removed all comments in Italian

dhp-workflows/dhp-graph-mapper/src/main/java/eu/dnetlib/dhp/oa/graph/hostedbymap/SparkApplyHostedByMapToDatasource.scala

@@ -61,8 +61,6 @@ object SparkApplyHostedByMapToDatasource {
     val pinfo : Dataset[EntityInfo] = Aggregators.datasourceToSingleId( spark.read.textFile(preparedInfoPath)
       .map(ei => mapper.readValue(ei, classOf[EntityInfo])))
 
-    //c. dataset join risultato del passo prima di a per datasource id, gruppo per ds id e cambio compatibilita' se necessario
-
     applyHBtoDats(pinfo, dats).write.mode(SaveMode.Overwrite).option("compression","gzip").json(outputPath)
   }
 

dhp-workflows/dhp-graph-mapper/src/main/java/eu/dnetlib/dhp/oa/graph/hostedbymap/SparkApplyHostedByMapToResult.scala

@@ -14,9 +14,6 @@ import org.slf4j.{Logger, LoggerFactory}
 
 import scala.collection.JavaConverters._
 
-//a. publication join risultato del passo precedente su result id (left) setto la istanza (se piu' di una instance
-// nel result => salto)con l'hosted by anche access right della instance se openaccess e' true
-
 
 object SparkApplyHostedByMapToResult {
 
@@ -76,8 +73,6 @@ object SparkApplyHostedByMapToResult {
     val pinfo : Dataset[EntityInfo] = spark.read.textFile(preparedInfoPath)
         .map(ei => mapper.readValue(ei, classOf[EntityInfo]))
 
-    //a. publication join risultato del passo precedente su result id (left) setto la istanza (se piu' di una instance
-    // nel result => salto)con l'hosted by anche access right della instance se openaccess e' true
     applyHBtoPubs(pinfo, pubs).write.mode(SaveMode.Overwrite).option("compression","gzip").json(outputPath)
 
 

dhp-workflows/dhp-graph-mapper/src/main/java/eu/dnetlib/dhp/oa/graph/hostedbymap/SparkPrepareHostedByInfoToApply.scala

@@ -106,14 +106,15 @@ object SparkPrepareHostedByInfoToApply {
     import spark.implicits._
 
 
-    //STEP1: leggere la hostedbymap e trasformarla in entity info
+    //STEP1: read the hostedbymap and transform it in EntityInfo
     val hostedByInfo:Dataset[EntityInfo] = spark.createDataset(spark.sparkContext.textFile(hostedByMapPath)).map(toEntityInfo)
 
-   //STEP2: creare la mappa publication id issn, eissn, lissn esplosa
+   //STEP2: create association (publication, issn), (publication, eissn), (publication, lissn)
     val resultInfoDataset:Dataset[EntityInfo] = prepareResultInfo(spark, graphPath + "/publication")
 
-    //STEP3: join resultInfo con hostedByInfo sul journal_id dal result con left
-    // e riduzione di tutti i result con lo stesso id in una unica entry con aggiunto l'id della datasource
+    //STEP3: left join resultInfo with hostedByInfo on journal_id. Reduction of all the results with the same id in just
+    //one entry (one result could be associated to issn and eissn and so possivly matching more than once against the map)
+    //to this entry we add the id of the datasource for the next step
     joinResHBM(resultInfoDataset, hostedByInfo)
       .write.mode(SaveMode.Overwrite).option("compression", "gzip").json(outputPath)