Storing, Indexing and Querying Large Provenance Data Sets as RDF Graphs in Apache HBase Artem Chebotko Joint work with John Abraham and Pearl Brazier University of Texas – Pan American Anthony Piazza Piazza Consulting Andrey Kashlev and Shiyong Lu Wayne State University 7th IEEE International Workshop on Scientific Workflows, July 2, 2013 Was Derived From 1

Provenance in eScience  Metadata that captures history of an experiment  Problem diagnosis  Result interpretation  Experiment reproducibility  Scientific Workflow Community Provenance Challenges  2006: understanding and sharing information about provenance representations and capabilities  2006: interoperability of different provenance  2009: evaluating various aspects of OPM  2010: showcase OPM in the context of novel applications  Open Provenance Model ( )  PROV-DM: The PROV Data Model (W3C Recommendation 30 April 2013) 2

SWFMS and Provenance  Taverna  Kepler  View  VisTrails,  Pegasus  Swift  Galaxy  Triana  OPMProv  Karma  RDFProv  etc.  Support provenance collection  Use proprietary or third-party systems to manage provenance  Differ in provenance models, provenance vocabularies, inference support, and query languages.  May eventually converge to W3C PROV specifications 3

Sample OPM Provenance Graph  Nodes:  artifacts  processes  agents  Edges:  used  wasGeneratedBy  wasControlledBy  wasTriggeredBy  wasDerivedFrom 4

Sample Graph Serialization: OPMV and Terse RDF Triple Language utpb:schema rdf:type opmv:Artifact. utpb:instance rdf:type opmv:Artifact. utpb:dataset rdf:type opmv:Artifact. utpb:loadData rdf:type opmv:Process. utpb:loadData opmv:used utpb:schema, utpb:dataset. utpb:instance opmv:wasGeneratedBy utpb:loadData. utpb:instance opmv:wasDerivedFrom utpb:schema, utpb:dataset. 5

Provenance Serialization and Querying  Both OPM and PROV-DM can be serialized in RDF  Queried in SPARQL Find all artifacts and their values, if any, in a provenance graph with identifier 6

This Work - Motivation  Single provenance graph as an RDF graph  In general, readily manageable in main memory of a single machine  Hundreds of thousands or even millions of provenance graphs as a provenance (RDF) dataset  Challenging to manage  Our Focus/Problem: Efficient and scalable storage and querying of large collections of provenance graphs serialized as RDF graphs (in an Apache HBase database) 7

This Work - Contributions  Novel storage and indexing schemes for RDF data in HBase that are suitable for provenance datasets  Novel and efficient querying algorithms to evaluate SPARQL queries in HBase that are optimized to make use of bitmap indices and numeric values instead of triples  Empirical evaluation of our approach using provenance graphs and test queries of the University of Texas Provenance Benchmark (UTPB) 8

Talk Outline  RDF Data and Queries  Indexing Scheme  Storage Scheme  Query Processing  Performance Study  Related Work  Summary and Future work 9

RDF Data and Queries 10

RDF Data and Queries 11

Indexing Scheme  Selection Indices: I s, I p, I o  Find a triple with known s, p and o: 12

Indexing Scheme  Join Indices: I ss, I so, I os, I oo  Find triples with the same object as subject in triple at position i: I so (i) 13

Storage Scheme  One table with two column families for data and indices  Each row stores one complete provenance graph 14

Query Processing  Four efficient algorithms/functions:  application of selection indices  application of join indices  handling of special cases not supported by the indices  basic graph pattern evaluation 15

Query Processing 16

Query Processing 17

Query Processing 18

Query Processing 19

Query Processing 20

Query Processing 21

Performance Study  Implementation  Java, Hadoop 1.0.0, HBase 0.94  Cluster setup  One HBase Master  Eight HBase Region Servers  All commodity machines  Benchmark – UTPB (5 datasets, 11 queries) 22

Performance Study  Q1 – simplest, yet most expensive query due to a large result set  Q1. Find all provenance graph identifiers. PREFIX rdf: PREFIX owl: SELECT * WHERE { ?graph rdf:type owl:Thing. } 23

Performance Study  Q2 – Q11 – different complexity, yet similar performance  Example: Q8. Find all artifacts and their values, if any, in a particular provenance graph. PREFIX opmv: PREFIX rdf: PREFIX opmo: PREFIX utpb: SELECT ?artifact ?value F ROM NAMED WHERE { GRAPH utpb:opmGraph { ?artifact rdf:type opmv:Artifact. OPTIONAL { ?artifact opmo:annotation ?annotation. ?annotation opmo:property ?property. ?property opmo:value ?value. }. OPTIONAL { ?artifact opmo:avalue ?artifactValue. ?artifactValue opmo:content ?value. }. } 24

Performance Study  Please see other queries in the paper – very efficient and scalable (nearly constant scalability due to minimal data transfers and fast index-based join processing) 25

Related Work  HBase, BigTable, Cassandra  Hadoop, Hive, Pig, CouchDB, MongoDB, etc.  NoSQL solutions to RDF data management  Provenance management systems  RDF data indexing 26

Summary and Future Work  Designed novel storage and indexing schemes for RDF data in HBase that are suitable for provenance datasets  Empirical evaluation results are promising  Future work  Compare, compare, compare  More experiments with multi-user workloads  More optimizations  PROV-DM benchmark anyone? 27

THANK YOU! Questions?  My contact information:  Artem Chebotko, Department of Computer Science, University of Texas – Pan American   WasDerivedFrom