enrico.ottonello
/
dnet-hadoop
forked from D-Net/dnet-hadoop
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

Merge branch 'master' into stable_ids

This commit is contained in:
Claudio Atzori 2020-10-09 13:53:40 +02:00
parent 8958f20813 e751c1402f
commit 3e6c8bca39
13 changed files with 232 additions and 103 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
dhp-workflows/dhp-doiboost/src/main/java/eu/dnetlib/doiboost/crossref/Crossref2Oaf.scala
View File

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

93
dhp-workflows/dhp-doiboost/src/main/java/eu/dnetlib/doiboost/crossref/CrossrefDataset.scala Normal file
View File

@ -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)
}
}

29
dhp-workflows/dhp-doiboost/src/main/resources/eu/dnetlib/dhp/doiboost/crossref/oozie_app/workflow.xml
View File

@ -46,11 +46,11 @@
<job-tracker>${jobTracker}</job-tracker> <job-tracker>${jobTracker}</job-tracker>
<name-node>${nameNode}</name-node> <name-node>${nameNode}</name-node>
<main-class>eu.dnetlib.doiboost.crossref.CrossrefImporter</main-class> <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>${nameNode}</arg> <arg>-n</arg><arg>${nameNode}</arg>
<arg>-ts</arg><arg>${timestamp}</arg> <arg>-ts</arg><arg>${timestamp}</arg>
</java> </java>
<ok to="ExtractCrossrefToOAF"/> <ok to="End"/>
<error to="Kill"/> <error to="Kill"/>
</action> </action>
@ -68,7 +68,7 @@
--driver-memory=${sparkDriverMemory} --driver-memory=${sparkDriverMemory}
${sparkExtraOPT} ${sparkExtraOPT}
</spark-opts> </spark-opts>
<arg>--sourcePath</arg><arg>${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>${workingPath}/input/crossref</arg> <arg>--targetPath</arg><arg>${workingPath}/input/crossref</arg>
<arg>--master</arg><arg>yarn-cluster</arg> <arg>--master</arg><arg>yarn-cluster</arg>
</spark> </spark>
@ -76,5 +76,28 @@
<error to="Kill"/> <error to="Kill"/>
</action> </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"/> <end name="End"/>
</workflow-app> </workflow-app>

6
dhp-workflows/dhp-doiboost/src/main/resources/eu/dnetlib/dhp/doiboost/crossref_to_dataset_params.json Normal file
View File

@ -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}
]

2
dhp-workflows/dhp-doiboost/src/main/resources/eu/dnetlib/dhp/doiboost/intersection/oozie_app/workflow.xml
View File

@ -89,7 +89,7 @@
</spark-opts> </spark-opts>
<arg>--dbPublicationPath</arg><arg>${workingDirPath}/doiBoostPublicationFiltered</arg> <arg>--dbPublicationPath</arg><arg>${workingDirPath}/doiBoostPublicationFiltered</arg>
<arg>--dbDatasetPath</arg><arg>${workingDirPath}/crossrefDataset</arg> <arg>--dbDatasetPath</arg><arg>${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>--dbaffiliationRelationPath</arg><arg>${workingDirPath}/doiBoostPublicationAffiliation</arg>
<arg>-do</arg><arg>${workingDirPath}/doiBoostOrganization</arg> <arg>-do</arg><arg>${workingDirPath}/doiBoostOrganization</arg>
<arg>--targetPath</arg><arg>${workingDirPath}/actionDataSet</arg> <arg>--targetPath</arg><arg>${workingDirPath}/actionDataSet</arg>

57
dhp-workflows/dhp-doiboost/src/test/java/eu/dnetlib/dhp/doiboost/QueryTest.scala
View File

@ -1,54 +1,45 @@
package eu.dnetlib.dhp.doiboost 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.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.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.::
import scala.collection.JavaConverters._ import scala.collection.JavaConverters._
class QueryTest { class QueryTest {
def extract_payload(input:String) :String = {
def extractLicense(p:Publication):Tuple2[String,String] = { implicit lazy val formats: DefaultFormats.type = org.json4s.DefaultFormats
lazy val json: json4s.JValue = parse(input)
val tmp = p.getInstance().asScala.map(i => i.getLicense.getValue).distinct.mkString(",")
(p.getId,tmp)
}
compact(render((json \ "payload")))
def hasDOI(publication: Publication, doi:String):Boolean = {
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))
} }
} }

2
dhp-workflows/dhp-doiboost/src/test/java/eu/dnetlib/doiboost/crossref/CrossrefMappingTest.scala
View File

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

6
dhp-workflows/dhp-graph-mapper/pom.xml
View File

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

19
dhp-workflows/dhp-graph-mapper/src/main/java/eu/dnetlib/dhp/sx/ebi/EBIAggregator.scala
View File

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

8
dhp-workflows/dhp-graph-mapper/src/main/resources/eu/dnetlib/dhp/oa/graph/hive/oozie_app/lib/scripts/postprocessing.sql
View File

@ -1,10 +1,10 @@
DROP VIEW IF EXISTS ${hiveDbName}.result; DROP VIEW IF EXISTS ${hiveDbName}.result;
CREATE VIEW IF NOT EXISTS ${hiveDbName}.result as CREATE VIEW IF NOT EXISTS ${hiveDbName}.result as
select id, dateofcollection, title, publisher, bestaccessright, datainfo, collectedfrom, pid, author, resulttype, language, country, subject, description, dateofacceptance, relevantdate, embargoenddate, resourcetype, context, externalreference, instance from ${hiveDbName}.publication p select id, originalid, dateofcollection, title, publisher, bestaccessright, datainfo, collectedfrom, pid, author, resulttype, language, country, subject, description, dateofacceptance, relevantdate, embargoenddate, resourcetype, context, externalreference, instance from ${hiveDbName}.publication p
union all union all
select id, dateofcollection, title, publisher, bestaccessright, datainfo, collectedfrom, pid, author, resulttype, language, country, subject, description, dateofacceptance, relevantdate, embargoenddate, resourcetype, context, externalreference, instance from ${hiveDbName}.dataset d select id, originalid, dateofcollection, title, publisher, bestaccessright, datainfo, collectedfrom, pid, author, resulttype, language, country, subject, description, dateofacceptance, relevantdate, embargoenddate, resourcetype, context, externalreference, instance from ${hiveDbName}.dataset d
union all union all
select id, dateofcollection, title, publisher, bestaccessright, datainfo, collectedfrom, pid, author, resulttype, language, country, subject, description, dateofacceptance, relevantdate, embargoenddate, resourcetype, context, externalreference, instance from ${hiveDbName}.software s select id, originalid, dateofcollection, title, publisher, bestaccessright, datainfo, collectedfrom, pid, author, resulttype, language, country, subject, description, dateofacceptance, relevantdate, embargoenddate, resourcetype, context, externalreference, instance from ${hiveDbName}.software s
union all union all
select id, dateofcollection, title, publisher, bestaccessright, datainfo, collectedfrom, pid, author, resulttype, language, country, subject, description, dateofacceptance, relevantdate, embargoenddate, resourcetype, context, externalreference, instance from ${hiveDbName}.otherresearchproduct o; select id, originalid, dateofcollection, title, publisher, bestaccessright, datainfo, collectedfrom, pid, author, resulttype, language, country, subject, description, dateofacceptance, relevantdate, embargoenddate, resourcetype, context, externalreference, instance from ${hiveDbName}.otherresearchproduct o;

1
dhp-workflows/dhp-graph-mapper/src/test/java/eu/dnetlib/dhp/sx/graph/SparkScholexplorerGraphImporterTest.java
View File

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

10
dhp-workflows/dhp-graph-mapper/src/test/resources/eu/dnetlib/dhp/sx/graph/publication.json Normal file
View File

@ -0,0 +1,10 @@
{"collectedfrom":[{"key":"dli_________::datacite","value":"Datasets in Datacite","dataInfo":null}],"dataInfo":{"invisible":false,"inferred":null,"deletedbyinference":false,"trust":"0.9","inferenceprovenance":null,"provenanceaction":null},"lastupdatetimestamp":null,"id":"50|1307198540d2264d839dfd8c9a19f4a7","originalId":["10.3390/w11050916"],"pid":[{"value":"10.3390/w11050916","qualifier":{"classid":"doi","classname":"doi","schemeid":"dnet:pid_types","schemename":"dnet:pid_types"},"dataInfo":null}],"dateofcollection":"2018-10-28T00:39:04.337Z","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":"2019-05-01","qualifier":{"classid":"date","classname":"date","schemeid":"dnet::date","schemename":"dnet::date"},"dataInfo":null}],"description":[{"value":"In terms of climate change and precipitation, there is large interest in how large-scale climatic features affect regional rainfall amount and rainfall occurrence. Large-scale climate elements need to be downscaled to the regional level for hydrologic applications. Here, a new Nonhomogeneous Hidden Markov Model (NHMM) called the Bayesian-NHMM is presented for downscaling and predicting of multisite daily rainfall during rainy season over the Huaihe River Basin (HRB). The Bayesian-NHMM provides a Bayesian method for parameters estimation. The model avoids the risk to have no solutions for parameter estimation, which often occurs in the traditional NHMM that uses point estimates of parameters. The Bayesian-NHMM accurately captures seasonality and interannual variability of rainfall amount and wet days during the rainy season. The model establishes a link between large-scale meteorological characteristics and local precipitation patterns. It also provides a more stable and efficient method to estimate parameters...","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":[{"license":null,"accessright":null,"instancetype":null,"hostedby":{"key":"openaire____::1256f046-bf1f-4afc-8b47-d0b147148b18","value":"Unknown Repository","dataInfo":null},"url":["10.3390/w11050916"],"distributionlocation":null,"collectedfrom":null,"dateofacceptance":null,"processingchargeamount":null,"processingchargecurrency":null,"refereed":null}],"journal":null,"originalObjIdentifier":"datacite____::100bb045f34ea2da81433d0b9ae3afa1","dlicollectedfrom":[{"id":"dli_________::datacite","name":"Datasets in Datacite","completionStatus":"complete","collectionMode":null}],"completionStatus":"complete"}
{"collectedfrom":[{"key":"dli_________::datacite","value":"Datasets in Datacite","dataInfo":null}],"dataInfo":{"invisible":false,"inferred":null,"deletedbyinference":false,"trust":"0.9","inferenceprovenance":null,"provenanceaction":null},"lastupdatetimestamp":null,"id":"50|1307198540d2264d839dfd8c9a19f4a7","originalId":["10.3390/w11050916"],"pid":[{"value":"10.3390/w11050916","qualifier":{"classid":"doi","classname":"doi","schemeid":"dnet:pid_types","schemename":"dnet:pid_types"},"dataInfo":null}],"dateofcollection":"2018-10-28T00:39:04.337Z","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":"2019-05-01","qualifier":{"classid":"date","classname":"date","schemeid":"dnet::date","schemename":"dnet::date"},"dataInfo":null}],"description":[{"value":"In terms of climate change and precipitation, there is large interest in how large-scale climatic features affect regional rainfall amount and rainfall occurrence. Large-scale climate elements need to be downscaled to the regional level for hydrologic applications. Here, a new Nonhomogeneous Hidden Markov Model (NHMM) called the Bayesian-NHMM is presented for downscaling and predicting of multisite daily rainfall during rainy season over the Huaihe River Basin (HRB). The Bayesian-NHMM provides a Bayesian method for parameters estimation. The model avoids the risk to have no solutions for parameter estimation, which often occurs in the traditional NHMM that uses point estimates of parameters. The Bayesian-NHMM accurately captures seasonality and interannual variability of rainfall amount and wet days during the rainy season. The model establishes a link between large-scale meteorological characteristics and local precipitation patterns. It also provides a more stable and efficient method to estimate parameters in the model. These results suggest that prediction of daily precipitation could be improved by the suggested new Bayesian-NHMM method, which can be helpful for water resources management and research on climate change.","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":[{"license":null,"accessright":null,"instancetype":null,"hostedby":{"key":"openaire____::1256f046-bf1f-4afc-8b47-d0b147148b18","value":"Unknown Repository","dataInfo":null},"url":["10.3390/w11050916"],"distributionlocation":null,"collectedfrom":null,"dateofacceptance":null,"processingchargeamount":null,"processingchargecurrency":null,"refereed":null}],"journal":null,"originalObjIdentifier":"datacite____::100bb045f34ea2da81433d0b9ae3afa1","dlicollectedfrom":[{"id":"dli_________::datacite","name":"Datasets in Datacite","completionStatus":"complete","collectionMode":null}],"completionStatus":"complete"}
{"collectedfrom":[{"key":"dli_________::datacite","value":"Datasets in Datacite","dataInfo":null}],"dataInfo":{"invisible":false,"inferred":null,"deletedbyinference":false,"trust":"0.9","inferenceprovenance":null,"provenanceaction":null},"lastupdatetimestamp":null,"id":"50|1307198540d2264d839dfd8c9a19f4a7","originalId":["10.3390/w11050916"],"pid":[{"value":"10.3390/w11050916","qualifier":{"classid":"doi","classname":"doi","schemeid":"dnet:pid_types","schemename":"dnet:pid_types"},"dataInfo":null}],"dateofcollection":"2018-10-28T00:39:04.337Z","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":"2019-05-01","qualifier":{"classid":"date","classname":"date","schemeid":"dnet::date","schemename":"dnet::date"},"dataInfo":null}],"description":[{"value":"In terms of climate change and precipitation, there is large interest in how large-scale climatic features affect regional rainfall amount and rainfall occurrence. Large-scale climate elements need to be downscaled to the regional level for hydrologic applications. Here, a new Nonhomogeneous Hidden Markov Model (NHMM) called the Bayesian-NHMM is presented for downscaling and predicting of multisite daily rainfall during rainy season over the Huaihe River Basin (HRB). The Bayesian-NHMM provides a Bayesian method for parameters estimation. The model avoids the risk to have no solutions for parameter estimation, which often occurs in the traditional NHMM that uses point estimates of parameters. The Bayesian-NHMM accurately captures seasonality and interannual variability of rainfall amount and wet days during the rainy season. The model establishes a link between large-scale meteorological characteristics and local precipitation patterns. It also provides a more stable and efficient method to estimate parameters in the model. These results suggest that prediction of daily precipitation could be improved by the suggested new Bayesian-NHMM method, which can be helpful for water resources management and research on climate change.","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":[{"license":null,"accessright":null,"instancetype":null,"hostedby":{"key":"openaire____::1256f046-bf1f-4afc-8b47-d0b147148b18","value":"Unknown Repository","dataInfo":null},"url":["10.3390/w11050916"],"distributionlocation":null,"collectedfrom":null,"dateofacceptance":null,"processingchargeamount":null,"processingchargecurrency":null,"refereed":null}],"journal":null,"originalObjIdentifier":"datacite____::100bb045f34ea2da81433d0b9ae3afa1","dlicollectedfrom":[{"id":"dli_________::datacite","name":"Datasets in Datacite","completionStatus":"complete","collectionMode":null}],"completionStatus":"complete"}
{"collectedfrom":[{"key":"dli_________::datacite","value":"Datasets in Datacite","dataInfo":null}],"dataInfo":{"invisible":false,"inferred":null,"deletedbyinference":false,"trust":"0.9","inferenceprovenance":null,"provenanceaction":null},"lastupdatetimestamp":null,"id":"50|1307198540d2264d839dfd8c9a19f4a7","originalId":["10.3390/w11050916"],"pid":[{"value":"10.3390/w11050916","qualifier":{"classid":"doi","classname":"doi","schemeid":"dnet:pid_types","schemename":"dnet:pid_types"},"dataInfo":null}],"dateofcollection":"2018-10-28T00:39:04.337Z","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":"2019-05-01","qualifier":{"classid":"date","classname":"date","schemeid":"dnet::date","schemename":"dnet::date"},"dataInfo":null}],"description":[{"value":"In terms of climate change and precipitation, there is large interest in how large-scale climatic features affect regional rainfall amount and rainfall occurrence. Large-scale climate elements need to be downscaled to the regional level for hydrologic applications. Here, a new Nonhomogeneous Hidden Markov Model (NHMM) called the Bayesian-NHMM is presented for downscaling and predicting of multisite daily rainfall during rainy season over the Huaihe River Basin (HRB). The Bayesian-NHMM provides a Bayesian method for parameters estimation. The model avoids the risk to have no solutions for parameter estimation, which often occurs in the traditional NHMM that uses point estimates of parameters. The Bayesian-NHMM accurately captures seasonality and interannual variability of rainfall amount and wet days during the rainy season. The model establishes a link between large-scale meteorological characteristics and local precipitation patterns. It also provides a more stable and efficient method to estimate parameters in the model. These results suggest that prediction of daily precipitation could be improved by the suggested new Bayesian-NHMM method, which can be helpful for water resources management and research on climate change.","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":[{"license":null,"accessright":null,"instancetype":null,"hostedby":{"key":"openaire____::1256f046-bf1f-4afc-8b47-d0b147148b18","value":"Unknown Repository","dataInfo":null},"url":["10.3390/w11050916"],"distributionlocation":null,"collectedfrom":null,"dateofacceptance":null,"processingchargeamount":null,"processingchargecurrency":null,"refereed":null}],"journal":null,"originalObjIdentifier":"datacite____::100bb045f34ea2da81433d0b9ae3afa1","dlicollectedfrom":[{"id":"dli_________::datacite","name":"Datasets in Datacite","completionStatus":"complete","collectionMode":null}],"completionStatus":"complete"}
{"collectedfrom":[{"key":"dli_________::datacite","value":"Datasets in Datacite","dataInfo":null}],"dataInfo":{"invisible":false,"inferred":null,"deletedbyinference":false,"trust":"0.9","inferenceprovenance":null,"provenanceaction":null},"lastupdatetimestamp":null,"id":"50|1307198540d2264d839dfd8c9a19f4a7","originalId":["10.3390/w11050916"],"pid":[{"value":"10.3390/w11050916","qualifier":{"classid":"doi","classname":"doi","schemeid":"dnet:pid_types","schemename":"dnet:pid_types"},"dataInfo":null}],"dateofcollection":"2018-10-28T00:39:04.337Z","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":"2019-05-01","qualifier":{"classid":"date","classname":"date","schemeid":"dnet::date","schemename":"dnet::date"},"dataInfo":null}],"description":[{"value":"In terms of climate change and precipitation, there is large interest in how large-scale climatic features affect regional rainfall amount and rainfall occurrence. Large-scale climate elements need to be downscaled to the regional level for hydrologic applications. Here, a new Nonhomogeneous Hidden Markov Model (NHMM) called the Bayesian-NHMM is presented for downscaling and predicting of multisite daily rainfall during rainy season over the Huaihe River Basin (HRB). The Bayesian-NHMM provides a Bayesian method for parameters estimation. The model avoids the risk to have no solutions for parameter estimation, which often occurs in the traditional NHMM that uses point estimates of parameters. The Bayesian-NHMM accurately captures seasonality and interannual variability of rainfall amount and wet days during the rainy season. The model establishes a link between large-scale meteorological characteristics and local precipitation patterns. It also provides a more stable and efficient method to estimate parameters in the model. These results suggest that prediction of daily precipitation could be improved by the suggested new Bayesian-NHMM method, which can be helpful for water resources management and research on climate change.","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":[{"license":null,"accessright":null,"instancetype":null,"hostedby":{"key":"openaire____::1256f046-bf1f-4afc-8b47-d0b147148b18","value":"Unknown Repository","dataInfo":null},"url":["10.3390/w11050916"],"distributionlocation":null,"collectedfrom":null,"dateofacceptance":null,"processingchargeamount":null,"processingchargecurrency":null,"refereed":null}],"journal":null,"originalObjIdentifier":"datacite____::100bb045f34ea2da81433d0b9ae3afa1","dlicollectedfrom":[{"id":"dli_________::datacite","name":"Datasets in Datacite","completionStatus":"complete","collectionMode":null}],"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":"2020-10-04T14:16:06.105Z","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":"2020-09-27T11:39:38.835Z","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":"2020-08-30T11:48:49.809Z","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":"2020-08-14T14:25:55.176Z","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"}

100
dhp-workflows/dhp-graph-provision-scholexplorer/src/main/java/eu/dnetlib/dhp/export/SparkExportContentForOpenAire.scala
View File

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