,7 +27,7 @@ public abstract class AbstractClusteringFunction extends AbstractPaceFunctions i
 		return fields
 			.stream()
 			.filter(f -> !f.isEmpty())
-			.map(this::normalize)
+			.map(s -> normalize(s))
 			.map(s -> filterAllStopWords(s))
 			.map(s -> doApply(conf, s))
 			.map(c -> filterBlacklisted(c, ngramBlacklist))
@@ -36,11 +36,24 @@ public abstract class AbstractClusteringFunction extends AbstractPaceFunctions i
 			.collect(Collectors.toCollection(HashSet::new));
 	}
 
-	public Map<String, Integer> getParams() {
+	public Map<String, Object> getParams() {
 		return params;
 	}
 
 	protected Integer param(String name) {
-		return params.get(name);
+		Object val = params.get(name);
+		if (val == null)
+			return null;
+		if (val instanceof Number) {
+			return ((Number) val).intValue();
+		}
+		return Integer.parseInt(val.toString());
+	}
+
+	protected int paramOrDefault(String name, int i) {
+		Integer res = param(name);
+		if (res == null)
+			res = i;
+		return res;
 	}
 }

dhp-pace-core/src/main/java/eu/dnetlib/pace/clustering/Acronyms.java

@@ -13,7 +13,7 @@ import eu.dnetlib.pace.config.Config;
 @ClusteringClass("acronyms")
 public class Acronyms extends AbstractClusteringFunction {
 
-	public Acronyms(Map<String, Integer> params) {
+	public Acronyms(Map<String, Object> params) {
 		super(params);
 	}
 

dhp-pace-core/src/main/java/eu/dnetlib/pace/clustering/ClusteringFunction.java

@@ -11,6 +11,6 @@ public interface ClusteringFunction {
 
 	public Collection<String> apply(Config config, List<String> fields);
 
-	public Map<String, Integer> getParams();
+	public Map<String, Object> getParams();
 
 }

dhp-pace-core/src/main/java/eu/dnetlib/pace/clustering/ImmutableFieldValue.java

@@ -12,7 +12,7 @@ import eu.dnetlib.pace.config.Config;
 @ClusteringClass("immutablefieldvalue")
 public class ImmutableFieldValue extends AbstractClusteringFunction {
 
-	public ImmutableFieldValue(final Map<String, Integer> params) {
+	public ImmutableFieldValue(final Map<String, Object> params) {
 		super(params);
 	}
 

dhp-pace-core/src/main/java/eu/dnetlib/pace/clustering/JSONListClustering.java

@@ -0,0 +1,69 @@
+
+package eu.dnetlib.pace.clustering;
+
+import java.util.Collection;
+import java.util.HashSet;
+import java.util.List;
+import java.util.Map;
+import java.util.stream.Collectors;
+
+import org.apache.commons.lang3.StringUtils;
+
+import com.jayway.jsonpath.Configuration;
+import com.jayway.jsonpath.DocumentContext;
+import com.jayway.jsonpath.JsonPath;
+import com.jayway.jsonpath.Option;
+
+import eu.dnetlib.pace.common.AbstractPaceFunctions;
+import eu.dnetlib.pace.config.Config;
+import eu.dnetlib.pace.util.MapDocumentUtil;
+
+@ClusteringClass("jsonlistclustering")
+public class JSONListClustering extends AbstractPaceFunctions implements ClusteringFunction {
+
+	private Map<String, Object> params;
+
+	public JSONListClustering(Map<String, Object> params) {
+		this.params = params;
+	}
+
+	@Override
+	public Map<String, Object> getParams() {
+		return params;
+	}
+
+	@Override
+	public Collection<String> apply(Config conf, List<String> fields) {
+		return fields
+			.stream()
+			.filter(f -> !f.isEmpty())
+			.map(s -> doApply(conf, s))
+			.filter(StringUtils::isNotBlank)
+			.collect(Collectors.toCollection(HashSet::new));
+	}
+
+	private String doApply(Config conf, String json) {
+		StringBuilder st = new StringBuilder(); // to build the string used for comparisons basing on the jpath into
+		// parameters
+		final DocumentContext documentContext = JsonPath
+			.using(Configuration.defaultConfiguration().addOptions(Option.SUPPRESS_EXCEPTIONS))
+			.parse(json);
+
+		// for each path in the param list
+		for (String key : params.keySet().stream().filter(k -> k.contains("jpath")).collect(Collectors.toList())) {
+			String path = params.get(key).toString();
+			String value = MapDocumentUtil.getJPathString(path, documentContext);
+			if (value == null || value.isEmpty())
+				value = "";
+			st.append(value);
+			st.append(" ");
+		}
+
+		st.setLength(st.length() - 1);
+
+		if (StringUtils.isBlank(st)) {
+			return "1";
+		}
+		return st.toString();
+	}
+}

dhp-pace-core/src/main/java/eu/dnetlib/pace/clustering/KeywordsClustering.java

@@ -11,7 +11,7 @@ import eu.dnetlib.pace.config.Config;
 @ClusteringClass("keywordsclustering")
 public class KeywordsClustering extends AbstractClusteringFunction {
 
-	public KeywordsClustering(Map<String, Integer> params) {
+	public KeywordsClustering(Map<String, Object> params) {
 		super(params);
 	}
 
@@ -19,8 +19,8 @@ public class KeywordsClustering extends AbstractClusteringFunction {
 	protected Collection<String> doApply(final Config conf, String s) {
 
 		// takes city codes and keywords codes without duplicates
-		Set<String> keywords = getKeywords(s, conf.translationMap(), params.getOrDefault("windowSize", 4));
-		Set<String> cities = getCities(s, params.getOrDefault("windowSize", 4));
+		Set<String> keywords = getKeywords(s, conf.translationMap(), paramOrDefault("windowSize", 4));
+		Set<String> cities = getCities(s, paramOrDefault("windowSize", 4));
 
 		// list of combination to return as result
 		final Collection<String> combinations = new LinkedHashSet<String>();
@@ -28,7 +28,7 @@ public class KeywordsClustering extends AbstractClusteringFunction {
 		for (String keyword : keywordsToCodes(keywords, conf.translationMap())) {
 			for (String city : citiesToCodes(cities)) {
 				combinations.add(keyword + "-" + city);
-				if (combinations.size() >= params.getOrDefault("max", 2)) {
+				if (combinations.size() >= paramOrDefault("max", 2)) {
 					return combinations;
 				}
 			}
@@ -42,8 +42,8 @@ public class KeywordsClustering extends AbstractClusteringFunction {
 		return fields
 			.stream()
 			.filter(f -> !f.isEmpty())
-			.map(this::cleanup)
-			.map(this::normalize)
+			.map(KeywordsClustering::cleanup)
+			.map(KeywordsClustering::normalize)
 			.map(s -> filterAllStopWords(s))
 			.map(s -> doApply(conf, s))
 			.map(c -> filterBlacklisted(c, ngramBlacklist))

dhp-pace-core/src/main/java/eu/dnetlib/pace/clustering/LastNameFirstInitial.java

@@ -16,7 +16,7 @@ public class LastNameFirstInitial extends AbstractClusteringFunction {
 
 	private boolean DEFAULT_AGGRESSIVE = true;
 
-	public LastNameFirstInitial(final Map<String, Integer> params) {
+	public LastNameFirstInitial(final Map<String, Object> params) {
 		super(params);
 	}
 
@@ -25,7 +25,7 @@ public class LastNameFirstInitial extends AbstractClusteringFunction {
 		return fields
 			.stream()
 			.filter(f -> !f.isEmpty())
-			.map(this::normalize)
+			.map(LastNameFirstInitial::normalize)
 			.map(s -> doApply(conf, s))
 			.map(c -> filterBlacklisted(c, ngramBlacklist))
 			.flatMap(c -> c.stream())
@@ -33,8 +33,7 @@ public class LastNameFirstInitial extends AbstractClusteringFunction {
 			.collect(Collectors.toCollection(HashSet::new));
 	}
 
-	@Override
-	protected String normalize(final String s) {
+	public static String normalize(final String s) {
 		return fixAliases(transliterate(nfd(unicodeNormalization(s))))
 			// do not compact the regexes in a single expression, would cause StackOverflowError in case of large input
 			// strings

dhp-pace-core/src/main/java/eu/dnetlib/pace/clustering/LowercaseClustering.java

@@ -15,7 +15,7 @@ import eu.dnetlib.pace.config.Config;
 @ClusteringClass("lowercase")
 public class LowercaseClustering extends AbstractClusteringFunction {
 
-	public LowercaseClustering(final Map<String, Integer> params) {
+	public LowercaseClustering(final Map<String, Object> params) {
 		super(params);
 	}
 

dhp-pace-core/src/main/java/eu/dnetlib/pace/clustering/NgramPairs.java

@@ -12,11 +12,11 @@ import eu.dnetlib.pace.config.Config;
 @ClusteringClass("ngrampairs")
 public class NgramPairs extends Ngrams {
 
-	public NgramPairs(Map<String, Integer> params) {
+	public NgramPairs(Map<String, Object> params) {
 		super(params, false);
 	}
 
-	public NgramPairs(Map<String, Integer> params, boolean sorted) {
+	public NgramPairs(Map<String, Object> params, boolean sorted) {
 		super(params, sorted);
 	}
 

dhp-pace-core/src/main/java/eu/dnetlib/pace/clustering/Ngrams.java

@@ -10,11 +10,11 @@ public class Ngrams extends AbstractClusteringFunction {
 
 	private final boolean sorted;
 
-	public Ngrams(Map<String, Integer> params) {
+	public Ngrams(Map<String, Object> params) {
 		this(params, false);
 	}
 
-	public Ngrams(Map<String, Integer> params, boolean sorted) {
+	public Ngrams(Map<String, Object> params, boolean sorted) {
 		super(params);
 		this.sorted = sorted;
 	}

dhp-pace-core/src/main/java/eu/dnetlib/pace/clustering/NumAuthorsTitleSuffixPrefixChain.java

@@ -0,0 +1,113 @@
+
+package eu.dnetlib.pace.clustering;
+
+import java.util.*;
+import java.util.stream.Collectors;
+import java.util.stream.StreamSupport;
+
+import com.google.common.base.Splitter;
+import com.google.common.collect.Sets;
+
+import eu.dnetlib.pace.config.Config;
+
+@ClusteringClass("numAuthorsTitleSuffixPrefixChain")
+public class NumAuthorsTitleSuffixPrefixChain extends AbstractClusteringFunction {
+
+	public NumAuthorsTitleSuffixPrefixChain(Map<String, Object> params) {
+		super(params);
+	}
+
+	@Override
+	public Collection<String> apply(Config conf, List<String> fields) {
+
+		try {
+			int num_authors = Math.min(Integer.parseInt(fields.get(0)), 21); // SIZE threshold is 20, +1
+
+			if (num_authors > 0) {
+				return super.apply(conf, fields.subList(1, fields.size()))
+					.stream()
+					.map(s -> num_authors + "-" + s)
+					.collect(Collectors.toList());
+			}
+		} catch (NumberFormatException e) {
+			// missing or null authors array
+		}
+
+		return Collections.emptyList();
+	}
+
+	@Override
+	protected Collection<String> doApply(Config conf, String s) {
+		return suffixPrefixChain(cleanup(s), param("mod"));
+	}
+
+	private Collection<String> suffixPrefixChain(String s, int mod) {
+		// create the list of words from the string (remove short words)
+		List<String> wordsList = Arrays
+			.stream(s.split(" "))
+			.filter(si -> si.length() > 3)
+			.collect(Collectors.toList());
+
+		final int words = wordsList.size();
+		final int letters = s.length();
+
+		// create the prefix: number of words + number of letters/mod
+		String prefix = words / mod + "-";
+
+		return doSuffixPrefixChain(wordsList, prefix);
+
+	}
+
+	private Collection<String> doSuffixPrefixChain(List<String> wordsList, String prefix) {
+
+		Set<String> set = Sets.newLinkedHashSet();
+		switch (wordsList.size()) {
+			case 0:
+				break;
+			case 1:
+				set.add(wordsList.get(0));
+				break;
+			case 2:
+				set
+					.add(
+						prefix +
+							suffix(wordsList.get(0), 3) +
+							prefix(wordsList.get(1), 3));
+
+				set
+					.add(
+						prefix +
+							prefix(wordsList.get(0), 3) +
+							suffix(wordsList.get(1), 3));
+
+				break;
+			default:
+				set
+					.add(
+						prefix +
+							suffix(wordsList.get(0), 3) +
+							prefix(wordsList.get(1), 3) +
+							suffix(wordsList.get(2), 3));
+
+				set
+					.add(
+						prefix +
+							prefix(wordsList.get(0), 3) +
+							suffix(wordsList.get(1), 3) +
+							prefix(wordsList.get(2), 3));
+				break;
+		}
+
+		return set;
+
+	}
+
+	private String suffix(String s, int len) {
+		return s.substring(s.length() - len);
+	}
+
+	private String prefix(String s, int len) {
+		return s.substring(0, len);
+	}
+
+}

dhp-pace-core/src/main/java/eu/dnetlib/pace/clustering/PersonClustering.java

@@ -17,11 +17,11 @@ import eu.dnetlib.pace.model.Person;
 @ClusteringClass("personClustering")
 public class PersonClustering extends AbstractPaceFunctions implements ClusteringFunction {
 
-	private Map<String, Integer> params;
+	private Map<String, Object> params;
 
 	private static final int MAX_TOKENS = 5;
 
-	public PersonClustering(final Map<String, Integer> params) {
+	public PersonClustering(final Map<String, Object> params) {
 		this.params = params;
 	}
 
@@ -77,7 +77,7 @@ public class PersonClustering extends AbstractPaceFunctions implements Clusterin
 //	}
 
 	@Override
-	public Map<String, Integer> getParams() {
+	public Map<String, Object> getParams() {
 		return params;
 	}
 

dhp-pace-core/src/main/java/eu/dnetlib/pace/clustering/PersonHash.java

@@ -15,7 +15,7 @@ public class PersonHash extends AbstractClusteringFunction {
 
 	private boolean DEFAULT_AGGRESSIVE = false;
 
-	public PersonHash(final Map<String, Integer> params) {
+	public PersonHash(final Map<String, Object> params) {
 		super(params);
 	}
 

dhp-pace-core/src/main/java/eu/dnetlib/pace/clustering/RandomClusteringFunction.java

@@ -8,7 +8,7 @@ import eu.dnetlib.pace.config.Config;
 
 public class RandomClusteringFunction extends AbstractClusteringFunction {
 
-	public RandomClusteringFunction(Map<String, Integer> params) {
+	public RandomClusteringFunction(Map<String, Object> params) {
 		super(params);
 	}
 

dhp-pace-core/src/main/java/eu/dnetlib/pace/clustering/SortedNgramPairs.java

@@ -1,7 +1,10 @@
 
 package eu.dnetlib.pace.clustering;
 
-import java.util.*;
+import java.util.Collection;
+import java.util.Collections;
+import java.util.List;
+import java.util.Map;
 
 import com.google.common.base.Joiner;
 import com.google.common.base.Splitter;
@@ -12,7 +15,7 @@ import eu.dnetlib.pace.config.Config;
 @ClusteringClass("sortedngrampairs")
 public class SortedNgramPairs extends NgramPairs {
 
-	public SortedNgramPairs(Map<String, Integer> params) {
+	public SortedNgramPairs(Map<String, Object> params) {
 		super(params, false);
 	}
 

dhp-pace-core/src/main/java/eu/dnetlib/pace/clustering/SpaceTrimmingFieldValue.java

@@ -15,7 +15,7 @@ import eu.dnetlib.pace.config.Config;
 @ClusteringClass("spacetrimmingfieldvalue")
 public class SpaceTrimmingFieldValue extends AbstractClusteringFunction {
 
-	public SpaceTrimmingFieldValue(final Map<String, Integer> params) {
+	public SpaceTrimmingFieldValue(final Map<String, Object> params) {
 		super(params);
 	}
 
@@ -25,7 +25,7 @@ public class SpaceTrimmingFieldValue extends AbstractClusteringFunction {
 
 		res
 			.add(
-				StringUtils.isBlank(s) ? RandomStringUtils.random(getParams().get("randomLength"))
+				StringUtils.isBlank(s) ? RandomStringUtils.random(param("randomLength"))
 					: s.toLowerCase().replaceAll("\\s+", ""));
 
 		return res;

dhp-pace-core/src/main/java/eu/dnetlib/pace/clustering/SuffixPrefix.java

@@ -12,7 +12,7 @@ import eu.dnetlib.pace.config.Config;
 @ClusteringClass("suffixprefix")
 public class SuffixPrefix extends AbstractClusteringFunction {
 
-	public SuffixPrefix(Map<String, Integer> params) {
+	public SuffixPrefix(Map<String, Object> params) {
 		super(params);
 	}
 

dhp-pace-core/src/main/java/eu/dnetlib/pace/clustering/UrlClustering.java

@@ -15,12 +15,17 @@ import eu.dnetlib.pace.config.Config;
 @ClusteringClass("urlclustering")
 public class UrlClustering extends AbstractPaceFunctions implements ClusteringFunction {
 
-	protected Map<String, Integer> params;
+	protected Map<String, Object> params;
 
-	public UrlClustering(final Map<String, Integer> params) {
+	public UrlClustering(final Map<String, Object> params) {
 		this.params = params;
 	}
 
+	@Override
+	public Map<String, Object> getParams() {
+		return params;
+	}
+
 	@Override
 	public Collection<String> apply(final Config conf, List<String> fields) {
 		try {
@@ -35,11 +40,6 @@ public class UrlClustering extends AbstractPaceFunctions implements ClusteringFu
 		}
 	}
 
-	@Override
-	public Map<String, Integer> getParams() {
-		return null;
-	}
-
 	private URL asUrl(String value) {
 		try {
 			return new URL(value);

dhp-pace-core/src/main/java/eu/dnetlib/pace/clustering/WordsStatsSuffixPrefixChain.java

@@ -11,7 +11,7 @@ import eu.dnetlib.pace.config.Config;
 @ClusteringClass("wordsStatsSuffixPrefixChain")
 public class WordsStatsSuffixPrefixChain extends AbstractClusteringFunction {
 
-	public WordsStatsSuffixPrefixChain(Map<String, Integer> params) {
+	public WordsStatsSuffixPrefixChain(Map<String, Object> params) {
 		super(params);
 	}
 

dhp-pace-core/src/main/java/eu/dnetlib/pace/clustering/WordsSuffixPrefix.java

@@ -12,7 +12,7 @@ import eu.dnetlib.pace.config.Config;
 @ClusteringClass("wordssuffixprefix")
 public class WordsSuffixPrefix extends AbstractClusteringFunction {
 
-	public WordsSuffixPrefix(Map<String, Integer> params) {
+	public WordsSuffixPrefix(Map<String, Object> params) {
 		super(params);
 	}
 

dhp-pace-core/src/main/java/eu/dnetlib/pace/common/AbstractPaceFunctions.java

@@ -16,7 +16,6 @@ import org.apache.commons.lang3.StringUtils;
 import com.google.common.base.Joiner;
 import com.google.common.base.Splitter;
 import com.google.common.collect.Iterables;
-import com.google.common.collect.Lists;
 import com.google.common.collect.Sets;
 import com.ibm.icu.text.Transliterator;
 
@@ -27,7 +26,7 @@ import eu.dnetlib.pace.clustering.NGramUtils;
  *
  * @author claudio
  */
-public abstract class AbstractPaceFunctions {
+public class AbstractPaceFunctions {
 
 	// city map to be used when translating the city names into codes
 	private static Map<String, String> cityMap = AbstractPaceFunctions
@@ -62,11 +61,14 @@ public abstract class AbstractPaceFunctions {
 
 	private static Pattern hexUnicodePattern = Pattern.compile("\\\\u(\\p{XDigit}{4})");
 
-	protected String concat(final List<String> l) {
+	private static Pattern romanNumberPattern = Pattern
+		.compile("^M{0,4}(CM|CD|D?C{0,3})(XC|XL|L?X{0,3})(IX|IV|V?I{0,3})$");
+
+	protected static String concat(final List<String> l) {
 		return Joiner.on(" ").skipNulls().join(l);
 	}
 
-	protected String cleanup(final String s) {
+	public static String cleanup(final String s) {
 		final String s1 = HTML_REGEX.matcher(s).replaceAll("");
 		final String s2 = unicodeNormalization(s1.toLowerCase());
 		final String s3 = nfd(s2);
@@ -82,7 +84,7 @@ public abstract class AbstractPaceFunctions {
 		return s12;
 	}
 
-	protected String fixXML(final String a) {
+	protected static String fixXML(final String a) {
 
 		return a
 			.replaceAll("&ndash;", " ")
@@ -91,7 +93,7 @@ public abstract class AbstractPaceFunctions {
 			.replaceAll("&minus;", " ");
 	}
 
-	protected boolean checkNumbers(final String a, final String b) {
+	protected static boolean checkNumbers(final String a, final String b) {
 		final String numbersA = getNumbers(a);
 		final String numbersB = getNumbers(b);
 		final String romansA = getRomans(a);
@@ -99,7 +101,7 @@ public abstract class AbstractPaceFunctions {
 		return !numbersA.equals(numbersB) || !romansA.equals(romansB);
 	}
 
-	protected String getRomans(final String s) {
+	protected static String getRomans(final String s) {
 		final StringBuilder sb = new StringBuilder();
 		for (final String t : s.split(" ")) {
 			sb.append(isRoman(t) ? t : "");
@@ -107,13 +109,12 @@ public abstract class AbstractPaceFunctions {
 		return sb.toString();
 	}
 
-	protected boolean isRoman(final String s) {
-		return s
-			.replaceAll("^M{0,4}(CM|CD|D?C{0,3})(XC|XL|L?X{0,3})(IX|IV|V?I{0,3})$", "qwertyuiop")
-			.equals("qwertyuiop");
+	protected static boolean isRoman(final String s) {
+		Matcher m = romanNumberPattern.matcher(s);
+		return m.matches() && m.hitEnd();
 	}
 
-	protected String getNumbers(final String s) {
+	protected static String getNumbers(final String s) {
 		final StringBuilder sb = new StringBuilder();
 		for (final String t : s.split(" ")) {
 			sb.append(isNumber(t) ? t : "");
@@ -121,7 +122,7 @@ public abstract class AbstractPaceFunctions {
 		return sb.toString();
 	}
 
-	public boolean isNumber(String strNum) {
+	public static boolean isNumber(String strNum) {
 		if (strNum == null) {
 			return false;
 		}
@@ -147,7 +148,7 @@ public abstract class AbstractPaceFunctions {
 		}
 	}
 
-	protected String removeSymbols(final String s) {
+	protected static String removeSymbols(final String s) {
 		final StringBuilder sb = new StringBuilder();
 
 		s.chars().forEach(ch -> {
@@ -157,11 +158,11 @@ public abstract class AbstractPaceFunctions {
 		return sb.toString().replaceAll("\\s+", " ");
 	}
 
-	protected boolean notNull(final String s) {
+	protected static boolean notNull(final String s) {
 		return s != null;
 	}
 
-	protected String normalize(final String s) {
+	public static String normalize(final String s) {
 		return fixAliases(transliterate(nfd(unicodeNormalization(s))))
 			.toLowerCase()
 			// do not compact the regexes in a single expression, would cause StackOverflowError in case of large input
@@ -174,16 +175,16 @@ public abstract class AbstractPaceFunctions {
 			.trim();
 	}
 
-	public String nfd(final String s) {
+	public static String nfd(final String s) {
 		return Normalizer.normalize(s, Normalizer.Form.NFD);
 	}
 
-	public String utf8(final String s) {
+	public static String utf8(final String s) {
 		byte[] bytes = s.getBytes(StandardCharsets.UTF_8);
 		return new String(bytes, StandardCharsets.UTF_8);
 	}
 
-	public String unicodeNormalization(final String s) {
+	public static String unicodeNormalization(final String s) {
 
 		Matcher m = hexUnicodePattern.matcher(s);
 		StringBuffer buf = new StringBuffer(s.length());
@@ -195,7 +196,7 @@ public abstract class AbstractPaceFunctions {
 		return buf.toString();
 	}
 
-	protected String filterStopWords(final String s, final Set<String> stopwords) {
+	protected static String filterStopWords(final String s, final Set<String> stopwords) {
 		final StringTokenizer st = new StringTokenizer(s);
 		final StringBuilder sb = new StringBuilder();
 		while (st.hasMoreTokens()) {
@@ -208,7 +209,7 @@ public abstract class AbstractPaceFunctions {
 		return sb.toString().trim();
 	}
 
-	public String filterAllStopWords(String s) {
+	public static String filterAllStopWords(String s) {
 
 		s = filterStopWords(s, stopwords_en);
 		s = filterStopWords(s, stopwords_de);
@@ -221,7 +222,8 @@ public abstract class AbstractPaceFunctions {
 		return s;
 	}
 
-	protected Collection<String> filterBlacklisted(final Collection<String> set, final Set<String> ngramBlacklist) {
+	protected static Collection<String> filterBlacklisted(final Collection<String> set,
+		final Set<String> ngramBlacklist) {
 		final Set<String> newset = Sets.newLinkedHashSet();
 		for (final String s : set) {
 			if (!ngramBlacklist.contains(s)) {
@@ -268,7 +270,7 @@ public abstract class AbstractPaceFunctions {
 		return m;
 	}
 
-	public String removeKeywords(String s, Set<String> keywords) {
+	public static String removeKeywords(String s, Set<String> keywords) {
 
 		s = " " + s + " ";
 		for (String k : keywords) {
@@ -278,39 +280,39 @@ public abstract class AbstractPaceFunctions {
 		return s.trim();
 	}
 
-	public double commonElementsPercentage(Set<String> s1, Set<String> s2) {
+	public static double commonElementsPercentage(Set<String> s1, Set<String> s2) {
 
 		double longer = Math.max(s1.size(), s2.size());
 		return (double) s1.stream().filter(s2::contains).count() / longer;
 	}
 
 	// convert the set of keywords to codes
-	public Set<String> toCodes(Set<String> keywords, Map<String, String> translationMap) {
+	public static Set<String> toCodes(Set<String> keywords, Map<String, String> translationMap) {
 		return keywords.stream().map(s -> translationMap.get(s)).collect(Collectors.toSet());
 	}
 
-	public Set<String> keywordsToCodes(Set<String> keywords, Map<String, String> translationMap) {
+	public static Set<String> keywordsToCodes(Set<String> keywords, Map<String, String> translationMap) {
 		return toCodes(keywords, translationMap);
 	}
 
-	public Set<String> citiesToCodes(Set<String> keywords) {
+	public static Set<String> citiesToCodes(Set<String> keywords) {
 		return toCodes(keywords, cityMap);
 	}
 
-	protected String firstLC(final String s) {
+	protected static String firstLC(final String s) {
 		return StringUtils.substring(s, 0, 1).toLowerCase();
 	}
 
-	protected Iterable<String> tokens(final String s, final int maxTokens) {
+	protected static Iterable<String> tokens(final String s, final int maxTokens) {
 		return Iterables.limit(Splitter.on(" ").omitEmptyStrings().trimResults().split(s), maxTokens);
 	}
 
-	public String normalizePid(String pid) {
+	public static String normalizePid(String pid) {
 		return DOI_PREFIX.matcher(pid.toLowerCase()).replaceAll("");
 	}
 
 	// get the list of keywords into the input string
-	public Set<String> getKeywords(String s1, Map<String, String> translationMap, int windowSize) {
+	public static Set<String> getKeywords(String s1, Map<String, String> translationMap, int windowSize) {
 
 		String s = s1;
 
@@ -340,7 +342,7 @@ public abstract class AbstractPaceFunctions {
 		return codes;
 	}
 
-	public Set<String> getCities(String s1, int windowSize) {
+	public static Set<String> getCities(String s1, int windowSize) {
 		return getKeywords(s1, cityMap, windowSize);
 	}
 

dhp-pace-core/src/main/java/eu/dnetlib/pace/model/ClusteringDef.java

@@ -18,7 +18,7 @@ public class ClusteringDef implements Serializable {
 
 	private List<String> fields;
 
-	private Map<String, Integer> params;
+	private Map<String, Object> params;
 
 	public ClusteringDef() {
 	}
@@ -43,11 +43,11 @@ public class ClusteringDef implements Serializable {
 		this.fields = fields;
 	}
 
-	public Map<String, Integer> getParams() {
+	public Map<String, Object> getParams() {
 		return params;
 	}
 
-	public void setParams(final Map<String, Integer> params) {
+	public void setParams(final Map<String, Object> params) {
 		this.params = params;
 	}
 

dhp-pace-core/src/main/java/eu/dnetlib/pace/model/FieldDef.java

@@ -2,6 +2,7 @@
 package eu.dnetlib.pace.model;
 
 import java.io.Serializable;
+import java.util.HashSet;
 import java.util.List;
 
 import com.fasterxml.jackson.core.JsonProcessingException;
@@ -36,6 +37,16 @@ public class FieldDef implements Serializable {
 	 */
 	private int length = -1;
 
+	private HashSet<String> filter;
+
+	private boolean sorted;
+
+	public boolean isSorted() {
+		return sorted;
+	}
+
+	private String clean;
+
 	public FieldDef() {
 	}
 
@@ -91,6 +102,30 @@ public class FieldDef implements Serializable {
 		this.path = path;
 	}
 
+	public HashSet<String> getFilter() {
+		return filter;
+	}
+
+	public void setFilter(HashSet<String> filter) {
+		this.filter = filter;
+	}
+
+	public boolean getSorted() {
+		return sorted;
+	}
+
+	public void setSorted(boolean sorted) {
+		this.sorted = sorted;
+	}
+
+	public String getClean() {
+		return clean;
+	}
+
+	public void setClean(String clean) {
+		this.clean = clean;
+	}
+
 	@Override
 	public String toString() {
 		try {

dhp-pace-core/src/main/java/eu/dnetlib/pace/model/SparkDeduper.scala

@@ -5,9 +5,9 @@ import eu.dnetlib.pace.util.{BlockProcessor, SparkReporter}
 import org.apache.spark.SparkContext
 import org.apache.spark.sql.catalyst.expressions.Literal
 import org.apache.spark.sql.expressions._
-import org.apache.spark.sql.functions.{col, lit, udf}
+import org.apache.spark.sql.functions.{col, desc, expr, lit, udf}
 import org.apache.spark.sql.types._
-import org.apache.spark.sql.{Column, Dataset, Row, functions}
+import org.apache.spark.sql.{Column, Dataset, Row, SaveMode, functions}
 
 import java.util.function.Predicate
 import java.util.stream.Collectors
@@ -80,6 +80,8 @@ case class SparkDeduper(conf: DedupConfig) extends Serializable {
         .withColumn("key", functions.explode(clusterValuesUDF(cd).apply(functions.array(inputColumns: _*))))
         // Add position column having the position of the row within the set of rows having the same key value ordered by the sorting value
         .withColumn("position", functions.row_number().over(Window.partitionBy("key").orderBy(col(model.orderingFieldName), col(model.identifierFieldName))))
+       // .withColumn("count", functions.max("position").over(Window.partitionBy("key").orderBy(col(model.orderingFieldName), col(model.identifierFieldName)).rowsBetween(Window.unboundedPreceding,Window.unboundedFollowing) ))
+       // .filter("count > 1")
 
       if (df_with_clustering_keys == null)
         df_with_clustering_keys = ds
@@ -88,20 +90,44 @@ case class SparkDeduper(conf: DedupConfig) extends Serializable {
     }
 
     //TODO: analytics
+    /*df_with_clustering_keys.groupBy(col("clustering"), col("key"))
+      .agg(expr("max(count) AS size"))
+      .orderBy(desc("size"))
+      .show*/
 
     val df_with_blocks = df_with_clustering_keys
-      // filter out rows with position exceeding the maxqueuesize parameter
-      .filter(col("position").leq(conf.getWf.getQueueMaxSize))
-      .groupBy("clustering", "key")
+      // split the clustering block into smaller blocks of queuemaxsize
+      .groupBy(col("clustering"), col("key"), functions.floor(col("position").divide(lit(conf.getWf.getQueueMaxSize))))
       .agg(functions.collect_set(functions.struct(model.schema.fieldNames.map(col): _*)).as("block"))
       .filter(functions.size(new Column("block")).gt(1))
+       .union(
+        //adjacency blocks
+        df_with_clustering_keys
+          // filter out leading and trailing elements
+          .filter(col("position").gt(conf.getWf.getSlidingWindowSize/2))
+          //.filter(col("position").lt(col("count").minus(conf.getWf.getSlidingWindowSize/2)))
+          // create small blocks of records on "the border" of maxqueuesize: getSlidingWindowSize/2 elements before and after
+          .filter(
+            col("position").mod(conf.getWf.getQueueMaxSize).lt(conf.getWf.getSlidingWindowSize/2) // slice of the start of block
+            || col("position").mod(conf.getWf.getQueueMaxSize).gt(conf.getWf.getQueueMaxSize - (conf.getWf.getSlidingWindowSize/2)) //slice of the end of the block
+          )
+          .groupBy(col("clustering"), col("key"), functions.floor((col("position") + lit(conf.getWf.getSlidingWindowSize/2)).divide(lit(conf.getWf.getQueueMaxSize))))
+          .agg(functions.collect_set(functions.struct(model.schema.fieldNames.map(col): _*)).as("block"))
+          .filter(functions.size(new Column("block")).gt(1))
+      )
 
     df_with_blocks
   }
 
   def clusterValuesUDF(cd: ClusteringDef) = {
     udf[mutable.WrappedArray[String], mutable.WrappedArray[Any]](values => {
-      values.flatMap(f => cd.clusteringFunction().apply(conf, Seq(f.toString).asJava).asScala)
+      val valueList = values.flatMap {
+        case a: mutable.WrappedArray[Any] => a.map(_.toString)
+        case s: Any => Seq(s.toString)
+      }.asJava;
+
+      mutable.WrappedArray.make(cd.clusteringFunction().apply(conf, valueList).toArray())
+
     })
   }
 

dhp-pace-core/src/main/java/eu/dnetlib/pace/model/SparkModel.scala

@@ -1,13 +1,16 @@
 package eu.dnetlib.pace.model
 
 import com.jayway.jsonpath.{Configuration, JsonPath}
+import eu.dnetlib.pace.common.AbstractPaceFunctions
 import eu.dnetlib.pace.config.{DedupConfig, Type}
 import eu.dnetlib.pace.util.MapDocumentUtil
+import org.apache.commons.lang3.StringUtils
 import org.apache.spark.sql.catalyst.encoders.RowEncoder
 import org.apache.spark.sql.catalyst.expressions.GenericRowWithSchema
 import org.apache.spark.sql.types.{DataTypes, Metadata, StructField, StructType}
 import org.apache.spark.sql.{Dataset, Row}
 
+import java.util.Locale
 import java.util.regex.Pattern
 import scala.collection.JavaConverters._
 
@@ -60,7 +63,7 @@ case class SparkModel(conf: DedupConfig) {
     values(identityFieldPosition) = MapDocumentUtil.getJPathString(conf.getWf.getIdPath, documentContext)
 
     schema.fieldNames.zipWithIndex.foldLeft(values) {
-      case ((res, (fname, index))) => {
+      case ((res, (fname, index))) =>
         val fdef = conf.getPace.getModelMap.get(fname)
 
         if (fdef != null) {
@@ -96,13 +99,52 @@ case class SparkModel(conf: DedupConfig) {
             case Type.DoubleArray =>
               MapDocumentUtil.getJPathArray(fdef.getPath, json)
           }
+
+          val filter = fdef.getFilter
+
+          if (StringUtils.isNotBlank(fdef.getClean)) {
+            res(index) = res(index) match {
+              case x: Seq[String] => x.map(clean(_, fdef.getClean)).toSeq
+              case _ => clean(res(index).toString, fdef.getClean)
+            }
+          }
+
+          if (filter != null && !filter.isEmpty) {
+            res(index) = res(index) match {
+              case x: String if filter.contains(x.toLowerCase(Locale.ROOT)) => null
+              case x: Seq[String] => x.filter(s => !filter.contains(s.toLowerCase(Locale.ROOT))).toSeq
+              case _ => res(index)
+            }
+          }
+
+          if (fdef.getSorted) {
+            res(index) = res(index) match {
+              case x: Seq[String] => x.sorted.toSeq
+              case _ => res(index)
+            }
+          }
         }
 
         res
-      }
     }
 
     new GenericRowWithSchema(values, schema)
   }
+
+  def clean(value: String, cleantype: String) : String = {
+    val res = cleantype match {
+      case "title" => AbstractPaceFunctions.cleanup(value)
+      case _ => value
+    }
+
+//    if (!res.equals(AbstractPaceFunctions.normalize(value))) {
+//      println(res)
+//      println(AbstractPaceFunctions.normalize(value))
+//      println()
+//    }
+
+    res
+  }
+
 }
 

dhp-pace-core/src/main/java/eu/dnetlib/pace/tree/AuthorsMatch.java

@@ -23,7 +23,6 @@ public class AuthorsMatch extends AbstractListComparator {
 	private String MODE; // full or surname
 	private int SIZE_THRESHOLD;
 	private String TYPE; // count or percentage
-	private int common;
 
 	public AuthorsMatch(Map<String, String> params) {
 		super(params, new com.wcohen.ss.JaroWinkler());
@@ -35,7 +34,6 @@ public class AuthorsMatch extends AbstractListComparator {
 		FULLNAME_THRESHOLD = Double.parseDouble(params.getOrDefault("fullname_th", "0.9"));
 		SIZE_THRESHOLD = Integer.parseInt(params.getOrDefault("size_th", "20"));
 		TYPE = params.getOrDefault("type", "percentage");
-		common = 0;
 	}
 
 	protected AuthorsMatch(double w, AbstractStringDistance ssalgo) {
@@ -44,22 +42,27 @@ public class AuthorsMatch extends AbstractListComparator {
 
 	@Override
 	public double compare(final List<String> a, final List<String> b, final Config conf) {
-
 		if (a.isEmpty() || b.isEmpty())
 			return -1;
 
 		if (a.size() > SIZE_THRESHOLD || b.size() > SIZE_THRESHOLD)
 			return 1.0;
 
-		List<Person> aList = a.stream().map(author -> new Person(author, false)).collect(Collectors.toList());
+		int maxMiss = Integer.MAX_VALUE;
 		List<Person> bList = b.stream().map(author -> new Person(author, false)).collect(Collectors.toList());
 
-		common = 0;
+		Double threshold = getDoubleParam("threshold");
+
+		if (threshold != null && threshold >= 0.0 && threshold <= 1.0 && a.size() == b.size()) {
+			maxMiss = (int) Math.floor((1 - threshold) * Math.max(a.size(), b.size()));
+		}
+
+		int common = 0;
 		// compare each element of List1 with each element of List2
-		for (Person p1 : aList)
+		for (int i = 0; i < a.size(); i++) {
+			Person p1 = new Person(a.get(i), false);
 
 			for (Person p2 : bList) {
-
 				// both persons are inaccurate
 				if (!p1.isAccurate() && !p2.isAccurate()) {
 					// compare just normalized fullnames
@@ -118,11 +121,15 @@ public class AuthorsMatch extends AbstractListComparator {
 					}
 
 				}
-
 			}
 
+			if (i - common > maxMiss) {
+				return 0.0;
+			}
+		}
+
 		// normalization factor to compute the score
-		int normFactor = aList.size() == bList.size() ? aList.size() : (aList.size() + bList.size() - common);
+		int normFactor = a.size() == b.size() ? a.size() : (a.size() + b.size() - common);
 
 		if (TYPE.equals("percentage")) {
 			return (double) common / normFactor;

dhp-pace-core/src/main/java/eu/dnetlib/pace/tree/InstanceTypeMatch.java

@@ -25,6 +25,7 @@ public class InstanceTypeMatch extends AbstractListComparator {
 		translationMap.put("Conference object", "*");
 		translationMap.put("Other literature type", "*");
 		translationMap.put("Unknown", "*");
+		translationMap.put("UNKNOWN", "*");
 
 		// article types
 		translationMap.put("Article", "Article");
@@ -76,5 +77,4 @@ public class InstanceTypeMatch extends AbstractListComparator {
 	protected double normalize(final double d) {
 		return d;
 	}
-
 }

dhp-pace-core/src/main/java/eu/dnetlib/pace/tree/LevensteinTitle.java

@@ -3,6 +3,7 @@ package eu.dnetlib.pace.tree;
 
 import java.util.Map;
 
+import org.apache.commons.lang3.StringUtils;
 import org.apache.commons.logging.Log;
 import org.apache.commons.logging.LogFactory;
 
@@ -30,16 +31,25 @@ public class LevensteinTitle extends AbstractStringComparator {
 	}
 
 	@Override
-	public double distance(final String a, final String b, final Config conf) {
-		final String ca = cleanup(a);
-		final String cb = cleanup(b);
-
+	public double distance(final String ca, final String cb, final Config conf) {
 		final boolean check = checkNumbers(ca, cb);
 
 		if (check)
 			return 0.5;
 
-		return normalize(ssalgo.score(ca, cb), ca.length(), cb.length());
+		Double threshold = getDoubleParam("threshold");
+
+		// reduce Levenshtein algo complexity when target threshold is known
+		if (threshold != null && threshold >= 0.0 && threshold <= 1.0) {
+			int maxdistance = (int) Math.floor((1 - threshold) * Math.max(ca.length(), cb.length()));
+			int score = StringUtils.getLevenshteinDistance(ca, cb, maxdistance);
+			if (score == -1) {
+				return 0;
+			}
+			return normalize(score, ca.length(), cb.length());
+		} else {
+			return normalize(StringUtils.getLevenshteinDistance(ca, cb), ca.length(), cb.length());
+		}
 	}
 
 	private double normalize(final double score, final int la, final int lb) {

dhp-pace-core/src/main/java/eu/dnetlib/pace/tree/MaxLengthMatch.java

@@ -0,0 +1,29 @@
+
+package eu.dnetlib.pace.tree;
+
+import java.util.Map;
+
+import eu.dnetlib.pace.config.Config;
+import eu.dnetlib.pace.tree.support.AbstractStringComparator;
+import eu.dnetlib.pace.tree.support.ComparatorClass;
+
+@ComparatorClass("maxLengthMatch")
+public class MaxLengthMatch extends AbstractStringComparator {
+
+	private final int limit;
+
+	public MaxLengthMatch(Map<String, String> params) {
+		super(params);
+
+		limit = Integer.parseInt(params.getOrDefault("limit", "200"));
+	}
+
+	@Override
+	public double compare(String a, String b, final Config conf) {
+		return a.length() < limit && b.length() < limit ? 1.0 : -1.0;
+	}
+
+	protected String toString(final Object object) {
+		return toFirstString(object);
+	}
+}

dhp-pace-core/src/main/java/eu/dnetlib/pace/tree/support/AbstractComparator.java

@@ -127,4 +127,14 @@ public abstract class AbstractComparator<T> extends AbstractPaceFunctions implem
 		return this.weight;
 	}
 
+	public Double getDoubleParam(String name) {
+		String svalue = params.get(name);
+
+		try {
+			return Double.parseDouble(svalue);
+		} catch (Throwable t) {
+		}
+
+		return null;
+	}
 }

dhp-pace-core/src/main/java/eu/dnetlib/pace/util/BlockProcessor.java

@@ -67,8 +67,10 @@ public class BlockProcessor {
 
 	private void processRows(final List<Row> queue, final Reporter context) {
 
-		for (int pivotPos = 0; pivotPos < queue.size(); pivotPos++) {
-			final Row pivot = queue.get(pivotPos);
+		IncrementalConnectedComponents icc = new IncrementalConnectedComponents(queue.size());
+
+		for (int i = 0; i < queue.size(); i++) {
+			final Row pivot = queue.get(i);
 
 			final String idPivot = pivot.getString(identifierFieldPos); // identifier
 			final Object fieldsPivot = getJavaValue(pivot, orderFieldPos);
@@ -76,9 +78,9 @@ public class BlockProcessor {
 			final WfConfig wf = dedupConf.getWf();
 
 			if (fieldPivot != null) {
-				int i = 0;
-				for (int windowPos = pivotPos + 1; windowPos < queue.size(); windowPos++) {
-					final Row curr = queue.get(windowPos);
+				for (int j = icc.nextUnconnected(i, i + 1); j >= 0
+					&& j < queue.size(); j = icc.nextUnconnected(i, j + 1)) {
+					final Row curr = queue.get(j);
 					final String idCurr = curr.getString(identifierFieldPos); // identifier
 
 					if (mustSkip(idCurr)) {
@@ -86,7 +88,7 @@ public class BlockProcessor {
 						break;
 					}
 
-					if (++i > wf.getSlidingWindowSize()) {
+					if (wf.getSlidingWindowSize() > 0 && (j - i) > wf.getSlidingWindowSize()) {
 						break;
 					}
 
@@ -97,7 +99,9 @@ public class BlockProcessor {
 
 						final TreeProcessor treeProcessor = new TreeProcessor(dedupConf);
 
-						emitOutput(treeProcessor.compare(pivot, curr), idPivot, idCurr, context);
+						if (emitOutput(treeProcessor.compare(pivot, curr), idPivot, idCurr, context)) {
+							icc.connect(i, j);
+						}
 					}
 				}
 			}
@@ -115,7 +119,8 @@ public class BlockProcessor {
 		return null;
 	}
 
-	private void emitOutput(final boolean result, final String idPivot, final String idCurr, final Reporter context) {
+	private boolean emitOutput(final boolean result, final String idPivot, final String idCurr,
+		final Reporter context) {
 
 		if (result) {
 			if (idPivot.compareTo(idCurr) <= 0) {
@@ -127,6 +132,8 @@ public class BlockProcessor {
 		} else {
 			context.incrementCounter(dedupConf.getWf().getEntityType(), "d < " + dedupConf.getWf().getThreshold(), 1);
 		}
+
+		return result;
 	}
 
 	private boolean mustSkip(final String idPivot) {
@@ -142,5 +149,4 @@ public class BlockProcessor {
 
 		context.emit(type, from, to);
 	}
-
 }

dhp-pace-core/src/main/java/eu/dnetlib/pace/util/IncrementalConnectedComponents.java

@@ -0,0 +1,50 @@
+
+package eu.dnetlib.pace.util;
+
+import java.util.BitSet;
+
+public class IncrementalConnectedComponents {
+	final private int size;
+
+	final private BitSet[] indexes;
+
+	IncrementalConnectedComponents(int size) {
+		this.size = size;
+		this.indexes = new BitSet[size];
+	}
+
+	public void connect(int i, int j) {
+		if (indexes[i] == null) {
+			if (indexes[j] == null) {
+				indexes[i] = new BitSet(size);
+			} else {
+				indexes[i] = indexes[j];
+			}
+		} else {
+			if (indexes[j] != null && indexes[i] != indexes[j]) {
+				// merge adjacency lists for i and j
+				indexes[i].or(indexes[j]);
+			}
+		}
+
+		indexes[i].set(i);
+		indexes[i].set(j);
+		indexes[j] = indexes[i];
+	}
+
+	public int nextUnconnected(int i, int j) {
+		if (indexes[i] == null) {
+			return j;
+		}
+		int result = indexes[i].nextClearBit(j);
+
+		return (result >= size) ? -1 : result;
+	}
+
+	public BitSet getConnections(int i) {
+		if (indexes[i] == null) {
+			return null;
+		}
+		return indexes[i];
+	}
+}

dhp-pace-core/src/main/java/eu/dnetlib/pace/util/MapDocumentUtil.java

@@ -97,6 +97,8 @@ public class MapDocumentUtil {
 			Object o = json.read(jsonPath);
 			if (o instanceof String)
 				return (String) o;
+			if (o instanceof Number)
+				return (String) o.toString();
 			if (o instanceof JSONArray && ((JSONArray) o).size() > 0)
 				return (String) ((JSONArray) o).get(0);
 			return "";

dhp-pace-core/src/main/java/eu/dnetlib/pace/util/PaceResolver.java

@@ -40,7 +40,7 @@ public class PaceResolver implements Serializable {
 				Collectors.toMap(cl -> cl.getAnnotation(ComparatorClass.class).value(), cl -> (Class<Comparator>) cl));
 	}
 
-	public ClusteringFunction getClusteringFunction(String name, Map<String, Integer> params) throws PaceException {
+	public ClusteringFunction getClusteringFunction(String name, Map<String, Object> params) throws PaceException {
 		try {
 			return clusteringFunctions.get(name).getDeclaredConstructor(Map.class).newInstance(params);
 		} catch (InstantiationException | IllegalAccessException | InvocationTargetException

dhp-pace-core/src/test/java/eu/dnetlib/pace/clustering/ClusteringFunctionTest.java

@@ -15,7 +15,7 @@ import eu.dnetlib.pace.config.DedupConfig;
 
 public class ClusteringFunctionTest extends AbstractPaceTest {
 
-	private static Map<String, Integer> params;
+	private static Map<String, Object> params;
 	private static DedupConfig conf;
 
 	@BeforeAll
@@ -40,10 +40,10 @@ public class ClusteringFunctionTest extends AbstractPaceTest {
 
 	@Test
 	public void testNgram() {
-		params.put("ngramLen", 3);
-		params.put("max", 8);
-		params.put("maxPerToken", 2);
-		params.put("minNgramLen", 1);
+		params.put("ngramLen", "3");
+		params.put("max", "8");
+		params.put("maxPerToken", "2");
+		params.put("minNgramLen", "1");
 
 		final ClusteringFunction ngram = new Ngrams(params);
 
@@ -54,8 +54,8 @@ public class ClusteringFunctionTest extends AbstractPaceTest {
 
 	@Test
 	public void testNgramPairs() {
-		params.put("ngramLen", 3);
-		params.put("max", 2);
+		params.put("ngramLen", "3");
+		params.put("max", "2");
 
 		final ClusteringFunction np = new NgramPairs(params);
 
@@ -66,8 +66,8 @@ public class ClusteringFunctionTest extends AbstractPaceTest {
 
 	@Test
 	public void testSortedNgramPairs() {
-		params.put("ngramLen", 3);
-		params.put("max", 2);
+		params.put("ngramLen", "3");
+		params.put("max", "2");
 
 		final ClusteringFunction np = new SortedNgramPairs(params);
 
@@ -87,9 +87,9 @@ public class ClusteringFunctionTest extends AbstractPaceTest {
 
 	@Test
 	public void testAcronym() {
-		params.put("max", 4);
-		params.put("minLen", 1);
-		params.put("maxLen", 3);
+		params.put("max", "4");
+		params.put("minLen", "1");
+		params.put("maxLen", "3");
 
 		final ClusteringFunction acro = new Acronyms(params);
 
@@ -100,8 +100,8 @@ public class ClusteringFunctionTest extends AbstractPaceTest {
 
 	@Test
 	public void testSuffixPrefix() {
-		params.put("len", 3);
-		params.put("max", 4);
+		params.put("len", "3");
+		params.put("max", "4");
 
 		final ClusteringFunction sp = new SuffixPrefix(params);
 
@@ -109,8 +109,8 @@ public class ClusteringFunctionTest extends AbstractPaceTest {
 		System.out.println(s);
 		System.out.println(sp.apply(conf, Lists.newArrayList(s)));
 
-		params.put("len", 3);
-		params.put("max", 1);
+		params.put("len", "3");
+		params.put("max", "1");
 
 		System.out.println(sp.apply(conf, Lists.newArrayList("Framework for general-purpose deduplication")));
 	}
@@ -118,8 +118,8 @@ public class ClusteringFunctionTest extends AbstractPaceTest {
 	@Test
 	public void testWordsSuffixPrefix() {
 
-		params.put("len", 3);
-		params.put("max", 4);
+		params.put("len", "3");
+		params.put("max", "4");
 
 		final ClusteringFunction sp = new WordsSuffixPrefix(params);
 
@@ -130,7 +130,7 @@ public class ClusteringFunctionTest extends AbstractPaceTest {
 
 	@Test
 	public void testWordsStatsSuffixPrefix() {
-		params.put("mod", 10);
+		params.put("mod", "10");
 
 		final ClusteringFunction sp = new WordsStatsSuffixPrefixChain(params);
 
@@ -167,7 +167,7 @@ public class ClusteringFunctionTest extends AbstractPaceTest {
 	@Test
 	public void testFieldValue() {
 
-		params.put("randomLength", 5);
+		params.put("randomLength", "5");
 
 		final ClusteringFunction sp = new SpaceTrimmingFieldValue(params);
 

dhp-pace-core/src/test/java/eu/dnetlib/pace/util/IncrementalConnectedComponentsTest.java

@@ -0,0 +1,40 @@
+
+package eu.dnetlib.pace.util;
+
+import static org.junit.jupiter.api.Assertions.assertEquals;
+import static org.junit.jupiter.api.Assertions.assertNull;
+
+import org.junit.jupiter.api.Test;
+
+public class IncrementalConnectedComponentsTest {
+
+	@Test
+	public void transitiveClosureTest() {
+		IncrementalConnectedComponents icc = new IncrementalConnectedComponents(10);
+
+		icc.connect(0, 1);
+		icc.connect(0, 2);
+		icc.connect(0, 3);
+
+		icc.connect(1, 2);
+		icc.connect(1, 4);
+		icc.connect(1, 5);
+
+		icc.connect(6, 7);
+		icc.connect(6, 9);
+
+		assertEquals(icc.getConnections(0).toString(), "{0, 1, 2, 3, 4, 5}");
+		assertEquals(icc.getConnections(1).toString(), "{0, 1, 2, 3, 4, 5}");
+		assertEquals(icc.getConnections(2).toString(), "{0, 1, 2, 3, 4, 5}");
+		assertEquals(icc.getConnections(3).toString(), "{0, 1, 2, 3, 4, 5}");
+		assertEquals(icc.getConnections(4).toString(), "{0, 1, 2, 3, 4, 5}");
+		assertEquals(icc.getConnections(5).toString(), "{0, 1, 2, 3, 4, 5}");
+
+		assertEquals(icc.getConnections(6).toString(), "{6, 7, 9}");
+		assertEquals(icc.getConnections(7).toString(), "{6, 7, 9}");
+		assertEquals(icc.getConnections(9).toString(), "{6, 7, 9}");
+
+		assertNull(icc.getConnections(8));
+	}
+
+}

dhp-workflows/dhp-dedup-openaire/src/main/java/eu/dnetlib/dhp/oa/dedup/AbstractSparkAction.java

@@ -101,6 +101,10 @@ abstract class AbstractSparkAction implements Serializable {
 		return SparkSession.builder().config(conf).getOrCreate();
 	}
 
+	protected static SparkSession getSparkWithHiveSession(SparkConf conf) {
+		return SparkSession.builder().enableHiveSupport().config(conf).getOrCreate();
+	}
+
 	protected static <T> void save(Dataset<T> dataset, String outPath, SaveMode mode) {
 		dataset.write().option("compression", "gzip").mode(mode).json(outPath);
 	}

dhp-workflows/dhp-dedup-openaire/src/main/java/eu/dnetlib/dhp/oa/dedup/DedupRecordFactory.java

@@ -2,20 +2,19 @@
 package eu.dnetlib.dhp.oa.dedup;
 
 import java.lang.reflect.InvocationTargetException;
-import java.util.*;
-import java.util.stream.Collectors;
+import java.util.ArrayList;
+import java.util.Iterator;
+import java.util.List;
 
-import org.apache.commons.beanutils.BeanUtils;
-import org.apache.commons.lang3.StringUtils;
 import org.apache.spark.api.java.function.MapFunction;
-import org.apache.spark.api.java.function.MapGroupsFunction;
+import org.apache.spark.api.java.function.ReduceFunction;
 import org.apache.spark.sql.Dataset;
 import org.apache.spark.sql.Encoders;
+import org.apache.spark.sql.Row;
 import org.apache.spark.sql.SparkSession;
 
 import com.fasterxml.jackson.databind.DeserializationFeature;
 import com.fasterxml.jackson.databind.ObjectMapper;
-import com.google.common.collect.Lists;
 
 import eu.dnetlib.dhp.oa.dedup.model.Identifier;
 import eu.dnetlib.dhp.oa.merge.AuthorMerger;
@@ -41,88 +40,91 @@ public class DedupRecordFactory {
 		long ts = System.currentTimeMillis();
 
 		// <id, json_entity>
-		Dataset<Tuple2<String, T>> entities = spark
+		Dataset<Row> entities = spark
 			.read()
-			.textFile(entitiesInputPath)
+			.schema(Encoders.bean(clazz).schema())
+			.json(entitiesInputPath)
+			.as(Encoders.bean(clazz))
 			.map(
-				(MapFunction<String, Tuple2<String, T>>) it -> {
-					T entity = OBJECT_MAPPER.readValue(it, clazz);
+				(MapFunction<T, Tuple2<String, T>>) entity -> {
 					return new Tuple2<>(entity.getId(), entity);
 				},
-				Encoders.tuple(Encoders.STRING(), Encoders.kryo(clazz)));
+				Encoders.tuple(Encoders.STRING(), Encoders.kryo(clazz)))
+			.selectExpr("_1 AS id", "_2 AS kryoObject");
 
 		// <source, target>: source is the dedup_id, target is the id of the mergedIn
-		Dataset<Tuple2<String, String>> mergeRels = spark
+		Dataset<Row> mergeRels = spark
 			.read()
 			.load(mergeRelsInputPath)
-			.as(Encoders.bean(Relation.class))
 			.where("relClass == 'merges'")
-			.map(
-				(MapFunction<Relation, Tuple2<String, String>>) r -> new Tuple2<>(r.getSource(), r.getTarget()),
-				Encoders.tuple(Encoders.STRING(), Encoders.STRING()));
+			.selectExpr("source as dedupId", "target as id");
 
 		return mergeRels
-			.joinWith(entities, mergeRels.col("_2").equalTo(entities.col("_1")), "inner")
+			.join(entities, "id")
+			.select("dedupId", "kryoObject")
+			.as(Encoders.tuple(Encoders.STRING(), Encoders.kryo(clazz)))
+			.groupByKey((MapFunction<Tuple2<String, T>, String>) Tuple2::_1, Encoders.STRING())
+			.reduceGroups(
+				(ReduceFunction<Tuple2<String, T>>) (t1, t2) -> new Tuple2<>(t1._1(),
+					reduceEntity(t1._1(), t1._2(), t2._2(), clazz)))
 			.map(
-				(MapFunction<Tuple2<Tuple2<String, String>, Tuple2<String, T>>, Tuple2<String, T>>) value -> new Tuple2<>(
-					value._1()._1(), value._2()._2()),
-				Encoders.tuple(Encoders.STRING(), Encoders.kryo(clazz)))
-			.groupByKey(
-				(MapFunction<Tuple2<String, T>, String>) Tuple2::_1, Encoders.STRING())
-			.mapGroups(
-				(MapGroupsFunction<String, Tuple2<String, T>, T>) (key,
-					values) -> entityMerger(key, values, ts, dataInfo, clazz),
+				(MapFunction<Tuple2<String, Tuple2<String, T>>, T>) t -> {
+					T res = t._2()._2();
+					res.setDataInfo(dataInfo);
+					res.setLastupdatetimestamp(ts);
+					return res;
+				},
 				Encoders.bean(clazz));
 	}
 
+	public static <T extends OafEntity> T reduceEntity(
+		String id, T entity, T duplicate, Class<T> clazz) {
+
+		int compare = new IdentifierComparator()
+			.compare(Identifier.newInstance(entity), Identifier.newInstance(duplicate));
+
+		if (compare > 0) {
+			T swap = duplicate;
+			duplicate = entity;
+			entity = swap;
+		}
+
+		entity.mergeFrom(duplicate);
+		entity.setId(id);
+
+		if (ModelSupport.isSubClass(duplicate, Result.class)) {
+			Result re = (Result) entity;
+			Result rd = (Result) duplicate;
+
+			List<List<Author>> authors = new ArrayList<>();
+			if (re.getAuthor() != null) {
+				authors.add(re.getAuthor());
+			}
+			if (rd.getAuthor() != null) {
+				authors.add(rd.getAuthor());
+			}
+
+			re.setAuthor(AuthorMerger.merge(authors));
+		}
+
+		return entity;
+	}
+
 	public static <T extends OafEntity> T entityMerger(
 		String id, Iterator<Tuple2<String, T>> entities, long ts, DataInfo dataInfo, Class<T> clazz)
 		throws IllegalAccessException, InstantiationException, InvocationTargetException {
+		T base = entities.next()._2();
 
-		final Comparator<Identifier<T>> idComparator = new IdentifierComparator<>();
-
-		final LinkedList<T> entityList = Lists
-			.newArrayList(entities)
-			.stream()
-			.map(t -> Identifier.newInstance(t._2()))
-			.sorted(idComparator)
-			.map(Identifier::getEntity)
-			.collect(Collectors.toCollection(LinkedList::new));
-
-		final T entity = clazz.newInstance();
-		final T first = entityList.removeFirst();
-
-		BeanUtils.copyProperties(entity, first);
-
-		final List<List<Author>> authors = Lists.newArrayList();
-
-		entityList
-			.forEach(
-				duplicate -> {
-					entity.mergeFrom(duplicate);
-					if (ModelSupport.isSubClass(duplicate, Result.class)) {
-						Result r1 = (Result) duplicate;
-						Optional
-							.ofNullable(r1.getAuthor())
-							.ifPresent(a -> authors.add(a));
-					}
-				});
-
-		// set authors and date
-		if (ModelSupport.isSubClass(entity, Result.class)) {
-			Optional
-				.ofNullable(((Result) entity).getAuthor())
-				.ifPresent(a -> authors.add(a));
-
-			((Result) entity).setAuthor(AuthorMerger.merge(authors));
+		while (entities.hasNext()) {
+			T duplicate = entities.next()._2();
+			if (duplicate != null)
+				base = reduceEntity(id, base, duplicate, clazz);
 		}
 
-		entity.setId(id);
+		base.setDataInfo(dataInfo);
+		base.setLastupdatetimestamp(ts);
 
-		entity.setLastupdatetimestamp(ts);
-		entity.setDataInfo(dataInfo);
-
-		return entity;
+		return base;
 	}
 
 }

dhp-workflows/dhp-dedup-openaire/src/main/java/eu/dnetlib/dhp/oa/dedup/IdGenerator.java

@@ -1,6 +1,7 @@
 
 package eu.dnetlib.dhp.oa.dedup;
 
+import static eu.dnetlib.dhp.utils.DHPUtils.md5;
 import static org.apache.commons.lang3.StringUtils.substringAfter;
 import static org.apache.commons.lang3.StringUtils.substringBefore;
 
@@ -14,33 +15,36 @@ import eu.dnetlib.dhp.schema.oaf.utils.PidType;
 public class IdGenerator implements Serializable {
 
 	// pick the best pid from the list (consider date and pidtype)
-	public static <T extends OafEntity> String generate(List<Identifier<T>> pids, String defaultID) {
+	public static <T extends OafEntity> String generate(List<? extends Identifier> pids, String defaultID) {
 		if (pids == null || pids.isEmpty())
 			return defaultID;
 
 		return generateId(pids);
 	}
 
-	private static <T extends OafEntity> String generateId(List<Identifier<T>> pids) {
-		Identifier<T> bp = pids
+	private static String generateId(List<? extends Identifier> pids) {
+		Identifier bp = pids
 			.stream()
 			.min(Identifier::compareTo)
 			.orElseThrow(() -> new IllegalStateException("unable to generate id"));
 
-		String prefix = substringBefore(bp.getOriginalID(), "|");
-		String ns = substringBefore(substringAfter(bp.getOriginalID(), "|"), "::");
-		String suffix = substringAfter(bp.getOriginalID(), "::");
+		return generate(bp.getOriginalID());
+	}
+
+	public static String generate(String originalId) {
+		String prefix = substringBefore(originalId, "|");
+		String ns = substringBefore(substringAfter(originalId, "|"), "::");
+		String suffix = substringAfter(originalId, "::");
 
 		final String pidType = substringBefore(ns, "_");
 		if (PidType.isValid(pidType)) {
 			return prefix + "|" + dedupify(ns) + "::" + suffix;
 		} else {
-			return prefix + "|dedup_wf_001::" + suffix;
+			return prefix + "|dedup_wf_001::" + md5(originalId); // hash the whole originalId to avoid collisions
 		}
 	}
 
 	private static String dedupify(String ns) {
-
 		StringBuilder prefix;
 		if (PidType.valueOf(substringBefore(ns, "_")) == PidType.openorgs) {
 			prefix = new StringBuilder(substringBefore(ns, "_"));
@@ -53,5 +57,4 @@ public class IdGenerator implements Serializable {
 		}
 		return prefix.substring(0, 12);
 	}
-
 }

dhp-workflows/dhp-dedup-openaire/src/main/java/eu/dnetlib/dhp/oa/dedup/SparkCreateMergeRels.java

@@ -1,51 +1,47 @@
 
 package eu.dnetlib.dhp.oa.dedup;
 
-import static eu.dnetlib.dhp.schema.common.ModelConstants.DNET_PROVENANCE_ACTIONS;
-import static eu.dnetlib.dhp.schema.common.ModelConstants.PROVENANCE_DEDUP;
-
-import java.io.IOException;
-import java.util.*;
-import java.util.stream.Collectors;
-
-import org.apache.commons.io.IOUtils;
-import org.apache.spark.SparkConf;
-import org.apache.spark.api.java.JavaPairRDD;
-import org.apache.spark.api.java.JavaSparkContext;
-import org.apache.spark.api.java.function.FlatMapFunction;
-import org.apache.spark.api.java.function.MapFunction;
-import org.apache.spark.api.java.function.MapGroupsFunction;
-import org.apache.spark.graphx.Edge;
-import org.apache.spark.rdd.RDD;
-import org.apache.spark.sql.Dataset;
-import org.apache.spark.sql.Encoders;
-import org.apache.spark.sql.SaveMode;
-import org.apache.spark.sql.SparkSession;
-import org.dom4j.DocumentException;
-import org.slf4j.Logger;
-import org.slf4j.LoggerFactory;
-import org.xml.sax.SAXException;
-
-import com.google.common.collect.Lists;
 import com.google.common.hash.Hashing;
-
+import com.kwartile.lib.cc.ConnectedComponent;
 import eu.dnetlib.dhp.application.ArgumentApplicationParser;
-import eu.dnetlib.dhp.oa.dedup.graph.ConnectedComponent;
-import eu.dnetlib.dhp.oa.dedup.graph.GraphProcessor;
-import eu.dnetlib.dhp.oa.dedup.model.Identifier;
 import eu.dnetlib.dhp.schema.common.EntityType;
 import eu.dnetlib.dhp.schema.common.ModelConstants;
 import eu.dnetlib.dhp.schema.common.ModelSupport;
-import eu.dnetlib.dhp.schema.oaf.DataInfo;
-import eu.dnetlib.dhp.schema.oaf.OafEntity;
-import eu.dnetlib.dhp.schema.oaf.Qualifier;
-import eu.dnetlib.dhp.schema.oaf.Relation;
+import eu.dnetlib.dhp.schema.oaf.*;
+import eu.dnetlib.dhp.schema.oaf.utils.PidType;
 import eu.dnetlib.dhp.utils.ISLookupClientFactory;
 import eu.dnetlib.enabling.is.lookup.rmi.ISLookUpException;
 import eu.dnetlib.enabling.is.lookup.rmi.ISLookUpService;
 import eu.dnetlib.pace.config.DedupConfig;
-import eu.dnetlib.pace.util.MapDocumentUtil;
-import scala.Tuple2;
+import org.apache.commons.io.IOUtils;
+import org.apache.commons.lang3.StringUtils;
+import org.apache.spark.SparkConf;
+import org.apache.spark.api.java.function.FlatMapFunction;
+import org.apache.spark.sql.Dataset;
+import org.apache.spark.sql.*;
+import org.apache.spark.sql.catalyst.encoders.RowEncoder;
+import org.apache.spark.sql.expressions.UserDefinedFunction;
+import org.apache.spark.sql.expressions.Window;
+import org.apache.spark.sql.expressions.WindowSpec;
+import org.apache.spark.sql.types.DataTypes;
+import org.apache.spark.sql.types.StructType;
+import org.dom4j.DocumentException;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+import org.xml.sax.SAXException;
+import scala.Tuple3;
+import scala.collection.JavaConversions;
+
+import java.io.IOException;
+import java.time.LocalDate;
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.Collections;
+import java.util.Optional;
+
+import static eu.dnetlib.dhp.schema.common.ModelConstants.DNET_PROVENANCE_ACTIONS;
+import static eu.dnetlib.dhp.schema.common.ModelConstants.PROVENANCE_DEDUP;
+import static org.apache.spark.sql.functions.*;
 
 public class SparkCreateMergeRels extends AbstractSparkAction {
 
@@ -68,10 +64,12 @@ public class SparkCreateMergeRels extends AbstractSparkAction {
 		log.info("isLookupUrl {}", isLookUpUrl);
 
 		SparkConf conf = new SparkConf();
+		conf.set("hive.metastore.uris", parser.get("hiveMetastoreUris"));
 		conf.set("spark.serializer", "org.apache.spark.serializer.KryoSerializer");
+
 		conf.registerKryoClasses(ModelSupport.getOafModelClasses());
 
-		new SparkCreateMergeRels(parser, getSparkSession(conf))
+		new SparkCreateMergeRels(parser, getSparkWithHiveSession(conf))
 			.run(ISLookupClientFactory.getLookUpService(isLookUpUrl));
 	}
 
@@ -87,14 +85,15 @@ public class SparkCreateMergeRels extends AbstractSparkAction {
 			.ofNullable(parser.get("cutConnectedComponent"))
 			.map(Integer::valueOf)
 			.orElse(0);
+
+		final String pivotHistoryDatabase = parser.get("pivotHistoryDatabase");
+
 		log.info("connected component cut: '{}'", cut);
 		log.info("graphBasePath: '{}'", graphBasePath);
 		log.info("isLookUpUrl:   '{}'", isLookUpUrl);
 		log.info("actionSetId:   '{}'", actionSetId);
 		log.info("workingPath:   '{}'", workingPath);
 
-		final JavaSparkContext sc = JavaSparkContext.fromSparkContext(spark.sparkContext());
-
 		for (DedupConfig dedupConf : getConfigurations(isLookUpService, actionSetId)) {
 			final String subEntity = dedupConf.getWf().getSubEntityValue();
 			final Class<OafEntity> clazz = ModelSupport.entityTypes.get(EntityType.valueOf(subEntity));
@@ -106,113 +105,170 @@ public class SparkCreateMergeRels extends AbstractSparkAction {
 
 			final String mergeRelPath = DedupUtility.createMergeRelPath(workingPath, actionSetId, subEntity);
 
-			// <hash(id), id>
-			JavaPairRDD<Object, String> vertexes = createVertexes(sc, graphBasePath, subEntity, dedupConf);
-
-			final RDD<Edge<String>> edgeRdd = spark
+			final Dataset<Row> simRels = spark
 				.read()
 				.load(DedupUtility.createSimRelPath(workingPath, actionSetId, subEntity))
-				.as(Encoders.bean(Relation.class))
-				.javaRDD()
-				.map(it -> new Edge<>(hash(it.getSource()), hash(it.getTarget()), it.getRelClass()))
-				.rdd();
+				.select("source", "target");
 
-			Dataset<Tuple2<String, String>> rawMergeRels = spark
-				.createDataset(
-					GraphProcessor
-						.findCCs(vertexes.rdd(), edgeRdd, maxIterations, cut)
-						.toJavaRDD()
-						.filter(k -> k.getIds().size() > 1)
-						.flatMap(this::ccToRels)
-						.rdd(),
-					Encoders.tuple(Encoders.STRING(), Encoders.STRING()));
+			UserDefinedFunction hashUDF = functions
+				.udf(
+					(String s) -> hash(s), DataTypes.LongType);
 
-			Dataset<Tuple2<String, OafEntity>> entities = spark
+			// <hash(id), id>
+			Dataset<Row> vertexIdMap = simRels
+				.selectExpr("source as id")
+				.union(simRels.selectExpr("target as id"))
+				.distinct()
+				.withColumn("vertexId", hashUDF.apply(functions.col("id")));
+
+			final Dataset<Row> edges = spark
 				.read()
-				.textFile(DedupUtility.createEntityPath(graphBasePath, subEntity))
-				.map(
-					(MapFunction<String, Tuple2<String, OafEntity>>) it -> {
-						OafEntity entity = OBJECT_MAPPER.readValue(it, clazz);
-						return new Tuple2<>(entity.getId(), entity);
-					},
-					Encoders.tuple(Encoders.STRING(), Encoders.kryo(clazz)));
+				.load(DedupUtility.createSimRelPath(workingPath, actionSetId, subEntity))
+				.select("source", "target")
+				.withColumn("source", hashUDF.apply(functions.col("source")))
+				.withColumn("target", hashUDF.apply(functions.col("target")));
 
-			Dataset<Relation> mergeRels = rawMergeRels
-				.joinWith(entities, rawMergeRels.col("_2").equalTo(entities.col("_1")), "inner")
-				// <tmp_source,target>,<target,entity>
-				.map(
-					(MapFunction<Tuple2<Tuple2<String, String>, Tuple2<String, OafEntity>>, Tuple2<String, OafEntity>>) value -> new Tuple2<>(
-						value._1()._1(), value._2()._2()),
-					Encoders.tuple(Encoders.STRING(), Encoders.kryo(clazz)))
-				// <tmp_source,entity>
-				.groupByKey(
-					(MapFunction<Tuple2<String, OafEntity>, String>) Tuple2::_1, Encoders.STRING())
-				.mapGroups(
-					(MapGroupsFunction<String, Tuple2<String, OafEntity>, ConnectedComponent>) this::generateID,
-					Encoders.bean(ConnectedComponent.class))
-				// <root_id, list(target)>
+			Dataset<Row> cliques = ConnectedComponent
+				.runOnPairs(edges, 50, spark);
+
+			Dataset<Row> rawMergeRels = cliques
+				.join(vertexIdMap, JavaConversions.asScalaBuffer(Collections.singletonList("vertexId")), "inner")
+				.drop("vertexId")
+				.distinct();
+
+			Dataset<Row> pivotHistory = spark
+				.createDataset(
+					Collections.emptyList(),
+					RowEncoder
+						.apply(StructType.fromDDL("id STRING, firstUsage STRING, lastUsage STRING, dedupId STRING")));
+
+			if (StringUtils.isNotBlank(pivotHistoryDatabase)) {
+				pivotHistory = spark
+					.read()
+					.table(pivotHistoryDatabase + "." + subEntity)
+					.selectExpr("id", "lastUsage", "dedupId");
+			}
+
+			String collectedfromExpr = "false AS collectedfrom";
+			String dateExpr = "'' AS date";
+
+			if (Result.class.isAssignableFrom(clazz)) {
+				if (Publication.class.isAssignableFrom(clazz)) {
+					collectedfromExpr = "array_contains(collectedfrom.key, '" + ModelConstants.CROSSREF_ID
+						+ "') AS collectedfrom";
+				} else if (eu.dnetlib.dhp.schema.oaf.Dataset.class.isAssignableFrom(clazz)) {
+					collectedfromExpr = "array_contains(collectedfrom.key, '" + ModelConstants.DATACITE_ID
+						+ "') AS collectedfrom";
+				}
+
+				dateExpr = "dateofacceptance.value AS date";
+			}
+
+			UserDefinedFunction mapPid = udf(
+				(String s) -> Math.min(PidType.tryValueOf(s).ordinal(), PidType.w3id.ordinal()), DataTypes.IntegerType);
+			UserDefinedFunction validDate = udf((String date) -> {
+				if (StringUtils.isNotBlank(date)
+					&& date.matches(DatePicker.DATE_PATTERN) && DatePicker.inRange(date)) {
+					return date;
+				}
+				return LocalDate.now().plusWeeks(1).toString();
+			}, DataTypes.StringType);
+
+			Dataset<Row> pivotingData = spark
+				.read()
+				.schema(Encoders.bean(clazz).schema())
+				.json(DedupUtility.createEntityPath(graphBasePath, subEntity))
+				.selectExpr(
+					"id",
+					"regexp_extract(id, '^\\\\d+\\\\|([^_]+).*::', 1) AS pidType",
+					collectedfromExpr,
+					dateExpr)
+				.withColumn("pidType", mapPid.apply(col("pidType"))) // ordinal of pid type
+				.withColumn("date", validDate.apply(col("date")));
+
+			UserDefinedFunction generateDedupId = udf((String s) -> IdGenerator.generate(s), DataTypes.StringType);
+
+			// ordering to selected pivot id
+			WindowSpec w = Window
+				.partitionBy("groupId")
+				.orderBy(
+					col("lastUsage").desc_nulls_last(),
+					col("pidType").asc_nulls_last(),
+					col("collectedfrom").desc_nulls_last(),
+					col("date").asc_nulls_last(),
+					col("id").asc_nulls_last());
+
+			Dataset<Relation> output = rawMergeRels
+				.join(pivotHistory, JavaConversions.asScalaBuffer(Collections.singletonList("id")), "full")
+				.join(pivotingData, JavaConversions.asScalaBuffer(Collections.singletonList("id")), "left")
+				.withColumn("pivot", functions.first("id").over(w))
+				.withColumn("pivotDedupId", functions.first("dedupId").over(w))
+				.withColumn("position", functions.row_number().over(w))
+				.filter(cut > 0 ? col("position").lt(lit(cut)) : lit(true))
+				// .select("id", "groupId", "collectedfrom", "pivot", "dedupId", "pivotDedupId")
+				// .distinct()
 				.flatMap(
-					(FlatMapFunction<ConnectedComponent, Relation>) cc -> ccToMergeRel(cc, dedupConf),
-					Encoders.bean(Relation.class));
+					(FlatMapFunction<Row, Tuple3<String, String, String>>) (Row r) -> {
+						String id = r.getAs("id");
+						String pivot = r.getAs("pivot");
+						String pivotDedupId = r.getAs("pivotDedupId"); // dedupId associated with the pivot
+						String dedupId = r.getAs("dedupId"); // dedupId associated with this id if it was a pivot
 
-			saveParquet(mergeRels, mergeRelPath, SaveMode.Overwrite);
+						// filter out id == pivotDedupId
+						// those are caused by claim expressed on pivotDedupId
+						// information will be merged after creating deduprecord
+						if (id.equals(pivotDedupId)) {
+							return Collections.emptyIterator();
+						}
 
+						ArrayList<Tuple3<String, String, String>> res = new ArrayList<>();
+
+						// singleton pivots have null groupId as they do not match rawMergeRels
+						if (r.isNullAt(r.fieldIndex("groupId"))) {
+							// the record is existing if it matches pivotingData
+							if (!r.isNullAt(r.fieldIndex("collectedfrom"))) {
+								// create relation with old dedup id
+								res.add(new Tuple3<>(id, dedupId, null));
+							}
+							return res.iterator();
+						}
+
+						// new pivot, assign pivotDedupId with current IdGenerator
+						if (StringUtils.isBlank(pivotDedupId)) {
+							pivotDedupId = IdGenerator.generate(pivot);
+						}
+
+						// this was a pivot in a preceding graph but it has been merged into a new group with different
+						// pivot
+						if (StringUtils.isNotBlank(dedupId) && !pivot.equals(id) && !dedupId.equals(pivotDedupId)) {
+							// materialize the previous dedup record as a merge relation with the new one
+							res.add(new Tuple3<>(dedupId, pivotDedupId, null));
+						}
+
+						// add merge relations
+						res.add(new Tuple3<>(id, pivotDedupId, pivot));
+
+						return res.iterator();
+					}, Encoders.tuple(Encoders.STRING(), Encoders.STRING(), Encoders.STRING()))
+				.distinct()
+				.flatMap(
+					(FlatMapFunction<Tuple3<String, String, String>, Relation>) (Tuple3<String, String, String> r) -> {
+						String id = r._1();
+						String dedupId = r._2();
+						String pivot = r._3();
+
+						ArrayList<Relation> res = new ArrayList<>();
+						res.add(rel(pivot, dedupId, id, ModelConstants.MERGES, dedupConf));
+						res.add(rel(pivot, id, dedupId, ModelConstants.IS_MERGED_IN, dedupConf));
+
+						return res.iterator();
+					}, Encoders.bean(Relation.class));
+
+			saveParquet(output, mergeRelPath, SaveMode.Overwrite);
 		}
 	}
 
-	private <T extends OafEntity> ConnectedComponent generateID(String key, Iterator<Tuple2<String, T>> values) {
-
-		List<Identifier<T>> identifiers = Lists
-			.newArrayList(values)
-			.stream()
-			.map(v -> Identifier.newInstance(v._2()))
-			.collect(Collectors.toList());
-
-		String rootID = IdGenerator.generate(identifiers, key);
-
-		if (Objects.equals(rootID, key))
-			throw new IllegalStateException("generated default ID: " + rootID);
-
-		return new ConnectedComponent(rootID,
-			identifiers.stream().map(i -> i.getEntity().getId()).collect(Collectors.toSet()));
-	}
-
-	private JavaPairRDD<Object, String> createVertexes(JavaSparkContext sc, String graphBasePath, String subEntity,
-		DedupConfig dedupConf) {
-
-		return sc
-			.textFile(DedupUtility.createEntityPath(graphBasePath, subEntity))
-			.mapToPair(json -> {
-				String id = MapDocumentUtil.getJPathString(dedupConf.getWf().getIdPath(), json);
-				return new Tuple2<>(hash(id), id);
-			});
-	}
-
-	private Iterator<Tuple2<String, String>> ccToRels(ConnectedComponent cc) {
-		return cc
-			.getIds()
-			.stream()
-			.map(id -> new Tuple2<>(cc.getCcId(), id))
-			.iterator();
-	}
-
-	private Iterator<Relation> ccToMergeRel(ConnectedComponent cc, DedupConfig dedupConf) {
-		return cc
-			.getIds()
-			.stream()
-			.flatMap(
-				id -> {
-					List<Relation> tmp = new ArrayList<>();
-
-					tmp.add(rel(cc.getCcId(), id, ModelConstants.MERGES, dedupConf));
-					tmp.add(rel(id, cc.getCcId(), ModelConstants.IS_MERGED_IN, dedupConf));
-
-					return tmp.stream();
-				})
-			.iterator();
-	}
-
-	private Relation rel(String source, String target, String relClass, DedupConfig dedupConf) {
+	private static Relation rel(String pivot, String source, String target, String relClass, DedupConfig dedupConf) {
 
 		String entityType = dedupConf.getWf().getEntityType();
 
@@ -238,6 +294,14 @@ public class SparkCreateMergeRels extends AbstractSparkAction {
 		// TODO calculate the trust value based on the similarity score of the elements in the CC
 
 		r.setDataInfo(info);
+
+		if (pivot != null) {
+			KeyValue pivotKV = new KeyValue();
+			pivotKV.setKey("pivot");
+			pivotKV.setValue(pivot);
+
+			r.setProperties(Arrays.asList(pivotKV));
+		}
 		return r;
 	}
 

dhp-workflows/dhp-dedup-openaire/src/main/java/eu/dnetlib/dhp/oa/dedup/SparkWhitelistSimRels.java

@@ -91,18 +91,12 @@ public class SparkWhitelistSimRels extends AbstractSparkAction {
 			Dataset<Row> entities = spark
 				.read()
 				.textFile(DedupUtility.createEntityPath(graphBasePath, subEntity))
-				.repartition(numPartitions)
-				.withColumn("id", functions.get_json_object(new Column("value"), dedupConf.getWf().getIdPath()));
+				.select(functions.get_json_object(new Column("value"), dedupConf.getWf().getIdPath()).as("id"))
+				.distinct();
 
-			Dataset<Row> whiteListRels1 = whiteListRels
-				.join(entities, entities.col("id").equalTo(whiteListRels.col("from")), "inner")
-				.select("from", "to");
-
-			Dataset<Row> whiteListRels2 = whiteListRels1
-				.join(entities, whiteListRels1.col("to").equalTo(entities.col("id")), "inner")
-				.select("from", "to");
-
-			Dataset<Relation> whiteListSimRels = whiteListRels2
+			Dataset<Relation> whiteListSimRels = whiteListRels
+				.join(entities, entities.col("id").equalTo(whiteListRels.col("from")), "leftsemi")
+				.join(entities, functions.col("to").equalTo(entities.col("id")), "leftsemi")
 				.map(
 					(MapFunction<Row, Relation>) r -> DedupUtility
 						.createSimRel(r.getString(0), r.getString(1), entity),

dhp-workflows/dhp-dedup-openaire/src/main/java/eu/dnetlib/dhp/oa/dedup/graph/ConnectedComponent.java

@@ -1,100 +0,0 @@
-
-package eu.dnetlib.dhp.oa.dedup.graph;
-
-import java.io.IOException;
-import java.io.Serializable;
-import java.util.Set;
-import java.util.stream.Collectors;
-
-import org.apache.commons.lang3.StringUtils;
-import org.codehaus.jackson.annotate.JsonIgnore;
-
-import com.fasterxml.jackson.databind.ObjectMapper;
-
-import eu.dnetlib.dhp.utils.DHPUtils;
-import eu.dnetlib.pace.util.PaceException;
-
-public class ConnectedComponent implements Serializable {
-
-	private String ccId;
-	private Set<String> ids;
-
-	private static final String CONNECTED_COMPONENT_ID_PREFIX = "connect_comp";
-
-	public ConnectedComponent(Set<String> ids, final int cut) {
-		this.ids = ids;
-
-		this.ccId = createDefaultID();
-
-		if (cut > 0 && ids.size() > cut) {
-			this.ids = ids
-				.stream()
-				.filter(id -> !ccId.equalsIgnoreCase(id))
-				.limit(cut - 1)
-				.collect(Collectors.toSet());
-//			this.ids.add(ccId); ??
-		}
-	}
-
-	public ConnectedComponent(String ccId, Set<String> ids) {
-		this.ccId = ccId;
-		this.ids = ids;
-	}
-
-	public String createDefaultID() {
-		if (ids.size() > 1) {
-			final String s = getMin();
-			String prefix = s.split("\\|")[0];
-			ccId = prefix + "|" + CONNECTED_COMPONENT_ID_PREFIX + "::" + DHPUtils.md5(s);
-			return ccId;
-		} else {
-			return ids.iterator().next();
-		}
-	}
-
-	@JsonIgnore
-	public String getMin() {
-
-		final StringBuilder min = new StringBuilder();
-
-		ids
-			.forEach(
-				id -> {
-					if (StringUtils.isBlank(min.toString())) {
-						min.append(id);
-					} else {
-						if (min.toString().compareTo(id) > 0) {
-							min.setLength(0);
-							min.append(id);
-						}
-					}
-				});
-		return min.toString();
-	}
-
-	@Override
-	public String toString() {
-		ObjectMapper mapper = new ObjectMapper();
-		try {
-			return mapper.writeValueAsString(this);
-		} catch (IOException e) {
-			throw new PaceException("Failed to create Json: ", e);
-		}
-	}
-
-	public Set<String> getIds() {
-		return ids;
-	}
-
-	public void setIds(Set<String> ids) {
-		this.ids = ids;
-	}
-
-	public String getCcId() {
-		return ccId;
-	}
-
-	public void setCcId(String ccId) {
-		this.ccId = ccId;
-	}
-}

dhp-workflows/dhp-dedup-openaire/src/main/java/eu/dnetlib/dhp/oa/dedup/graph/GraphProcessor.scala

@@ -1,37 +0,0 @@
-package eu.dnetlib.dhp.oa.dedup.graph
-
-import org.apache.spark.graphx._
-import org.apache.spark.rdd.RDD
-
-import scala.collection.JavaConversions;
-
-object GraphProcessor {
-
-  def findCCs(vertexes: RDD[(VertexId, String)], edges: RDD[Edge[String]], maxIterations: Int, cut:Int): RDD[ConnectedComponent] = {
-    val graph: Graph[String, String] = Graph(vertexes, edges).partitionBy(PartitionStrategy.RandomVertexCut) //TODO remember to remove partitionby
-    val cc = graph.connectedComponents(maxIterations).vertices
-
-    val joinResult = vertexes.leftOuterJoin(cc).map {
-      case (id, (openaireId, cc)) => {
-        if (cc.isEmpty) {
-          (id, openaireId)
-        }
-        else {
-          (cc.get, openaireId)
-        }
-      }
-    }
-    val connectedComponents = joinResult.groupByKey()
-      .map[ConnectedComponent](cc => asConnectedComponent(cc, cut))
-    connectedComponents
-  }
-
-
-
-  def asConnectedComponent(group: (VertexId, Iterable[String]), cut:Int): ConnectedComponent = {
-    val docs = group._2.toSet[String]
-    val connectedComponent = new ConnectedComponent(JavaConversions.setAsJavaSet[String](docs), cut);
-    connectedComponent
-  }
-
-}

dhp-workflows/dhp-dedup-openaire/src/main/java/eu/dnetlib/dhp/oa/dedup/model/Identifier.java

@@ -3,21 +3,21 @@ package eu.dnetlib.dhp.oa.dedup.model;
 
 import java.io.Serializable;
 import java.text.SimpleDateFormat;
-import java.util.*;
-import java.util.stream.Collectors;
+import java.time.LocalDate;
+import java.util.Date;
+import java.util.List;
+import java.util.Objects;
 
 import org.apache.commons.lang3.StringUtils;
 
-import com.google.common.collect.Sets;
-
 import eu.dnetlib.dhp.oa.dedup.DatePicker;
 import eu.dnetlib.dhp.oa.dedup.IdentifierComparator;
 import eu.dnetlib.dhp.schema.common.EntityType;
-import eu.dnetlib.dhp.schema.common.ModelConstants;
 import eu.dnetlib.dhp.schema.common.ModelSupport;
-import eu.dnetlib.dhp.schema.oaf.*;
-import eu.dnetlib.dhp.schema.oaf.utils.OafMapperUtils;
-import eu.dnetlib.dhp.schema.oaf.utils.PidComparator;
+import eu.dnetlib.dhp.schema.oaf.Field;
+import eu.dnetlib.dhp.schema.oaf.KeyValue;
+import eu.dnetlib.dhp.schema.oaf.OafEntity;
+import eu.dnetlib.dhp.schema.oaf.Result;
 import eu.dnetlib.dhp.schema.oaf.utils.PidType;
 
 public class Identifier<T extends OafEntity> implements Serializable, Comparable<Identifier<T>> {
@@ -50,7 +50,7 @@ public class Identifier<T extends OafEntity> implements Serializable, Comparable
 		if (Objects.nonNull(date)) {
 			return date;
 		} else {
-			String sDate = BASE_DATE;
+			String sDate = LocalDate.now().plusDays(1).toString();
 			if (ModelSupport.isSubClass(getEntity(), Result.class)) {
 				Result result = (Result) getEntity();
 				if (isWellformed(result.getDateofacceptance())) {

dhp-workflows/dhp-dedup-openaire/src/main/resources/eu/dnetlib/dhp/oa/dedup/createCC_parameters.json

@@ -28,5 +28,17 @@
     "paramLongName": "workingPath",
     "paramDescription": "path for the working directory",
     "paramRequired": true
+  },
+  {
+    "paramName":"h",
+    "paramLongName":"hiveMetastoreUris",
+    "paramDescription": "the hive metastore uris",
+    "paramRequired": true
+  },
+  {
+    "paramName": "p",
+    "paramLongName": "pivotHistoryDatabase",
+    "paramDescription": "Pivot history database",
+    "paramRequired": false
   }
 ]

dhp-workflows/dhp-dedup-openaire/src/main/resources/eu/dnetlib/dhp/oa/dedup/scan/oozie_app/config-default.xml

@@ -15,4 +15,8 @@
         <name>oozie.action.sharelib.for.spark</name>
         <value>spark2</value>
     </property>
+    <property>
+        <name>hiveMetastoreUris</name>
+        <value>thrift://iis-cdh5-test-m3.ocean.icm.edu.pl:9083</value>
+    </property>
 </configuration>

dhp-workflows/dhp-dedup-openaire/src/main/resources/eu/dnetlib/dhp/oa/dedup/scan/oozie_app/workflow.xml

@@ -188,6 +188,8 @@
             <arg>--isLookUpUrl</arg><arg>${isLookUpUrl}</arg>
             <arg>--actionSetId</arg><arg>${actionSetId}</arg>
             <arg>--cutConnectedComponent</arg><arg>${cutConnectedComponent}</arg>
+            <arg>--hiveMetastoreUris</arg><arg>${hiveMetastoreUris}</arg>
+            <arg>--pivotHistoryDatabase</arg><arg>${pivotHistoryDatabase}</arg>
         </spark>
         <ok to="CreateDedupRecord"/>
         <error to="Kill"/>

dhp-workflows/dhp-dedup-openaire/src/main/scala/com/kwartile/lib/cc/ConnectedComponent.scala

@@ -0,0 +1,335 @@
+/** Copyright (c) 2017 Kwartile, Inc., http://www.kwartile.com
+  * Permission is hereby granted, free of charge, to any person obtaining a copy
+  * of this software and associated documentation files (the "Software"), to deal
+  * in the Software without restriction, including without limitation the rights
+  * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+  * copies of the Software, and to permit persons to whom the Software is
+  * furnished to do so, subject to the following conditions:
+  *
+  * The above copyright notice and this permission notice shall be included in all
+  * copies or substantial portions of the Software.
+  *
+  * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+  * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+  * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+  * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+  * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+  * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+  * SOFTWARE.
+  */
+
+/** Map-reduce implementation of Connected Component
+  * Given lists of subgraphs, returns all the nodes that are connected.
+  */
+
+package com.kwartile.lib.cc
+
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.{Dataset, Row, SparkSession}
+import org.apache.spark.storage.StorageLevel
+
+import scala.annotation.tailrec
+import scala.collection.mutable
+
+object ConnectedComponent extends Serializable {
+
+  /** Applies Small Star operation on RDD of nodePairs
+    *
+    * @param nodePairs on which to apply Small Star operations
+    * @return new nodePairs after the operation and conncectivy change count
+    */
+  private def smallStar(nodePairs: RDD[(Long, Long)]): (RDD[(Long, Long)], Long) = {
+
+    /** generate RDD of (self, List(neighbors)) where self > neighbors
+      * E.g.: nodePairs (1, 4), (6, 1), (3, 2), (6, 5)
+      * will result into (4, List(1)), (6, List(1)), (3, List(2)), (6, List(5))
+      */
+    val neighbors = nodePairs.map(x => {
+      val (self, neighbor) = (x._1, x._2)
+      if (self > neighbor)
+        (self, neighbor)
+      else
+        (neighbor, self)
+    })
+
+    /** reduce on self to get list of all its neighbors.
+      * E.g: (4, List(1)), (6, List(1)), (3, List(2)), (6, List(5))
+      * will result into (4, List(1)), (6, List(1, 5)), (3, List(2))
+      * Note:
+      * (1) you may need to tweak number of partitions.
+      * (2) also, watch out for data skew. In that case, consider using rangePartitioner
+      */
+    val empty = mutable.HashSet[Long]()
+    val allNeighbors = neighbors.aggregateByKey(empty)(
+      (lb, v) => lb += v,
+      (lb1, lb2) => lb1 ++ lb2
+    )
+
+    /** Apply Small Star operation on (self, List(neighbor)) to get newNodePairs and count the change in connectivity
+      */
+
+    val newNodePairsWithChangeCount = allNeighbors
+      .map(x => {
+        val self = x._1
+        val neighbors = x._2.toList
+        val minNode = argMin(self :: neighbors)
+        val newNodePairs = (self :: neighbors)
+          .map(neighbor => {
+            (neighbor, minNode)
+          })
+          .filter(x => {
+            val neighbor = x._1
+            val minNode = x._2
+            (neighbor <= self && neighbor != minNode) || (self == neighbor)
+          })
+        val uniqueNewNodePairs = newNodePairs.toSet.toList
+
+        /** We count the change by taking a diff of the new node pairs with the old node pairs
+          */
+        val connectivityChangeCount = (uniqueNewNodePairs diff neighbors.map((self, _))).length
+        (uniqueNewNodePairs, connectivityChangeCount)
+      })
+      .persist(StorageLevel.MEMORY_AND_DISK_SER)
+
+    /** Sum all the changeCounts
+      */
+    val totalConnectivityCountChange = newNodePairsWithChangeCount
+      .mapPartitions(iter => {
+        val (v, l) = iter.toSeq.unzip
+        val sum = l.sum
+        Iterator(sum)
+      })
+      .sum
+      .toLong
+
+    val newNodePairs = newNodePairsWithChangeCount.map(x => x._1).flatMap(x => x)
+    newNodePairsWithChangeCount.unpersist(false)
+    (newNodePairs, totalConnectivityCountChange)
+  }
+
+  /** Apply Large Star operation on a RDD of nodePairs
+    *
+    * @param nodePairs on which to apply Large Star operations
+    * @return new nodePairs after the operation and conncectivy change count
+    */
+  private def largeStar(nodePairs: RDD[(Long, Long)]): (RDD[(Long, Long)], Long) = {
+
+    /** generate RDD of (self, List(neighbors))
+      * E.g.: nodePairs (1, 4), (6, 1), (3, 2), (6, 5)
+      * will result into (4, List(1)), (1, List(4)), (6, List(1)), (1, List(6)), (3, List(2)), (2, List(3)), (6, List(5)), (5, List(6))
+      */
+
+    val neighbors = nodePairs.flatMap(x => {
+      val (self, neighbor) = (x._1, x._2)
+      if (self == neighbor)
+        List((self, neighbor))
+      else
+        List((self, neighbor), (neighbor, self))
+    })
+
+    /** reduce on self to get list of all its neighbors.
+      * E.g: (4, List(1)), (1, List(4)), (6, List(1)), (1, List(6)), (3, List(2)), (2, List(3)), (6, List(5)), (5, List(6))
+      * will result into (4, List(1)), (1, List(4, 6)), (6, List(1, 5)), (3, List(2)), (2, List(3)), (5, List(6))
+      * Note:
+      * (1) you may need to tweak number of partitions.
+      * (2) also, watch out for data skew. In that case, consider using rangePartitioner
+      */
+
+    val localAdd = (s: mutable.HashSet[Long], v: Long) => s += v
+    val partitionAdd = (s1: mutable.HashSet[Long], s2: mutable.HashSet[Long]) => s1 ++= s2
+    val allNeighbors =
+      neighbors.aggregateByKey(mutable.HashSet.empty[Long] /*, rangePartitioner*/ )(localAdd, partitionAdd)
+
+    /** Apply Large Star operation on (self, List(neighbor)) to get newNodePairs and count the change in connectivity
+      */
+
+    val newNodePairsWithChangeCount = allNeighbors
+      .map(x => {
+        val self = x._1
+        val neighbors = x._2.toList
+        val minNode = argMin(self :: neighbors)
+        val newNodePairs = (self :: neighbors)
+          .map(neighbor => {
+            (neighbor, minNode)
+          })
+          .filter(x => {
+            val neighbor = x._1
+            val minNode = x._2
+            neighbor > self || neighbor == minNode
+          })
+
+        val uniqueNewNodePairs = newNodePairs.toSet.toList
+        val connectivityChangeCount = (uniqueNewNodePairs diff neighbors.map((self, _))).length
+        (uniqueNewNodePairs, connectivityChangeCount)
+      })
+      .persist(StorageLevel.MEMORY_AND_DISK_SER)
+
+    val totalConnectivityCountChange = newNodePairsWithChangeCount
+      .mapPartitions(iter => {
+        val (v, l) = iter.toSeq.unzip
+        val sum = l.sum
+        Iterator(sum)
+      })
+      .sum
+      .toLong
+
+    /** Sum all the changeCounts
+      */
+    val newNodePairs = newNodePairsWithChangeCount.map(x => x._1).flatMap(x => x)
+    newNodePairsWithChangeCount.unpersist(false)
+    (newNodePairs, totalConnectivityCountChange)
+  }
+
+  private def argMin(nodes: List[Long]): Long = {
+    nodes.min(Ordering.by((node: Long) => node))
+  }
+
+  /** Build nodePairs given a list of nodes.  A list of nodes represents a subgraph.
+    *
+    * @param nodes that are part of a subgraph
+    * @return nodePairs for a subgraph
+    */
+  private def buildPairs(nodes: List[Long]): List[(Long, Long)] = {
+    buildPairs(nodes.head, nodes.tail, null.asInstanceOf[List[(Long, Long)]])
+  }
+
+  @tailrec
+  private def buildPairs(node: Long, neighbors: List[Long], partialPairs: List[(Long, Long)]): List[(Long, Long)] = {
+    if (neighbors.isEmpty) {
+      if (partialPairs != null)
+        List((node, node)) ::: partialPairs
+      else
+        List((node, node))
+    } else if (neighbors.length == 1) {
+      val neighbor = neighbors(0)
+      if (node > neighbor)
+        if (partialPairs != null) List((node, neighbor)) ::: partialPairs else List((node, neighbor))
+      else if (partialPairs != null) List((neighbor, node)) ::: partialPairs
+      else List((neighbor, node))
+    } else {
+      val newPartialPairs = neighbors
+        .map(neighbor => {
+          if (node > neighbor)
+            List((node, neighbor))
+          else
+            List((neighbor, node))
+        })
+        .flatMap(x => x)
+
+      if (partialPairs != null)
+        buildPairs(neighbors.head, neighbors.tail, newPartialPairs ::: partialPairs)
+      else
+        buildPairs(neighbors.head, neighbors.tail, newPartialPairs)
+    }
+  }
+
+  /** Implements alternatingAlgo.  Converges when the changeCount is either 0 or does not change from the previous iteration
+    *
+    * @param nodePairs                        for a graph
+    * @param largeStarConnectivityChangeCount change count that resulted from the previous iteration
+    * @param smallStarConnectivityChangeCount change count that resulted from the previous iteration
+    * @param didConverge                      flag to indicate the alorigth converged
+    * @param currIterationCount               counter to capture number of iterations
+    * @param maxIterationCount                maximum number iterations to try before giving up
+    * @return RDD of nodePairs
+    */
+
+  @tailrec
+  private def alternatingAlgo(
+    nodePairs: RDD[(Long, Long)],
+    largeStarConnectivityChangeCount: Long,
+    smallStarConnectivityChangeCount: Long,
+    didConverge: Boolean,
+    currIterationCount: Int,
+    maxIterationCount: Int
+  ): (RDD[(Long, Long)], Boolean, Long) = {
+
+    val iterationCount = currIterationCount + 1
+    if (didConverge)
+      (nodePairs, true, currIterationCount)
+    else if (currIterationCount >= maxIterationCount) {
+      (nodePairs, false, currIterationCount)
+    } else {
+
+      val (nodePairsLargeStar, currLargeStarConnectivityChangeCount) = largeStar(nodePairs)
+      val (nodePairsSmallStar, currSmallStarConnectivityChangeCount) = smallStar(nodePairsLargeStar)
+
+      if (
+        (currLargeStarConnectivityChangeCount == largeStarConnectivityChangeCount &&
+        currSmallStarConnectivityChangeCount == smallStarConnectivityChangeCount) ||
+        (currSmallStarConnectivityChangeCount == 0 && currLargeStarConnectivityChangeCount == 0)
+      ) {
+        alternatingAlgo(
+          nodePairsSmallStar,
+          currLargeStarConnectivityChangeCount,
+          currSmallStarConnectivityChangeCount,
+          true,
+          iterationCount,
+          maxIterationCount
+        )
+      } else {
+        alternatingAlgo(
+          nodePairsSmallStar,
+          currLargeStarConnectivityChangeCount,
+          currSmallStarConnectivityChangeCount,
+          false,
+          iterationCount,
+          maxIterationCount
+        )
+      }
+    }
+  }
+
+  /** Driver function
+    *
+    * @param cliques           list of nodes representing subgraphs (or cliques)
+    * @param maxIterationCount maximum number iterations to try before giving up
+    * @return Connected Components as nodePairs where second member of the nodePair is the minimum node in the component
+    */
+  def run(cliques: RDD[List[Long]], maxIterationCount: Int): (RDD[(Long, Long)], Boolean, Long) = {
+
+    val nodePairs = cliques
+      .map(aClique => {
+        buildPairs(aClique)
+      })
+      .flatMap(x => x)
+
+    val (cc, didConverge, iterCount) = alternatingAlgo(nodePairs, 9999999L, 9999999L, false, 0, maxIterationCount)
+
+    if (didConverge) {
+      (cc, didConverge, iterCount)
+    } else {
+      (null.asInstanceOf[RDD[(Long, Long)]], didConverge, iterCount)
+    }
+  }
+
+  def runOnPairs(nodePairs: RDD[(Long, Long)], maxIterationCount: Int): (RDD[(Long, Long)], Boolean, Long) = {
+    val (cc, didConverge, iterCount) = alternatingAlgo(nodePairs, 9999999L, 9999999L, false, 0, maxIterationCount)
+
+    if (didConverge) {
+      (cc, didConverge, iterCount)
+    } else {
+      (null.asInstanceOf[RDD[(Long, Long)]], didConverge, iterCount)
+    }
+  }
+
+  def runOnPairs(nodePairs: Dataset[Row], maxIterationCount: Int)(implicit spark: SparkSession): Dataset[Row] = {
+    import spark.implicits._
+
+    val (cc, didConverge, iterCount) = alternatingAlgo(
+      nodePairs.map(e => (e.getLong(0), e.getLong(1))).rdd,
+      9999999L,
+      9999999L,
+      false,
+      0,
+      maxIterationCount
+    )
+
+    if (didConverge) {
+      cc.toDF("vertexId", "groupId")
+    } else {
+      null.asInstanceOf[Dataset[Row]]
+    }
+  }
+
+}

dhp-workflows/dhp-dedup-openaire/src/test/java/eu/dnetlib/dhp/oa/dedup/SparkDedupTest.java

@@ -41,9 +41,13 @@ import com.google.common.collect.Sets;
 
 import eu.dnetlib.dhp.application.ArgumentApplicationParser;
 import eu.dnetlib.dhp.schema.common.ModelConstants;
+import eu.dnetlib.dhp.schema.common.ModelSupport;
 import eu.dnetlib.dhp.schema.oaf.*;
+import eu.dnetlib.dhp.schema.oaf.utils.OafMapperUtils;
+import eu.dnetlib.dhp.schema.sx.OafUtils;
 import eu.dnetlib.enabling.is.lookup.rmi.ISLookUpException;
 import eu.dnetlib.enabling.is.lookup.rmi.ISLookUpService;
+import scala.Tuple2;
 
 @ExtendWith(MockitoExtension.class)
 @TestMethodOrder(MethodOrderer.OrderAnnotation.class)
@@ -97,6 +101,7 @@ public class SparkDedupTest implements Serializable {
 
 		final SparkConf conf = new SparkConf();
 		conf.set("spark.sql.shuffle.partitions", "200");
+		conf.set("spark.sql.warehouse.dir", testOutputBasePath + "/spark-warehouse");
 		spark = SparkSession
 			.builder()
 			.appName(SparkDedupTest.class.getSimpleName())
@@ -186,11 +191,11 @@ public class SparkDedupTest implements Serializable {
 		System.out.println("ds_simrel = " + ds_simrel);
 		System.out.println("orp_simrel = " + orp_simrel);
 
-		assertEquals(1538, orgs_simrel);
-		assertEquals(3523, pubs_simrel);
-		assertEquals(168, sw_simrel);
-		assertEquals(221, ds_simrel);
-		assertEquals(3392, orp_simrel);
+		assertEquals(751, orgs_simrel);
+		assertEquals(546, pubs_simrel);
+		assertEquals(113, sw_simrel);
+		assertEquals(148, ds_simrel);
+		assertEquals(280, orp_simrel);
 
 	}
 
@@ -235,10 +240,10 @@ public class SparkDedupTest implements Serializable {
 			.count();
 
 		// entities simrels supposed to be equal to the number of previous step (no rels in whitelist)
-		assertEquals(1538, orgs_simrel);
-		assertEquals(3523, pubs_simrel);
-		assertEquals(221, ds_simrel);
-		assertEquals(3392, orp_simrel);
+		assertEquals(751, orgs_simrel);
+		assertEquals(546, pubs_simrel);
+		assertEquals(148, ds_simrel);
+		assertEquals(280, orp_simrel);
 //		System.out.println("orgs_simrel = " + orgs_simrel);
 //		System.out.println("pubs_simrel = " + pubs_simrel);
 //		System.out.println("ds_simrel = " + ds_simrel);
@@ -268,7 +273,7 @@ public class SparkDedupTest implements Serializable {
 						&& rel.getTarget().equalsIgnoreCase(whiteList.get(1).split(WHITELIST_SEPARATOR)[1]))
 				.count() > 0);
 
-		assertEquals(170, sw_simrel.count());
+		assertEquals(115, sw_simrel.count());
 //		System.out.println("sw_simrel = " + sw_simrel.count());
 
 	}
@@ -292,7 +297,9 @@ public class SparkDedupTest implements Serializable {
 					"-w",
 					testOutputBasePath,
 					"-cc",
-					"3"
+					"3",
+					"-h",
+					""
 				});
 
 		new SparkCreateMergeRels(parser, spark).run(isLookUpService);
@@ -365,6 +372,113 @@ public class SparkDedupTest implements Serializable {
 			.deleteDirectory(new File(testOutputBasePath + "/" + testActionSetId + "/otherresearchproduct_mergerel"));
 	}
 
+	@Test
+	@Order(3)
+	void createMergeRelsWithPivotHistoryTest() throws Exception {
+
+		ArgumentApplicationParser parser = new ArgumentApplicationParser(
+			classPathResourceAsString("/eu/dnetlib/dhp/oa/dedup/createCC_parameters.json"));
+
+		spark.sql("CREATE DATABASE IF NOT EXISTS pivot_history_test");
+		ModelSupport.oafTypes.keySet().forEach(entityType -> {
+			try {
+				spark
+					.read()
+					.json(
+						Paths
+							.get(SparkDedupTest.class.getResource("/eu/dnetlib/dhp/dedup/pivot_history").toURI())
+							.toFile()
+							.getAbsolutePath())
+					.write()
+					.mode("overwrite")
+					.saveAsTable("pivot_history_test." + entityType);
+			} catch (URISyntaxException e) {
+				throw new RuntimeException(e);
+			}
+		});
+
+		parser
+			.parseArgument(
+				new String[] {
+					"-i",
+					testGraphBasePath,
+					"-asi",
+					testActionSetId,
+					"-la",
+					"lookupurl",
+					"-w",
+					testOutputBasePath,
+					"-h",
+					"",
+					"-pivotHistoryDatabase",
+					"pivot_history_test"
+
+				});
+
+		new SparkCreateMergeRels(parser, spark).run(isLookUpService);
+
+		long orgs_mergerel = spark
+			.read()
+			.load(testOutputBasePath + "/" + testActionSetId + "/organization_mergerel")
+			.count();
+		final Dataset<Relation> pubs = spark
+			.read()
+			.load(testOutputBasePath + "/" + testActionSetId + "/publication_mergerel")
+			.as(Encoders.bean(Relation.class));
+		long sw_mergerel = spark
+			.read()
+			.load(testOutputBasePath + "/" + testActionSetId + "/software_mergerel")
+			.count();
+		long ds_mergerel = spark
+			.read()
+			.load(testOutputBasePath + "/" + testActionSetId + "/dataset_mergerel")
+			.count();
+
+		long orp_mergerel = spark
+			.read()
+			.load(testOutputBasePath + "/" + testActionSetId + "/otherresearchproduct_mergerel")
+			.count();
+
+		final List<Relation> merges = pubs
+			.filter("source == '50|arXiv_dedup_::c93aeb433eb90ed7a86e29be00791b7c'")
+			.collectAsList();
+		assertEquals(3, merges.size());
+		Set<String> dups = Sets
+			.newHashSet(
+				"50|doi_________::3b1d0d8e8f930826665df9d6b82fbb73",
+				"50|doi_________::d5021b53204e4fdeab6ff5d5bc468032",
+				"50|arXiv_______::c93aeb433eb90ed7a86e29be00791b7c");
+		merges.forEach(r -> {
+			assertEquals(ModelConstants.RESULT_RESULT, r.getRelType());
+			assertEquals(ModelConstants.DEDUP, r.getSubRelType());
+			assertEquals(ModelConstants.MERGES, r.getRelClass());
+			assertTrue(dups.contains(r.getTarget()));
+		});
+
+		final List<Relation> mergedIn = pubs
+			.filter("target == '50|arXiv_dedup_::c93aeb433eb90ed7a86e29be00791b7c'")
+			.collectAsList();
+		assertEquals(3, mergedIn.size());
+		mergedIn.forEach(r -> {
+			assertEquals(ModelConstants.RESULT_RESULT, r.getRelType());
+			assertEquals(ModelConstants.DEDUP, r.getSubRelType());
+			assertEquals(ModelConstants.IS_MERGED_IN, r.getRelClass());
+			assertTrue(dups.contains(r.getSource()));
+		});
+
+		assertEquals(1268, orgs_mergerel);
+		assertEquals(1112, pubs.count());
+		assertEquals(292, sw_mergerel);
+		assertEquals(476, ds_mergerel);
+		assertEquals(742, orp_mergerel);
+//		System.out.println("orgs_mergerel = " + orgs_mergerel);
+//		System.out.println("pubs_mergerel = " + pubs_mergerel);
+//		System.out.println("sw_mergerel = " + sw_mergerel);
+//		System.out.println("ds_mergerel = " + ds_mergerel);
+//		System.out.println("orp_mergerel = " + orp_mergerel);
+
+	}
+
 	@Test
 	@Order(4)
 	void createMergeRelsTest() throws Exception {
@@ -382,7 +496,9 @@ public class SparkDedupTest implements Serializable {
 					"-la",
 					"lookupurl",
 					"-w",
-					testOutputBasePath
+					testOutputBasePath,
+					"-h",
+					""
 				});
 
 		new SparkCreateMergeRels(parser, spark).run(isLookUpService);
@@ -437,10 +553,10 @@ public class SparkDedupTest implements Serializable {
 		});
 
 		assertEquals(1268, orgs_mergerel);
-		assertEquals(1450, pubs.count());
-		assertEquals(286, sw_mergerel);
-		assertEquals(472, ds_mergerel);
-		assertEquals(738, orp_mergerel);
+		assertEquals(1112, pubs.count());
+		assertEquals(292, sw_mergerel);
+		assertEquals(476, ds_mergerel);
+		assertEquals(742, orp_mergerel);
 //		System.out.println("orgs_mergerel = " + orgs_mergerel);
 //		System.out.println("pubs_mergerel = " + pubs_mergerel);
 //		System.out.println("sw_mergerel = " + sw_mergerel);
@@ -492,10 +608,10 @@ public class SparkDedupTest implements Serializable {
 			.count();
 
 		assertEquals(86, orgs_deduprecord);
-		assertEquals(68, pubs.count());
-		assertEquals(49, sw_deduprecord);
+		assertEquals(91, pubs.count());
+		assertEquals(47, sw_deduprecord);
 		assertEquals(97, ds_deduprecord);
-		assertEquals(92, orp_deduprecord);
+		assertEquals(93, orp_deduprecord);
 
 		verifyRoot_1(mapper, pubs);
 
@@ -629,13 +745,13 @@ public class SparkDedupTest implements Serializable {
 			.distinct()
 			.count();
 
-		assertEquals(902, publications);
+		assertEquals(925, publications);
 		assertEquals(839, organizations);
 		assertEquals(100, projects);
 		assertEquals(100, datasource);
-		assertEquals(198, softwares);
+		assertEquals(196, softwares);
 		assertEquals(389, dataset);
-		assertEquals(520, otherresearchproduct);
+		assertEquals(521, otherresearchproduct);
 
 //		System.out.println("publications = " + publications);
 //		System.out.println("organizations = " + organizations);

dhp-workflows/dhp-dedup-openaire/src/test/resources/eu/dnetlib/dhp/dedup/conf/ds.curr.conf.json

@@ -101,7 +101,8 @@
         "type" : "String",
         "path" : "$.title[?(@.qualifier.classid == 'main title')].value",
         "length" : 250,
-        "size" : 5
+        "size" : 5,
+        "clean": "title"
       },
       {
         "name" : "authors",

dhp-workflows/dhp-dedup-openaire/src/test/resources/eu/dnetlib/dhp/dedup/conf/orp.curr.conf.json

@@ -101,7 +101,8 @@
         "type" : "String",
         "path" : "$.title[?(@.qualifier.classid == 'main title')].value",
         "length" : 250,
-        "size" : 5
+        "size" : 5,
+        "clean": "title"
       },
       {
         "name" : "authors",

dhp-workflows/dhp-dedup-openaire/src/test/resources/eu/dnetlib/dhp/dedup/conf/pub.curr.conf.json

@@ -29,9 +29,8 @@
   },
   "pace": {
     "clustering" : [
-      { "name" : "ngrampairs", "fields" : [ "title" ], "params" : { "max" : "1", "ngramLen" : "3"} },
-      { "name" : "suffixprefix", "fields" : [ "title" ], "params" : { "max" : "1", "len" : "3" } },
-      { "name" : "lowercase", "fields" : [ "doi" ], "params" : { } }
+      { "name" : "numAuthorsTitleSuffixPrefixChain", "fields" : [ "num_authors", "title" ], "params" : { "mod" : "10" } },
+      { "name" : "jsonlistclustering", "fields" : [ "pid" ], "params" : { "jpath_value": "$.value", "jpath_classid": "$.qualifier.classid"} }
     ],
     "decisionTree": {
       "start": {
@@ -79,13 +78,37 @@
         "ignoreUndefined": "false"
       },
       "layer3": {
+        "fields": [
+          {
+            "field": "authors",
+            "comparator": "authorsMatch",
+            "weight": 1.0,
+            "countIfUndefined": "false",
+            "params": {
+              "surname_th": 0.75,
+              "fullname_th": 0.75,
+              "threshold": 0.6,
+              "mode": "full"
+            }
+          }
+        ],
+        "threshold": 0.6,
+        "aggregation": "MAX",
+        "positive": "layer4",
+        "negative": "NO_MATCH",
+        "undefined": "MATCH",
+        "ignoreUndefined": "true"
+      },
+      "layer4": {
         "fields": [
           {
             "field": "title",
             "comparator": "levensteinTitle",
             "weight": 1.0,
             "countIfUndefined": "true",
-            "params": {}
+            "params": {
+              "threshold": "0.99"
+            }
           }
         ],
         "threshold": 0.99,
@@ -97,23 +120,25 @@
       }
     },
     "model": [
-      {
-        "name": "doi",
-        "type": "String",
-        "path": "$.pid[?(@.qualifier.classid == 'doi')].value"
-      },
       {
         "name": "pid",
         "type": "JSON",
         "path": "$.pid",
         "overrideMatch": "true"
       },
+      {
+        "name": "alternateid",
+        "type": "JSON",
+        "path": "$.instance[*].alternateIdentifier[*]",
+        "overrideMatch": "true"
+      },
       {
         "name": "title",
         "type": "String",
         "path": "$.title[?(@.qualifier.classid == 'main title')].value",
         "length": 250,
-        "size": 5
+        "size": 5,
+        "clean": "title"
       },
       {
         "name": "authors",
@@ -122,9 +147,9 @@
         "size": 200
       },
       {
-        "name": "resulttype",
+        "name": "num_authors",
         "type": "String",
-        "path": "$.resulttype.classid"
+        "path": "$.author.length()"
       }
     ],
     "blacklists": {

dhp-workflows/dhp-dedup-openaire/src/test/resources/eu/dnetlib/dhp/dedup/conf/sw.curr.conf.json

@@ -75,7 +75,8 @@
             "type" : "String",
             "path" : "$.title[?(@.qualifier.classid == 'main title')].value",
             "length" : 250,
-            "size" : 5
+            "size" : 5,
+            "clean": "title"
           },
           {
             "name" : "url",

dhp-workflows/dhp-dedup-openaire/src/test/resources/eu/dnetlib/dhp/dedup/pivot_history/pivot_history.json

@@ -0,0 +1 @@
+{"id": "50|arXiv_______::c93aeb433eb90ed7a86e29be00791b7c", "firstUsage": "2022-01-01", "lastUsage": "2022-01-01", "dedupId": "50|arXiv_dedup_::c93aeb433eb90ed7a86e29be00791b7c" }

pom.xml

@@ -931,5 +931,25 @@
 				 -->
 			</properties>
 		</profile>
+
+		<!-- Activate ARM-compatible snappy dependency on new Silicon Macs -->
+		<profile>
+			<id>arm-silicon-mac</id>
+			<activation>
+				<os>
+					<arch>aarch64</arch>
+					<family>mac</family>
+				</os>
+			</activation>
+			<dependencyManagement>
+				<dependencies>
+					<dependency>
+						<groupId>org.xerial.snappy</groupId>
+						<artifactId>snappy-java</artifactId>
+						<version>1.1.8.4</version>
+					</dependency>
+				</dependencies>
+			</dependencyManagement>
+		</profile>
 	</profiles>
 </project>