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improved documentation in dhp-graph-provision

This commit is contained in:
Claudio Atzori 2020-11-10 11:48:55 +01:00
parent 027ef2326c
commit 18d9aad70c
6 changed files with 29 additions and 95 deletions

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@ -0,0 +1,21 @@
Joins the graph nodes by resolving the links of distance = 1 to create an adjacency list of linked objects. The
operation considers all the entity types (publication, dataset, software, ORP, project, datasource, organization, and
all the possible relationships (similarity links produced by the Dedup process are excluded).
The operation is implemented by sequentially joining one entity type at time (E) with the relationships (R), and
again by E, finally grouped by E.id;
The workflow is organized in different parts aimed to to reduce the complexity of the operation
1) PrepareRelationsJob: only consider relationships that are not virtually deleted ($.dataInfo.deletedbyinference ==
false), each entity can be linked at most to 100 other objects
2) CreateRelatedEntitiesJob: (phase 1): prepare tuples [relation - target entity] (R - T): for each entity type
E_i map E_i as RelatedEntity T_i to simplify the model and extracting only the necessary information join (R.target =
T_i.id) save the tuples (R_i, T_i) (phase 2): create the union of all the entity types E, hash by id read the tuples
(R, T), hash by R.source join E.id = (R, T).source, where E becomes the Source Entity S save the tuples (S, R, T)
3) AdjacencyListBuilderJob: given the tuple (S - R - T) we need to group by S.id -> List [ R - T ], mapping the
result as JoinedEntity
4) XmlConverterJob: convert the JoinedEntities as XML records

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@ -25,26 +25,8 @@ import scala.collection.JavaConverters;
import scala.collection.Seq;
/**
* Joins the graph nodes by resolving the links of distance = 1 to create an adjacency list of linked objects. The
* operation considers all the entity types (publication, dataset, software, ORP, project, datasource, organization, and
* all the possible relationships (similarity links produced by the Dedup process are excluded).
* <p>
* The operation is implemented by sequentially joining one entity type at time (E) with the relationships (R), and
* again by E, finally grouped by E.id;
* <p>
* The workflow is organized in different parts aimed to to reduce the complexity of the operation 1)
* PrepareRelationsJob: only consider relationships that are not virtually deleted ($.dataInfo.deletedbyinference ==
* false), each entity can be linked at most to 100 other objects
* <p>
* 2) JoinRelationEntityByTargetJob: (phase 1): prepare tuples [relation - target entity] (R - T): for each entity type
* E_i map E_i as RelatedEntity T_i to simplify the model and extracting only the necessary information join (R.target =
* T_i.id) save the tuples (R_i, T_i) (phase 2): create the union of all the entity types E, hash by id read the tuples
* (R, T), hash by R.source join E.id = (R, T).source, where E becomes the Source Entity S save the tuples (S, R, T)
* <p>
* 3) AdjacencyListBuilderJob: given the tuple (S - R - T) we need to group by S.id -> List [ R - T ], mapping the
* AdjacencyListBuilderJob: given the tuple (S - R - T) we need to group by S.id -> List [ R - T ], mapping the
* result as JoinedEntity
* <p>
* 4) XmlConverterJob: convert the JoinedEntities as XML records
*/
public class AdjacencyListBuilderJob {

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@ -31,26 +31,9 @@ import eu.dnetlib.dhp.schema.oaf.*;
import scala.Tuple2;
/**
* Joins the graph nodes by resolving the links of distance = 1 to create an adjacency list of linked objects. The
* operation considers all the entity types (publication, dataset, software, ORP, project, datasource, organization, and
* all the possible relationships (similarity links produced by the Dedup process are excluded).
* <p>
* The operation is implemented by sequentially joining one entity type at time (E) with the relationships (R), and
* again by E, finally grouped by E.id;
* <p>
* The workflow is organized in different parts aimed to to reduce the complexity of the operation 1)
* PrepareRelationsJob: only consider relationships that are not virtually deleted ($.dataInfo.deletedbyinference ==
* false), each entity can be linked at most to 100 other objects
* <p>
* 2) JoinRelationEntityByTargetJob: (phase 1): prepare tuples [relation - target entity] (R - T): for each entity type
* E_i map E_i as RelatedEntity T_i to simplify the model and extracting only the necessary information join (R.target =
* T_i.id) save the tuples (R_i, T_i) (phase 2): create the union of all the entity types E, hash by id read the tuples
* (R, T), hash by R.source join E.id = (R, T).source, where E becomes the Source Entity S save the tuples (S, R, T)
* <p>
* 3) AdjacencyListBuilderJob: given the tuple (S - R - T) we need to group by S.id -> List [ R - T ], mapping the
* result as JoinedEntity
* <p>
* 4) XmlConverterJob: convert the JoinedEntities as XML records
* CreateRelatedEntitiesJob: (phase 1): prepare tuples [relation - target entity] (R - T): for each entity type
* E_i map E_i as RelatedEntity T_i to simplify the model and extracting only the necessary information join
* (R.target = T_i.id) save the tuples (R_i, T_i)
*/
public class CreateRelatedEntitiesJob_phase1 {

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@ -34,26 +34,8 @@ import scala.collection.JavaConverters;
import scala.collection.Seq;
/**
* Joins the graph nodes by resolving the links of distance = 1 to create an adjacency list of linked objects. The
* operation considers all the entity types (publication, dataset, software, ORP, project, datasource, organization, and
* all the possible relationships (similarity links produced by the Dedup process are excluded).
* <p>
* The operation is implemented by sequentially joining one entity type at time (E) with the relationships (R), and
* again by E, finally grouped by E.id;
* <p>
* The workflow is organized in different parts aimed to to reduce the complexity of the operation 1)
* PrepareRelationsJob: only consider relationships that are not virtually deleted ($.dataInfo.deletedbyinference ==
* false), each entity can be linked at most to 100 other objects
* <p>
* 2) JoinRelationEntityByTargetJob: (phase 1): prepare tuples [relation - target entity] (R - T): for each entity type
* E_i map E_i as RelatedEntity T_i to simplify the model and extracting only the necessary information join (R.target =
* T_i.id) save the tuples (R_i, T_i) (phase 2): create the union of all the entity types E, hash by id read the tuples
* CreateRelatedEntitiesJob (phase 2): create the union of all the entity types E, hash by id read the tuples
* (R, T), hash by R.source join E.id = (R, T).source, where E becomes the Source Entity S save the tuples (S, R, T)
* <p>
* 3) AdjacencyListBuilderJob: given the tuple (S - R - T) we need to group by S.id -> List [ R - T ], mapping the
* result as JoinedEntity
* <p>
* 4) XmlConverterJob: convert the JoinedEntities as XML records
*/
public class CreateRelatedEntitiesJob_phase2 {

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@ -36,26 +36,8 @@ import eu.dnetlib.dhp.schema.oaf.Relation;
import scala.Tuple2;
/**
* Joins the graph nodes by resolving the links of distance = 1 to create an adjacency list of linked objects. The
* operation considers all the entity types (publication, dataset, software, ORP, project, datasource, organization, and
* all the possible relationships (similarity links produced by the Dedup process are excluded).
* <p>
* The operation is implemented by sequentially joining one entity type at time (E) with the relationships (R), and
* again by E, finally grouped by E.id;
* <p>
* The workflow is organized in different parts aimed to to reduce the complexity of the operation 1)
* PrepareRelationsJob: only consider relationships that are not virtually deleted ($.dataInfo.deletedbyinference ==
* false), each entity can be linked at most to 100 other objects
* <p>
* 2) JoinRelationEntityByTargetJob: (phase 1): prepare tuples [relation - target entity] (R - T): for each entity type
* E_i map E_i as RelatedEntity T_i to simplify the model and extracting only the necessary information join (R.target =
* T_i.id) save the tuples (R_i, T_i) (phase 2): create the union of all the entity types E, hash by id read the tuples
* (R, T), hash by R.source join E.id = (R, T).source, where E becomes the Source Entity S save the tuples (S, R, T)
* <p>
* 3) AdjacencyListBuilderJob: given the tuple (S - R - T) we need to group by S.id -> List [ R - T ], mapping the
* result as JoinedEntity
* <p>
* 4) XmlConverterJob: convert the JoinedEntities as XML records
* PrepareRelationsJob prunes the relationships: only consider relationships that are not virtually deleted
* ($.dataInfo.deletedbyinference == false), each entity can be linked at most to 100 other objects
*/
public class PrepareRelationsJob {

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@ -37,23 +37,7 @@ import scala.collection.JavaConverters;
import scala.collection.Seq;
/**
* Joins the graph nodes by resolving the links of distance = 1 to create an adjacency list of linked objects. The
* operation considers all the entity types (publication, dataset, software, ORP, project, datasource, organization, and
* all the possible relationships (similarity links produced by the Dedup process are excluded).
* <p>
* The workflow is organized in different parts aimed to to reduce the complexity of the operation 1)
* PrepareRelationsJob: only consider relationships that are not virtually deleted ($.dataInfo.deletedbyinference ==
* false), each entity can be linked at most to 100 other objects
* <p>
* 2) JoinRelationEntityByTargetJob: (phase 1): prepare tuples [relation - target entity] (R - T): for each entity type
* E_i map E_i as RelatedEntity T_i to simplify the model and extracting only the necessary information join (R.target =
* T_i.id) save the tuples (R_i, T_i) (phase 2): create the union of all the entity types E, hash by id read the tuples
* (R, T), hash by R.source join E.id = (R, T).source, where E becomes the Source Entity S save the tuples (S, R, T)
* <p>
* 3) AdjacencyListBuilderJob: given the tuple (S - R - T) we need to group by S.id -> List [ R - T ], mapping the
* result as JoinedEntity
* <p>
* 4) XmlConverterJob: convert the JoinedEntities as XML records
* XmlConverterJob converts the JoinedEntities as XML records
*/
public class XmlConverterJob {