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Gagg: A graph Aggregation Operator
This document proposes a new graph aggregation operator called Gagg that can condense a large graph into a smaller structurally similar graph. Gagg operates in two steps, first grouping vertices based on dimensions like subject and object types, and then applying aggregation functions to compute metrics on the groups. The document evaluates an implementation of Gagg in Apache Jena and shows it can summarize graphs more efficiently than alternative approaches like custom SPARQL queries.
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Gagg: A graph Aggregation Operator
- 1. Gagg: A GraphAggregation Operator June 2nd 2015 Fadi Maali*, Stephane Campinas, Stefan Decker ESWC2015 * Funded by the Irish Research Council
- 2. The Famous LODCloud 1/20 http://lod-cloud.net/
- 3. The Famous LODCloud - COLOURED 2/20 http://lod-cloud.net/
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- 5. The Famous LODCloud (from a different Angel) 4/20
- 6. The Famous LODCloud (from a different Angel) 5/20
- 7. The Famous LODCloud (from a different Angel) 6/20
- 8. Graph Aggregation Condenses alarge graph into a structurally similar but smaller graph by collapsing vertices and edges
- 9. Graph Aggregation -Schema Discovery 8/20 Introducing RDF Graph Summary with application to Assisted SPARQL Formulation
- 10. Graph Aggregation -Requirements 9/20 :linkset :dbpedia :bbc-music :crossdomain 23k 1.2b 20m triples triples triples :open-license :media :cc-by-sa :closed-license :bbc-terms subject subject licenselicense subjectsTarget objectsTarget
- 11. Graph Aggregation Methods 1.Custom Code error prone, time, efficiency… 2. SPARQL error prone, time, efficiency… 3. Graph Databases expressivity, optimisation… 4. Gagg, a first-class operator
- 12. Graph Aggregation Methods 1.Custom Code error prone, time, efficiency… 2. SPARQL error prone, time, efficiency… 3. Graph Databases expressivity, optimisation… 4. Gagg, a first-class operator Operational Semantics In-memory evaluation algorithm Experimental evaluation
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- 14. Gagg: Two-steps Aggregation 11/20 ●Relation & measure ● Subject dimension(s) & measure ● Object dimension(s) & measure Uses aggregation functions and a template similar to CONSTRUCT queries
- 15. Graph Aggregation -Requirements 12/20 :linkset :dbpedia :bbc-music :crossdomain 23k 1.2b 20m triples triples triples :open-license :media :cc-by-sa :closed-license :bbc-terms subject subject licenselicense subjectsTarget objectsTarget measure relation
- 16. Graph Aggregation -Requirements 12/20 :linkset :dbpedia :bbc-music :crossdomain 23k 1.2b 20m triples triples triples :open-license :media :cc-by-sa :closed-license :bbc-terms subject subject licenselicense subjectsTarget objectsTarget measure relation ?l a void:LinkSet ; void:subjectsTarget ?s ; void:objectsTarget ?o ; void:triples ?m .
- 17. Graph Aggregation -Requirements 13/20 :linkset :dbpedia :bbc-music :crossdomain 23k 1.2b 20m triples triples triples :open-license :media :cc-by-sa :closed-license :bbc-terms subject subject licenselicense subjectsTarget objectsTarget
- 18. Graph Aggregation -Requirements 13/20 :linkset :dbpedia :bbc-music :crossdomain 23k 1.2b 20m triples triples triples :open-license :media :cc-by-sa :closed-license :bbc-terms subject subject licenselicense subjectsTarget objectsTarget ?s dct:subject ?sd ; void:triple ?sm .
- 19. Graph Aggregation -Requirements 14/20 :linkset :dbpedia :bbc-music :crossdomain 23k 1.2b 20m triples triples triples :open-license :media :cc-by-sa :closed-license :bbc-terms subject subject licenselicense subjectsTarget objectsTarget
- 20. Graph Aggregation -Requirements 14/20 :linkset :dbpedia :bbc-music :crossdomain 23k 1.2b 20m triples triples triples :open-license :media :cc-by-sa :closed-license :bbc-terms subject subject licenselicense subjectsTarget objectsTarget ?o dct:subject ?od ; void:triple ?om .
- 21. Graph Aggregation -Definition 15/20 Q=(D,M,E,N,R,f) D: subject dimensions M: subject measure E: object dimensions N: object measure R: relation query f: reduce function ?x ?sd ?x ?sm ?y ?od ?y ?om ?x ?m?p ?y
- 22. Graph Aggregation -Grouped Graph 16/20 crossdomain media 10k 33k 3k 1.2M ...... linksTo
- 23. Graph Aggregation -Evaluation 17/20 ● Build a binding table ● Build the Grouped Graph O(|B|) algorithm where B is the size of the binding table ● Apply the reduction function ?x ?m?p ?y ?sd ?sm ?od ?om
- 24. Graph Aggregation -Experiment Setup 18/20 ● Extended In-memory Apache Jena using SSE ● BSBM and SP2B ● Type Summary and Bibliometrics
- 25. Graph Aggregation -Experiment Results 19/20 Data size (#triples) fullSPARQL 3SPARQLs reduced Gagg 5k 0.08 0.06 0.01 0.03 190k 9.84 1.25 0.42 0.55 370k 31.88 2.82 1.00 1.13 1.8M 454.07 13.48 4.37 5.61 Type Summary on BSBM data
- 26. Graph aggregation asa first-class operator - Easier for users to express - Easier for engines to support and optimise - Easier for further research and study Further Questions: - Syntax and effect on SPARQL - Distributed implementation Conclusion 20/20
- 27. PREFIX : <http://example.org/> PREFIXrdf:<http://www.w3.org/1999/02/22-rdf-syntax- ns#> CONSTRUCT { _:b0 a ?t1; :count COUNT(?sub.s) . _:b1 a ?t2; :count COUNT(?obj.o). _:b2 a rdf:Statement; rdf:predicate ?p; rdf:subject _:b0; rdf:object _:b1; :count ?prop_count } WHERE { GRAPH_AGGREGATION { ?s ?p ?o {?s a ?t1} GROUP BY ?t1 AS ?sub {?o a ?t2} GROUP BY ?t2 AS ?obj } }
- 28. SELECT ?t1 ?count_s?subId ?t2 ?count_o ?objId ?p (COUNT(*) AS ?rel_count){ { SELECT ?t1 ?count_s ?subId ?t2 ?count_o ?objId ?p { ?s a ?t1 . ?s ?p ?o . ?o a ?t2 . { SELECT ?t1 ?subId (COUNT(DISTINCT ?s) AS ?count_s){ ?s a ?t1 . ?s ?p ?o .?o a ?t2 . BIND (iri(CONCAT(str(?t1), "_s")) AS ?subId) } GROUP BY ?t1 ?subId } { SELECT ?t2 ?objId (COUNT(DISTINCT ?t2) AS ?count_o){ ?s a ?t1 . ?s ?p ?o . ?o a ?t2 . BIND (iri(CONCAT(str(?t2), "_o")) AS ?objId) } GROUP BY ?t2 ?objId } } } } GROUP BY ?t1 ?count_s ?t2 ?count_o ?subId ?objId }