2020-04-27 14:52:31 +02:00
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2020-04-04 14:03:43 +02:00
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package eu.dnetlib.dhp.oa.provision;
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2020-04-18 12:42:58 +02:00
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import static eu.dnetlib.dhp.common.SparkSessionSupport.runWithSparkSession;
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2020-04-06 15:33:31 +02:00
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import com.fasterxml.jackson.databind.ObjectMapper;
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2020-04-04 14:03:43 +02:00
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import com.google.common.collect.Maps;
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import eu.dnetlib.dhp.application.ArgumentApplicationParser;
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import eu.dnetlib.dhp.common.HdfsSupport;
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import eu.dnetlib.dhp.oa.provision.model.*;
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import eu.dnetlib.dhp.oa.provision.utils.ContextMapper;
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import eu.dnetlib.dhp.oa.provision.utils.XmlRecordFactory;
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import eu.dnetlib.dhp.schema.oaf.*;
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2020-04-18 12:42:58 +02:00
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import java.util.ArrayList;
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import java.util.Map;
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import java.util.Optional;
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import java.util.stream.Collectors;
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2020-04-04 14:03:43 +02:00
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import org.apache.commons.io.IOUtils;
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import org.apache.hadoop.io.Text;
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import org.apache.hadoop.io.compress.GzipCodec;
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import org.apache.hadoop.mapred.SequenceFileOutputFormat;
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import org.apache.spark.SparkConf;
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import org.apache.spark.SparkContext;
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import org.apache.spark.api.java.function.MapFunction;
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import org.apache.spark.api.java.function.PairFunction;
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import org.apache.spark.sql.Encoders;
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import org.apache.spark.sql.SparkSession;
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import org.apache.spark.util.LongAccumulator;
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import org.slf4j.Logger;
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import org.slf4j.LoggerFactory;
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import scala.Tuple2;
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/**
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2020-04-27 14:52:31 +02:00
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* Joins the graph nodes by resolving the links of distance = 1 to create an adjacency list of linked objects. The
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* operation considers all the entity types (publication, dataset, software, ORP, project, datasource, organization, and
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* all the possible relationships (similarity links produced by the Dedup process are excluded).
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* <p>
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* The workflow is organized in different parts aimed to to reduce the complexity of the operation 1)
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* PrepareRelationsJob: only consider relationships that are not virtually deleted ($.dataInfo.deletedbyinference ==
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* false), each entity can be linked at most to 100 other objects
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* <p>
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* 2) JoinRelationEntityByTargetJob: (phase 1): prepare tuples [relation - target entity] (R - T): for each entity type
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* E_i map E_i as RelatedEntity T_i to simplify the model and extracting only the necessary information join (R.target =
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* T_i.id) save the tuples (R_i, T_i) (phase 2): create the union of all the entity types E, hash by id read the tuples
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* (R, T), hash by R.source join E.id = (R, T).source, where E becomes the Source Entity S save the tuples (S, R, T)
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* <p>
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* 3) AdjacencyListBuilderJob: given the tuple (S - R - T) we need to group by S.id -> List [ R - T ], mapping the
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* result as JoinedEntity
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* <p>
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* 4) XmlConverterJob: convert the JoinedEntities as XML records
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2020-04-04 14:03:43 +02:00
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*/
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public class XmlConverterJob {
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2020-04-27 14:52:31 +02:00
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private static final Logger log = LoggerFactory.getLogger(XmlConverterJob.class);
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private static final ObjectMapper OBJECT_MAPPER = new ObjectMapper();
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public static final String schemaLocation = "https://www.openaire.eu/schema/1.0/oaf-1.0.xsd";
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public static void main(String[] args) throws Exception {
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final ArgumentApplicationParser parser = new ArgumentApplicationParser(
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IOUtils
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.toString(
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XmlConverterJob.class
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.getResourceAsStream(
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"/eu/dnetlib/dhp/oa/provision/input_params_xml_converter.json")));
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parser.parseArgument(args);
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Boolean isSparkSessionManaged = Optional
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.ofNullable(parser.get("isSparkSessionManaged"))
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.map(Boolean::valueOf)
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.orElse(Boolean.TRUE);
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log.info("isSparkSessionManaged: {}", isSparkSessionManaged);
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String inputPath = parser.get("inputPath");
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log.info("inputPath: {}", inputPath);
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String outputPath = parser.get("outputPath");
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log.info("outputPath: {}", outputPath);
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String isLookupUrl = parser.get("isLookupUrl");
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log.info("isLookupUrl: {}", isLookupUrl);
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String otherDsTypeId = parser.get("otherDsTypeId");
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log.info("otherDsTypeId: {}", otherDsTypeId);
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SparkConf conf = new SparkConf();
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runWithSparkSession(
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conf,
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isSparkSessionManaged,
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spark -> {
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removeOutputDir(spark, outputPath);
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convertToXml(
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spark, inputPath, outputPath, ContextMapper.fromIS(isLookupUrl), otherDsTypeId);
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});
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}
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private static void convertToXml(
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SparkSession spark,
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String inputPath,
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String outputPath,
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ContextMapper contextMapper,
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String otherDsTypeId) {
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final XmlRecordFactory recordFactory = new XmlRecordFactory(
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prepareAccumulators(spark.sparkContext()),
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contextMapper,
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false,
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schemaLocation,
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otherDsTypeId);
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spark
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.read()
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.load(inputPath)
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.as(Encoders.bean(JoinedEntity.class))
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.map(
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(MapFunction<JoinedEntity, JoinedEntity>) j -> {
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if (j.getLinks() != null) {
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j
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.setLinks(
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j
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.getLinks()
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.stream()
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.filter(t -> t.getRelation() != null & t.getRelatedEntity() != null)
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.collect(Collectors.toCollection(ArrayList::new)));
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}
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return j;
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},
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Encoders.bean(JoinedEntity.class))
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.map(
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(MapFunction<JoinedEntity, Tuple2<String, String>>) je -> new Tuple2<>(je.getEntity().getId(),
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recordFactory.build(je)),
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Encoders.tuple(Encoders.STRING(), Encoders.STRING()))
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.javaRDD()
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.mapToPair(
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(PairFunction<Tuple2<String, String>, Text, Text>) t -> new Tuple2<>(new Text(t._1()),
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new Text(t._2())))
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.saveAsHadoopFile(
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outputPath, Text.class, Text.class, SequenceFileOutputFormat.class, GzipCodec.class);
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}
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private static void removeOutputDir(SparkSession spark, String path) {
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HdfsSupport.remove(path, spark.sparkContext().hadoopConfiguration());
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}
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private static Map<String, LongAccumulator> prepareAccumulators(SparkContext sc) {
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Map<String, LongAccumulator> accumulators = Maps.newHashMap();
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accumulators
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.put(
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"resultResult_similarity_isAmongTopNSimilarDocuments",
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sc.longAccumulator("resultResult_similarity_isAmongTopNSimilarDocuments"));
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accumulators
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.put(
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"resultResult_similarity_hasAmongTopNSimilarDocuments",
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sc.longAccumulator("resultResult_similarity_hasAmongTopNSimilarDocuments"));
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accumulators
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.put(
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"resultResult_supplement_isSupplementTo",
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sc.longAccumulator("resultResult_supplement_isSupplementTo"));
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accumulators
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.put(
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"resultResult_supplement_isSupplementedBy",
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sc.longAccumulator("resultResult_supplement_isSupplementedBy"));
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accumulators
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.put(
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"resultResult_dedup_isMergedIn", sc.longAccumulator("resultResult_dedup_isMergedIn"));
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accumulators.put("resultResult_dedup_merges", sc.longAccumulator("resultResult_dedup_merges"));
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accumulators
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.put(
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"resultResult_publicationDataset_isRelatedTo",
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sc.longAccumulator("resultResult_publicationDataset_isRelatedTo"));
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accumulators
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.put(
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"resultResult_relationship_isRelatedTo",
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sc.longAccumulator("resultResult_relationship_isRelatedTo"));
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accumulators
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.put(
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"resultProject_outcome_isProducedBy",
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sc.longAccumulator("resultProject_outcome_isProducedBy"));
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accumulators
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.put(
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"resultProject_outcome_produces", sc.longAccumulator("resultProject_outcome_produces"));
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accumulators
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.put(
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"resultOrganization_affiliation_isAuthorInstitutionOf",
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sc.longAccumulator("resultOrganization_affiliation_isAuthorInstitutionOf"));
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accumulators
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.put(
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"resultOrganization_affiliation_hasAuthorInstitution",
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sc.longAccumulator("resultOrganization_affiliation_hasAuthorInstitution"));
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accumulators
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.put(
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"projectOrganization_participation_hasParticipant",
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sc.longAccumulator("projectOrganization_participation_hasParticipant"));
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accumulators
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.put(
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"projectOrganization_participation_isParticipant",
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sc.longAccumulator("projectOrganization_participation_isParticipant"));
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accumulators
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.put(
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"organizationOrganization_dedup_isMergedIn",
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sc.longAccumulator("organizationOrganization_dedup_isMergedIn"));
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accumulators
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.put(
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"organizationOrganization_dedup_merges",
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sc.longAccumulator("resultProject_outcome_produces"));
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accumulators
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.put(
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"datasourceOrganization_provision_isProvidedBy",
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sc.longAccumulator("datasourceOrganization_provision_isProvidedBy"));
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accumulators
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.put(
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"datasourceOrganization_provision_provides",
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sc.longAccumulator("datasourceOrganization_provision_provides"));
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return accumulators;
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}
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2020-04-04 14:03:43 +02:00
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}
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