adpted scholexplorer Dump generation to the new Dataset definition

dhp-workflows/dhp-doiboost/src/main/java/eu/dnetlib/doiboost/crossref/Crossref2Oaf.scala

@@ -14,6 +14,8 @@ import scala.collection.JavaConverters._
 import scala.collection.mutable
 import scala.util.matching.Regex
 
+case class CrossrefDT(doi: String, json:String) {}
+
 case class mappingAffiliation(name: String) {}
 
 case class mappingAuthor(given: Option[String], family: String, ORCID: Option[String], affiliation: Option[mappingAffiliation]) {}

dhp-workflows/dhp-doiboost/src/main/java/eu/dnetlib/doiboost/crossref/CrossrefDataset.scala

@@ -0,0 +1,93 @@
+package eu.dnetlib.doiboost.crossref
+
+import eu.dnetlib.dhp.application.ArgumentApplicationParser
+import org.apache.commons.io.IOUtils
+import org.apache.spark.SparkConf
+import org.apache.spark.sql.expressions.Aggregator
+import org.apache.spark.sql.{Dataset, Encoder, Encoders, SaveMode, SparkSession}
+import org.json4s
+import org.json4s.DefaultFormats
+import org.json4s.jackson.JsonMethods.parse
+import org.slf4j.{Logger, LoggerFactory}
+
+object CrossrefDataset {
+
+
+  def extractTimestamp(input:String): Long = {
+
+    implicit lazy val formats: DefaultFormats.type = org.json4s.DefaultFormats
+    lazy val json: json4s.JValue = parse(input)
+
+    (json\"indexed"\"timestamp").extractOrElse[Long](0)
+
+  }
+
+
+  def main(args: Array[String]): Unit = {
+
+
+    val logger: Logger = LoggerFactory.getLogger(SparkMapDumpIntoOAF.getClass)
+    val conf: SparkConf = new SparkConf()
+    val parser = new ArgumentApplicationParser(IOUtils.toString(CrossrefDataset.getClass.getResourceAsStream("/eu/dnetlib/dhp/doiboost/crossref_to_dataset_params.json")))
+    parser.parseArgument(args)
+    val spark: SparkSession =
+      SparkSession
+        .builder()
+        .config(conf)
+        .appName(SparkMapDumpIntoOAF.getClass.getSimpleName)
+        .master(parser.get("master")).getOrCreate()
+    import spark.implicits._
+
+
+    val crossrefAggregator = new Aggregator[CrossrefDT, CrossrefDT, CrossrefDT] with Serializable {
+
+      override def zero: CrossrefDT = null
+
+      override def reduce(b: CrossrefDT, a: CrossrefDT): CrossrefDT = {
+        if (b == null)
+          return a
+        if (a == null)
+          return b
+
+        val tb = extractTimestamp(b.json)
+        val ta = extractTimestamp(a.json)
+        if(ta >tb) {
+          return a
+        }
+        b
+      }
+
+      override def merge(a: CrossrefDT, b: CrossrefDT): CrossrefDT = {
+        if (b == null)
+          return a
+        if (a == null)
+          return b
+
+        val tb = extractTimestamp(b.json)
+        val ta = extractTimestamp(a.json)
+        if(ta >tb) {
+          return a
+        }
+        b
+      }
+
+      override def bufferEncoder: Encoder[CrossrefDT] = implicitly[Encoder[CrossrefDT]]
+
+      override def outputEncoder: Encoder[CrossrefDT] = implicitly[Encoder[CrossrefDT]]
+
+      override def finish(reduction: CrossrefDT): CrossrefDT = reduction
+    }
+
+    val sourcePath:String = parser.get("sourcePath")
+    val targetPath:String = parser.get("targetPath")
+
+    val ds:Dataset[CrossrefDT] = spark.read.load(sourcePath).as[CrossrefDT]
+
+    ds.groupByKey(_.doi)
+      .agg(crossrefAggregator.toColumn)
+      .map(s=>s._2)
+      .write.mode(SaveMode.Overwrite).save(targetPath)
+
+  }
+
+}

dhp-workflows/dhp-doiboost/src/main/resources/eu/dnetlib/dhp/doiboost/crossref/oozie_app/workflow.xml

@@ -46,11 +46,11 @@
             <job-tracker>${jobTracker}</job-tracker>
             <name-node>${nameNode}</name-node>
             <main-class>eu.dnetlib.doiboost.crossref.CrossrefImporter</main-class>
-            <arg>-t</arg><arg>${workingPath}/input/crossref/index_dump</arg>
+            <arg>-t</arg><arg>${workingPath}/input/crossref/index_dump_1</arg>
             <arg>-n</arg><arg>${nameNode}</arg>
             <arg>-ts</arg><arg>${timestamp}</arg>
         </java>
-        <ok to="ExtractCrossrefToOAF"/>
+        <ok to="End"/>
         <error to="Kill"/>
     </action>
 
@@ -68,7 +68,7 @@
                 --driver-memory=${sparkDriverMemory}
                 ${sparkExtraOPT}
             </spark-opts>
-            <arg>--sourcePath</arg><arg>${workingPath}/input/crossref/index_dump,${workingPath}/crossref/index_dump</arg>
+            <arg>--sourcePath</arg><arg>${workingPath}/input/crossref/index_dump,${workingPath}/input/crossref/index_dump_1,${workingPath}/crossref/index_dump</arg>
             <arg>--targetPath</arg><arg>${workingPath}/input/crossref</arg>
             <arg>--master</arg><arg>yarn-cluster</arg>
         </spark>
@@ -76,5 +76,28 @@
         <error to="Kill"/>
     </action>
 
+
+
+    <action name="GenerateDataset">
+        <spark xmlns="uri:oozie:spark-action:0.2">
+            <master>yarn-cluster</master>
+            <mode>cluster</mode>
+            <name>ExtractCrossrefToOAF</name>
+            <class>eu.dnetlib.doiboost.crossref.CrossrefDataset</class>
+            <jar>dhp-doiboost-${projectVersion}.jar</jar>
+            <spark-opts>
+                --executor-memory=${sparkExecutorMemory}
+                --executor-cores=${sparkExecutorCores}
+                --driver-memory=${sparkDriverMemory}
+                ${sparkExtraOPT}
+            </spark-opts>
+            <arg>--sourcePath</arg><arg>/data/doiboost/crossref/cr_dataset</arg>
+            <arg>--targetPath</arg><arg>/data/doiboost/crossref/crossrefDataset</arg>
+            <arg>--master</arg><arg>yarn-cluster</arg>
+        </spark>
+        <ok to="End"/>
+        <error to="Kill"/>
+    </action>
+
     <end name="End"/>
 </workflow-app>

dhp-workflows/dhp-doiboost/src/main/resources/eu/dnetlib/dhp/doiboost/crossref_to_dataset_params.json

@@ -0,0 +1,6 @@
+[
+  {"paramName":"s",   "paramLongName":"sourcePath", "paramDescription": "the path of the sequencial file to read",  "paramRequired": true},
+  {"paramName":"t",   "paramLongName":"targetPath", "paramDescription": "the working dir path",                      "paramRequired": true},
+  {"paramName":"m",   "paramLongName":"master",     "paramDescription": "the master name",                          "paramRequired": true}
+
+]

dhp-workflows/dhp-doiboost/src/main/resources/eu/dnetlib/dhp/doiboost/intersection/oozie_app/workflow.xml

@@ -89,7 +89,7 @@
             </spark-opts>
             <arg>--dbPublicationPath</arg><arg>${workingDirPath}/doiBoostPublicationFiltered</arg>
             <arg>--dbDatasetPath</arg><arg>${workingDirPath}/crossrefDataset</arg>
-            <arg>--crossRefRelation</arg><arg>/data/doiboost/input/crossref/relations</arg>
+            <arg>--crossRefRelation</arg><arg>${workingDirPath}/crossrefRelation</arg>
             <arg>--dbaffiliationRelationPath</arg><arg>${workingDirPath}/doiBoostPublicationAffiliation</arg>
             <arg>-do</arg><arg>${workingDirPath}/doiBoostOrganization</arg>
             <arg>--targetPath</arg><arg>${workingDirPath}/actionDataSet</arg>

dhp-workflows/dhp-doiboost/src/test/java/eu/dnetlib/dhp/doiboost/QueryTest.scala

@@ -1,54 +1,45 @@
 package eu.dnetlib.dhp.doiboost
-import eu.dnetlib.dhp.schema.oaf.{Publication, Relation, StructuredProperty, Dataset => OafDataset}
+import eu.dnetlib.dhp.schema.oaf.Project
+import org.apache.spark.SparkContext
 import org.apache.spark.sql.functions.{col, sum}
+import org.apache.hadoop.io.Text
+import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.{Dataset, Encoder, Encoders, SparkSession}
-
+import org.codehaus.jackson.map.ObjectMapper
+import org.json4s.DefaultFormats
+import org.json4s
+import org.json4s.DefaultFormats
+import org.json4s.JsonAST._
+import org.json4s.jackson.JsonMethods._
 import scala.::
 import scala.collection.JavaConverters._
 class QueryTest {
 
+  def extract_payload(input:String) :String = {
 
-  def extractLicense(p:Publication):Tuple2[String,String] = {
-
-      val tmp = p.getInstance().asScala.map(i => i.getLicense.getValue).distinct.mkString(",")
-      (p.getId,tmp)
-    }
+    implicit lazy val formats: DefaultFormats.type = org.json4s.DefaultFormats
+    lazy val json: json4s.JValue = parse(input)
 
 
-
-  def hasDOI(publication: Publication, doi:String):Boolean = {
+    compact(render((json \ "payload")))
 
 
-    val s = publication.getOriginalId.asScala.filter(i =>  i.equalsIgnoreCase(doi))
-
-    s.nonEmpty
 
   }
 
-  def hasNullHostedBy(publication: Publication):Boolean = {
-    publication.getInstance().asScala.exists(i => i.getHostedby == null || i.getHostedby.getValue == null)
-  }
+
+  def myQuery(spark:SparkSession, sc:SparkContext): Unit = {
+    implicit val mapEncoderPub: Encoder[Project] = Encoders.kryo[Project]
+
+
+//    val ds:Dataset[Project] = spark.createDataset(sc.sequenceFile("", classOf[Text], classOf[Text])
+//      .map(_._2.toString)
+//      .map(s => new ObjectMapper().readValue(s, classOf[Project])))
+//
+//      ds.write.saveAsTable()
 
 
 
-  def myQuery(spark:SparkSession): Unit = {
-    implicit val mapEncoderPub: Encoder[Publication] = Encoders.kryo[Publication]
-    implicit val mapEncoderDat: Encoder[OafDataset] = Encoders.kryo[OafDataset]
-    implicit val mapEncoderRel: Encoder[Relation] = Encoders.kryo[Relation]
-
-    val doiboostPubs:Dataset[Publication]    = spark.read.load("/data/doiboost/process/doiBoostPublicationFiltered").as[Publication]
-
-    val relFunder: Dataset[Relation] = spark.read.format("org.apache.spark.sql.parquet").load("/data/doiboost/process/crossrefRelation").as[Relation]
-
-    doiboostPubs.filter(p => p.getDateofacceptance != null && p.getDateofacceptance.getValue!= null && p.getDateofacceptance.getValue.length > 0 )
-
-    doiboostPubs.filter(p=>hasDOI(p, "10.1016/j.is.2020.101522")).collect()(0).getDescription.get(0).getValue
-
-
-
-    doiboostPubs.filter(p=> hasNullHostedBy(p)).count()
-
-    doiboostPubs.map(p=> (p.getId, p.getBestaccessright.getClassname))(Encoders.tuple(Encoders.STRING,Encoders.STRING))
   }
 
 }

dhp-workflows/dhp-doiboost/src/test/java/eu/dnetlib/doiboost/crossref/CrossrefMappingTest.scala

@@ -19,8 +19,6 @@ class CrossrefMappingTest {
 
 
 
-
-
   @Test
   def testFunderRelationshipsMapping(): Unit = {
     val template = Source.fromInputStream(getClass.getResourceAsStream("article_funder_template.json")).mkString

dhp-workflows/dhp-graph-mapper/pom.xml

@@ -84,6 +84,12 @@
             <version>${project.version}</version>
         </dependency>
 
+        <dependency>
+            <groupId>eu.dnetlib.dhp</groupId>
+            <artifactId>dhp-dedup-openaire</artifactId>
+            <version>${project.version}</version>
+        </dependency>
+
         <dependency>
             <groupId>com.jayway.jsonpath</groupId>
             <artifactId>json-path</artifactId>

dhp-workflows/dhp-graph-mapper/src/main/java/eu/dnetlib/dhp/sx/ebi/EBIAggregator.scala

@@ -1,4 +1,5 @@
 package eu.dnetlib.dhp.sx.ebi
+import eu.dnetlib.dhp.oa.dedup.AuthorMerger
 import eu.dnetlib.dhp.schema.oaf.{Publication, Relation, Dataset => OafDataset}
 import eu.dnetlib.dhp.schema.scholexplorer.{DLIDataset, DLIPublication, DLIUnknown}
 import org.apache.spark.sql.{Encoder, Encoders}
@@ -14,6 +15,7 @@ object EBIAggregator {
 
     override def reduce(b: OafDataset, a: (String, OafDataset)): OafDataset = {
       b.mergeFrom(a._2)
+      b.setAuthor(AuthorMerger.mergeAuthor(a._2.getAuthor, b.getAuthor))
       if (b.getId == null)
         b.setId(a._2.getId)
       b
@@ -22,6 +24,7 @@ object EBIAggregator {
 
     override def merge(wx: OafDataset, wy: OafDataset): OafDataset = {
       wx.mergeFrom(wy)
+      wx.setAuthor(AuthorMerger.mergeAuthor(wy.getAuthor, wx.getAuthor))
       if(wx.getId == null && wy.getId.nonEmpty)
         wx.setId(wy.getId)
       wx
@@ -35,8 +38,6 @@ object EBIAggregator {
       Encoders.kryo(classOf[OafDataset])
   }
 
-
-
   def getDLIUnknownAggregator(): Aggregator[(String, DLIUnknown), DLIUnknown, DLIUnknown] = new Aggregator[(String, DLIUnknown), DLIUnknown, DLIUnknown]{
 
     override def zero: DLIUnknown = new DLIUnknown()
@@ -69,6 +70,7 @@ object EBIAggregator {
 
     override def reduce(b: DLIDataset, a: (String, DLIDataset)): DLIDataset = {
       b.mergeFrom(a._2)
+      b.setAuthor(AuthorMerger.mergeAuthor(a._2.getAuthor, b.getAuthor))
       if (b.getId == null)
         b.setId(a._2.getId)
       b
@@ -76,6 +78,7 @@ object EBIAggregator {
 
     override def merge(wx: DLIDataset, wy: DLIDataset): DLIDataset = {
       wx.mergeFrom(wy)
+      wx.setAuthor(AuthorMerger.mergeAuthor(wy.getAuthor, wx.getAuthor))
       if(wx.getId == null && wy.getId.nonEmpty)
         wx.setId(wy.getId)
       wx
@@ -96,6 +99,8 @@ object EBIAggregator {
 
     override def reduce(b: DLIPublication, a: (String, DLIPublication)): DLIPublication = {
       b.mergeFrom(a._2)
+      b.setAuthor(AuthorMerger.mergeAuthor(a._2.getAuthor, b.getAuthor))
+
       if (b.getId == null)
         b.setId(a._2.getId)
       b
@@ -104,6 +109,7 @@ object EBIAggregator {
 
     override def merge(wx: DLIPublication, wy: DLIPublication): DLIPublication = {
       wx.mergeFrom(wy)
+      wx.setAuthor(AuthorMerger.mergeAuthor(wy.getAuthor, wx.getAuthor))
       if(wx.getId == null && wy.getId.nonEmpty)
         wx.setId(wy.getId)
       wx
@@ -124,6 +130,7 @@ object EBIAggregator {
 
     override def reduce(b: Publication, a: (String, Publication)): Publication = {
       b.mergeFrom(a._2)
+      b.setAuthor(AuthorMerger.mergeAuthor(a._2.getAuthor, b.getAuthor))
       if (b.getId == null)
         b.setId(a._2.getId)
       b
@@ -132,6 +139,7 @@ object EBIAggregator {
 
     override def merge(wx: Publication, wy: Publication): Publication = {
       wx.mergeFrom(wy)
+      wx.setAuthor(AuthorMerger.mergeAuthor(wy.getAuthor, wx.getAuthor))
       if(wx.getId == null && wy.getId.nonEmpty)
         wx.setId(wy.getId)
       wx
@@ -145,7 +153,6 @@ object EBIAggregator {
       Encoders.kryo(classOf[Publication])
   }
 
-
   def getRelationAggregator(): Aggregator[(String, Relation), Relation, Relation] = new Aggregator[(String, Relation), Relation, Relation]{
 
     override def zero: Relation = new Relation()
@@ -166,10 +173,4 @@ object EBIAggregator {
     override def outputEncoder: Encoder[Relation] =
       Encoders.kryo(classOf[Relation])
   }
-
-
-
-
-
-
 }

dhp-workflows/dhp-graph-mapper/src/test/java/eu/dnetlib/dhp/sx/graph/SparkScholexplorerGraphImporterTest.java

@@ -2,4 +2,5 @@
 package eu.dnetlib.dhp.sx.graph;
 
 public class SparkScholexplorerGraphImporterTest {
+
 }

dhp-workflows/dhp-graph-mapper/src/test/resources/eu/dnetlib/dhp/sx/graph/publication.json

@@ -0,0 +1,10 @@
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+{"collectedfrom":[{"key":"dli_________::crossref","value":"Crossref","dataInfo":null}],"dataInfo":{"invisible":false,"inferred":null,"deletedbyinference":false,"trust":"0.9","inferenceprovenance":null,"provenanceaction":null},"lastupdatetimestamp":null,"id":"50|1307198540d2264d839dfd8c9a19f4a7","originalId":["1307198540d2264d839dfd8c9a19f4a7"],"pid":[{"value":"10.3390/w11050916","qualifier":{"classid":"doi","classname":"doi","schemeid":"dnet:pid_types","schemename":"dnet:pid_types"},"dataInfo":null}],"dateofcollection":"2020-08-14T14:25:55.176Z","dateoftransformation":null,"extraInfo":null,"oaiprovenance":null,"measures":null,"author":[{"fullname":"Cao Qing","name":null,"surname":null,"rank":null,"pid":null,"affiliation":null},{"fullname":"Hao Zhenchun","name":null,"surname":null,"rank":null,"pid":null,"affiliation":null},{"fullname":"Yuan Feifei","name":null,"surname":null,"rank":null,"pid":null,"affiliation":null},{"fullname":"Berndtsson Ronny","name":null,"surname":null,"rank":null,"pid":null,"affiliation":null},{"fullname":"Xu Shijie","name":null,"surname":null,"rank":null,"pid":null,"affiliation":null},{"fullname":"Gao Huibin","name":null,"surname":null,"rank":null,"pid":null,"affiliation":null},{"fullname":"Hao Jie","name":null,"surname":null,"rank":null,"pid":null,"affiliation":null}],"resulttype":{"classid":"publication","classname":"publication","schemeid":"publication","schemename":"publication"},"language":null,"country":null,"subject":[],"title":[{"value":"On the Predictability of Daily Rainfall during Rainy Season over the Huaihe River Basin","qualifier":{"classid":"main title","classname":null,"schemeid":"dnet:dataCite_title","schemename":"dnet:dataCite_title"},"dataInfo":null}],"relevantdate":[{"value":"2019-05-02T07:15:22Z","qualifier":{"classid":"date","classname":"date","schemeid":"dnet::date","schemename":"dnet::date"},"dataInfo":null}],"description":[{"value":null,"dataInfo":null}],"dateofacceptance":null,"publisher":{"value":"MDPI AG","dataInfo":null},"embargoenddate":null,"source":null,"fulltext":null,"format":null,"contributor":null,"resourcetype":null,"coverage":null,"bestaccessright":null,"context":null,"externalReference":null,"instance":[],"journal":null,"originalObjIdentifier":"dli_resolver::1307198540d2264d839dfd8c9a19f4a7","dlicollectedfrom":[{"id":"dli_________::crossref","name":"Crossref","completionStatus":"complete","collectionMode":"resolved"}],"completionStatus":"complete"}
+{"collectedfrom":[{"key":"dli_________::crossref","value":"Crossref","dataInfo":null}],"dataInfo":{"invisible":false,"inferred":null,"deletedbyinference":false,"trust":"0.9","inferenceprovenance":null,"provenanceaction":null},"lastupdatetimestamp":null,"id":"50|1307198540d2264d839dfd8c9a19f4a7","originalId":["1307198540d2264d839dfd8c9a19f4a7"],"pid":[{"value":"10.3390/w11050916","qualifier":{"classid":"doi","classname":"doi","schemeid":"dnet:pid_types","schemename":"dnet:pid_types"},"dataInfo":null}],"dateofcollection":"2020-08-09T11:35:23.526Z","dateoftransformation":null,"extraInfo":null,"oaiprovenance":null,"measures":null,"author":[{"fullname":"Cao Qing","name":null,"surname":null,"rank":null,"pid":null,"affiliation":null},{"fullname":"Hao Zhenchun","name":null,"surname":null,"rank":null,"pid":null,"affiliation":null},{"fullname":"Yuan Feifei","name":null,"surname":null,"rank":null,"pid":null,"affiliation":null},{"fullname":"Berndtsson Ronny","name":null,"surname":null,"rank":null,"pid":null,"affiliation":null},{"fullname":"Xu Shijie","name":null,"surname":null,"rank":null,"pid":null,"affiliation":null},{"fullname":"Gao Huibin","name":null,"surname":null,"rank":null,"pid":null,"affiliation":null},{"fullname":"Hao Jie","name":null,"surname":null,"rank":null,"pid":null,"affiliation":null}],"resulttype":{"classid":"publication","classname":"publication","schemeid":"publication","schemename":"publication"},"language":null,"country":null,"subject":[],"title":[{"value":"On the Predictability of Daily Rainfall during Rainy Season over the Huaihe River Basin","qualifier":{"classid":"main title","classname":null,"schemeid":"dnet:dataCite_title","schemename":"dnet:dataCite_title"},"dataInfo":null}],"relevantdate":[{"value":"2019-05-02T07:15:22Z","qualifier":{"classid":"date","classname":"date","schemeid":"dnet::date","schemename":"dnet::date"},"dataInfo":null}],"description":[{"value":null,"dataInfo":null}],"dateofacceptance":null,"publisher":{"value":"MDPI AG","dataInfo":null},"embargoenddate":null,"source":null,"fulltext":null,"format":null,"contributor":null,"resourcetype":null,"coverage":null,"bestaccessright":null,"context":null,"externalReference":null,"instance":[],"journal":null,"originalObjIdentifier":"dli_resolver::1307198540d2264d839dfd8c9a19f4a7","dlicollectedfrom":[{"id":"dli_________::crossref","name":"Crossref","completionStatus":"complete","collectionMode":"resolved"}],"completionStatus":"complete"}

dhp-workflows/dhp-graph-provision-scholexplorer/src/main/java/eu/dnetlib/dhp/export/SparkExportContentForOpenAire.scala

@@ -32,10 +32,10 @@ object SparkExportContentForOpenAire {
         .master(parser.get("master")).getOrCreate()
 
 
-    val sc:SparkContext = spark.sparkContext
-
     val workingPath = parser.get("workingDirPath")
 
+    implicit  val dliPubEncoder: Encoder[DLIPublication] = Encoders.kryo(classOf[DLIPublication])
+    implicit  val dliDatEncoder: Encoder[DLIDataset] = Encoders.kryo(classOf[DLIDataset])
     implicit  val pubEncoder: Encoder[Publication] = Encoders.bean(classOf[Publication])
     implicit  val datEncoder: Encoder[OafDataset] = Encoders.bean(classOf[OafDataset])
     implicit  val relEncoder: Encoder[Relation] = Encoders.bean(classOf[Relation])
@@ -43,40 +43,41 @@ object SparkExportContentForOpenAire {
     import spark.implicits._
 
 
-    val relRDD:RDD[Relation] = sc.textFile(s"$workingPath/relation_j")
-      .map(s => new ObjectMapper().readValue(s, classOf[Relation]))
-      .filter(p => p.getDataInfo.getDeletedbyinference == false)
-    spark.createDataset(relRDD).write.mode(SaveMode.Overwrite).save(s"$workingPath/relationDS")
+    val dsRel = spark.read.load(s"$workingPath/relation_b").as[Relation]
+    dsRel.filter(r => r.getDataInfo==null || r.getDataInfo.getDeletedbyinference ==false).write.mode(SaveMode.Overwrite).save(s"$workingPath/export/relationDS")
 
-    val datRDD:RDD[OafDataset] = sc.textFile(s"$workingPath/dataset")
-      .map(s => new ObjectMapper().readValue(s, classOf[DLIDataset]))
+
+    val dsPubs = spark.read.load(s"$workingPath/publication").as[DLIPublication]
+    dsPubs
+      .filter(p=>p.getDataInfo.getDeletedbyinference == false)
+      .map(DLIToOAF.convertDLIPublicationToOAF)
+      .filter(p=>p!= null)
+      .write.mode(SaveMode.Overwrite).save(s"$workingPath/export/publicationDS")
+
+
+    val dsDataset = spark.read.load(s"$workingPath/dataset").as[DLIDataset]
+    dsDataset
       .filter(p => p.getDataInfo.getDeletedbyinference == false)
       .map(DLIToOAF.convertDLIDatasetTOOAF).filter(p=>p!= null)
-    spark.createDataset(datRDD).write.mode(SaveMode.Overwrite).save(s"$workingPath/datasetDS")
-
-
-    val pubRDD:RDD[Publication] = sc.textFile(s"$workingPath/publication")
-      .map(s => new ObjectMapper().readValue(s, classOf[DLIPublication]))
-      .filter(p => p.getDataInfo.getDeletedbyinference == false)
-      .map(DLIToOAF.convertDLIPublicationToOAF).filter(p=>p!= null)
-    spark.createDataset(pubRDD).write.mode(SaveMode.Overwrite).save(s"$workingPath/publicationDS")
+     .write.mode(SaveMode.Overwrite).save(s"$workingPath/export/datasetDS")
 
 
 
-    val pubs:Dataset[Publication] = spark.read.load(s"$workingPath/publicationDS").as[Publication]
-    val dats :Dataset[OafDataset] = spark.read.load(s"$workingPath/datasetDS").as[OafDataset]
-    val relDS1 :Dataset[Relation] = spark.read.load(s"$workingPath/relationDS").as[Relation]
+
+    val pubs:Dataset[Publication] = spark.read.load(s"$workingPath/export/publicationDS").as[Publication]
+    val dats :Dataset[OafDataset] = spark.read.load(s"$workingPath/export/datasetDS").as[OafDataset]
+    val relDS1 :Dataset[Relation] = spark.read.load(s"$workingPath/export/relationDS").as[Relation]
 
 
     val pub_id = pubs.select("id").distinct()
     val dat_id = dats.select("id").distinct()
 
 
-    pub_id.joinWith(relDS1, pub_id("id").equalTo(relDS1("source"))).map(k => k._2).write.mode(SaveMode.Overwrite).save(s"$workingPath/relationDS_f1")
+    pub_id.joinWith(relDS1, pub_id("id").equalTo(relDS1("source"))).map(k => k._2).write.mode(SaveMode.Overwrite).save(s"$workingPath/export/relationDS_f1")
 
-    val relDS2= spark.read.load(s"$workingPath/relationDS_f1").as[Relation]
+    val relDS2= spark.read.load(s"$workingPath/export/relationDS_f1").as[Relation]
 
-    relDS2.joinWith(dat_id, relDS2("target").equalTo(dats("id"))).map(k => k._1).write.mode(SaveMode.Overwrite).save(s"$workingPath/relationDS_filtered")
+    relDS2.joinWith(dat_id, relDS2("target").equalTo(dats("id"))).map(k => k._1).write.mode(SaveMode.Overwrite).save(s"$workingPath/export/relationDS_filtered")
 
 
     val r_source = relDS2.select(relDS2("source")).distinct()
@@ -87,22 +88,20 @@ object SparkExportContentForOpenAire {
 
     pubs.joinWith(r_source, pubs("id").equalTo(r_source("source")), "inner").map(k => k._1)
       .withColumn("row",row_number.over(w2)).where($"row" === 1).drop("row")
-      .write.mode(SaveMode.Overwrite).save(s"$workingPath/publicationDS_filtered")
+      .write.mode(SaveMode.Overwrite).save(s"$workingPath/export/publicationDS_filtered")
 
     dats.joinWith(r_target, dats("id").equalTo(r_target("target")), "inner").map(k => k._1)
       .withColumn("row",row_number.over(w2)).where($"row" === 1).drop("row")
-      .write.mode(SaveMode.Overwrite).save(s"$workingPath/datasetAS")
+      .write.mode(SaveMode.Overwrite).save(s"$workingPath/export/datasetAS")
 
-    spark.createDataset(sc.textFile(s"$workingPath/dataset")
-      .map(s => new ObjectMapper().readValue(s, classOf[DLIDataset]))
-      .map(DLIToOAF.convertDLIDatasetToExternalReference)
-      .filter(p => p != null)).as[DLIExternalReference].write.mode(SaveMode.Overwrite).save(s"$workingPath/externalReference")
 
-    val pf = spark.read.load(s"$workingPath/publicationDS_filtered").select("id")
-    val relDS3  = spark.read.load(s"$workingPath/relationDS").as[Relation]
+    dsDataset.map(DLIToOAF.convertDLIDatasetToExternalReference).filter(p => p != null).write.mode(SaveMode.Overwrite).save(s"$workingPath/export/externalReference")
+
+    val pf = spark.read.load(s"$workingPath/export/publicationDS_filtered").select("id")
+    val relDS3  = spark.read.load(s"$workingPath/export/relationDS").as[Relation]
     val relationTo = pf.joinWith(relDS3, pf("id").equalTo(relDS3("source")),"inner").map(t =>t._2)
 
-    val extRef =  spark.read.load(s"$workingPath/externalReference").as[DLIExternalReference]
+    val extRef =  spark.read.load(s"$workingPath/export/externalReference").as[DLIExternalReference]
 
     spark.createDataset(relationTo.joinWith(extRef, relationTo("target").equalTo(extRef("id")), "inner").map(d => {
         val r = d._1
@@ -112,11 +111,11 @@ object SparkExportContentForOpenAire {
       var dli_ext = ArrayBuffer[DLIExternalReference]()
       f._2.foreach(d => if (dli_ext.size < 100) dli_ext += d )
       (f._1, dli_ext)
-    })).write.mode(SaveMode.Overwrite).save(s"$workingPath/externalReference_grouped")
+    })).write.mode(SaveMode.Overwrite).save(s"$workingPath/export/externalReference_grouped")
 
-    val pubf :Dataset[Publication] = spark.read.load(s"$workingPath/publicationDS_filtered").as[Publication]
+    val pubf :Dataset[Publication] = spark.read.load(s"$workingPath/export/publicationDS_filtered").as[Publication]
 
-    val groupedERf:Dataset[(String, List[DLIExternalReference])]= spark.read.load(s"$workingPath/externalReference_grouped").as[(String, List[DLIExternalReference])]
+    val groupedERf:Dataset[(String, List[DLIExternalReference])]= spark.read.load(s"$workingPath/export/externalReference_grouped").as[(String, List[DLIExternalReference])]
 
     groupedERf.joinWith(pubf,pubf("id").equalTo(groupedERf("_1"))).map(t =>
       {
@@ -128,29 +127,28 @@ object SparkExportContentForOpenAire {
         } else
           publication
       }
-    ).write.mode(SaveMode.Overwrite).save(s"$workingPath/publicationAS")
+    ).write.mode(SaveMode.Overwrite).save(s"$workingPath/export/publicationAS")
 
 
-    spark.createDataset(sc.textFile(s"$workingPath/dataset")
-      .map(s => new ObjectMapper().readValue(s, classOf[DLIDataset]))
+    dsDataset
       .map(DLIToOAF.convertClinicalTrial)
-      .filter(p => p != null))
-      .write.mode(SaveMode.Overwrite).save(s"$workingPath/clinicalTrials")
+      .filter(p => p != null)
+      .write.mode(SaveMode.Overwrite).save(s"$workingPath/export/clinicalTrials")
 
-    val ct:Dataset[(String,String)] = spark.read.load(s"$workingPath/clinicalTrials").as[(String,String)]
+    val ct:Dataset[(String,String)] = spark.read.load(s"$workingPath/export/clinicalTrials").as[(String,String)]
 
-    val relDS= spark.read.load(s"$workingPath/relationDS_f1").as[Relation]
+    val relDS= spark.read.load(s"$workingPath/export/relationDS_f1").as[Relation]
 
     relDS.joinWith(ct, relDS("target").equalTo(ct("_1")), "inner")
       .map(k =>{
         val currentRel = k._1
         currentRel.setTarget(k._2._2)
         currentRel
-      }).write.mode(SaveMode.Overwrite).save(s"$workingPath/clinicalTrialsRels")
+      }).write.mode(SaveMode.Overwrite).save(s"$workingPath/export/clinicalTrialsRels")
 
 
-    val clRels:Dataset[Relation] = spark.read.load(s"$workingPath/clinicalTrialsRels").as[Relation]
-    val rels:Dataset[Relation] = spark.read.load(s"$workingPath/relationDS_filtered").as[Relation]
+    val clRels:Dataset[Relation] = spark.read.load(s"$workingPath/export/clinicalTrialsRels").as[Relation]
+    val rels:Dataset[Relation] = spark.read.load(s"$workingPath/export/relationDS_filtered").as[Relation]
 
     rels.union(clRels).flatMap(r => {
       val inverseRel = new Relation
@@ -162,18 +160,18 @@ object SparkExportContentForOpenAire {
       inverseRel.setSubRelType(r.getSubRelType)
       inverseRel.setRelClass(DLIToOAF.rel_inverse(r.getRelClass))
       List(r, inverseRel)
-    }).write.mode(SaveMode.Overwrite).save(s"$workingPath/relationAS")
+    }).write.mode(SaveMode.Overwrite).save(s"$workingPath/export/relationAS")
 
 
 
-    spark.read.load(s"$workingPath/publicationAS").as[Publication].map(DLIToOAF.fixInstance).write.mode(SaveMode.Overwrite).save(s"$workingPath/publicationAS_fixed")
-    spark.read.load(s"$workingPath/datasetAS").as[OafDataset].map(DLIToOAF.fixInstanceDataset).write.mode(SaveMode.Overwrite).save(s"$workingPath/datasetAS_fixed")
+    spark.read.load(s"$workingPath/export/publicationAS").as[Publication].map(DLIToOAF.fixInstance).write.mode(SaveMode.Overwrite).save(s"$workingPath/export/publicationAS_fixed")
+    spark.read.load(s"$workingPath/export/datasetAS").as[OafDataset].map(DLIToOAF.fixInstanceDataset).write.mode(SaveMode.Overwrite).save(s"$workingPath/export/datasetAS_fixed")
 
-    val fRels:Dataset[(String,String)] = spark.read.load(s"$workingPath/relationAS").as[Relation].map(DLIToOAF.toActionSet)
-    val fpubs:Dataset[(String,String)] = spark.read.load(s"$workingPath/publicationAS_fixed").as[Publication].map(DLIToOAF.toActionSet)
-    val fdats:Dataset[(String,String)] = spark.read.load(s"$workingPath/datasetAS_fixed").as[OafDataset].map(DLIToOAF.toActionSet)
+    val fRels:Dataset[(String,String)] = spark.read.load(s"$workingPath/export/relationAS").as[Relation].map(DLIToOAF.toActionSet)
+    val fpubs:Dataset[(String,String)] = spark.read.load(s"$workingPath/export/publicationAS_fixed").as[Publication].map(DLIToOAF.toActionSet)
+    val fdats:Dataset[(String,String)] = spark.read.load(s"$workingPath/export/datasetAS_fixed").as[OafDataset].map(DLIToOAF.toActionSet)
 
-    fRels.union(fpubs).union(fdats).rdd.map(s => (new Text(s._1), new Text(s._2))).saveAsHadoopFile(s"$workingPath/rawset", classOf[Text], classOf[Text], classOf[SequenceFileOutputFormat[Text,Text]], classOf[GzipCodec])
+    fRels.union(fpubs).union(fdats).rdd.map(s => (new Text(s._1), new Text(s._2))).saveAsHadoopFile(s"$workingPath/export/rawset", classOf[Text], classOf[Text], classOf[SequenceFileOutputFormat[Text,Text]], classOf[GzipCodec])
   }