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Ontology-driven Provenance Management in eScience: An Application in Parasite Research

Ontology-driven Provenance Management in eScience: An Application in Parasite Research. Satya S. Sahoo 1 , D. Brent Weatherly 2 , Raghava Mutharaju 1 , Pramod Anantharam 1 , Amit Sheth 1 , Rick L. Tarleton 2. 1 Kno.e.sis Center, Wright State University;

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Ontology-driven Provenance Management in eScience: An Application in Parasite Research

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  1. Ontology-driven Provenance Management in eScience: An Application in Parasite Research Satya S. Sahoo1, D. Brent Weatherly2, Raghava Mutharaju1, Pramod Anantharam1, Amit Sheth1, Rick L. Tarleton2 1Kno.e.sis Center, Wright State University; 2 Center for Tropical and Emerging Diseases, University of Georgia ODBASE2009 Vilamoura, Algarve-Portugal November 05, 2009

  2. Parasite Research: Creation of Parasite Strains New Parasite Strains

  3. Provenance in Parasite Research Gene Name Related Queries from Biologists • Q2: List all groups in the lab that used a Target Region Plasmid? • Q3: Which researcher created a new strain of the parasite (with ID = 66)? • An experiment was not successful – has this experiment been conducted earlier? What were the results? Gene Knockout and Strain Creation* Sequence Extraction 3‘ & 5’ Region Drug Resistant Plasmid Gene Name Plasmid Construction Knockout Construct Plasmid T.Cruzi sample ? Transfection Transfected Sample Drug Selection Cloned Sample Selected Sample Cell Cloning Cloned Sample *T.cruzi Semantic Problem Solving Environment Project, Courtesy of D.B. Weatherly and Flora Logan, Tarleton Lab, University of Georgia

  4. Provenance Management in Science • Provenance from the French word “provenir” describes the lineage or history of a data entity • For Verification and Validation of Data Integrity, Process Quality, and Trust • Issues in Provenance Management • Provenance Modeling • A Dedicated Query Infrastructure • Practical Provenance Management Systems

  5. Outline • Provenance Modeling: Provenir →Parasite Experiment ontology • Provenance Query Infrastructure • Provenance Query Engine • Evaluation Results • Query Optimization: Materialized Provenance Views

  6. Ontologies for Provenance Modeling • Advantages of using Ontologies • Formal Description: Machine Readability, Consistent Interpretation • Use Reasoning: Knowledge Discovery over Large Datasets • Problem: A gigantic, monolithic Provenance Ontology! – not feasible • Solution: Modular Approach using a Foundational Ontology FOUNDATIONAL ONTOLOGY PARASITE EXPERIMENT GLYCOPROTEIN EXPERIMENT OCEANOGRAPHY

  7. Provenir Ontology Gene Name Sequence Extraction 3‘ & 5’ Region Drug Resistant Plasmid AGENT Plasmid Construction Knockout Construct Plasmid T.Cruzi sample has_agent Transfection Transfection Machine DATA Transfected Sample Drug Selection participates_in Selected Sample PROCESS Cell Cloning Cloned Sample

  8. Provenir Ontology Schema SPATIAL THEMATIC TEMPORAL is_a is_a is_a located_in PARAMETER DATA COLLECTION is_a is_a AGENT has_temporal_value DATA participates_in has_agent PROCESS preceded_by

  9. Domain-specific Provenance: Parasite Experiment ontology PROVENIR ONTOLOGY agent has_agent is_a is_a data parameter has_participant is_a data_collection is_a process is_a spatial_parameter temporal_parameter domain_parameter is_a is_a is_a is_a is_a is_a transfection_machine location is_a drug_selection is_a is_a sample has_participant Time:DateTimeDescritption transfection cell_cloning is_a transfection_buffer strain_creation_ protocol Tcruzi_sample PARASITE EXPERIMENT ONTOLOGY has_parameter *Parasite Experiment ontology available at: http://wiki.knoesis.org/index.php/Trykipedia

  10. Outline • Provenance Modeling: Provenir →Parasite Experiment ontology • Provenance Query Infrastructure • Provenance Query Engine • Evaluation Results • Query Optimization: Materialized Provenance Views

  11. Provenance Query Classification Classified Provenance Queries into Three Categories • Type 1: Querying for Provenance Metadata • Example: Which gene was used create the cloned sample with ID = 66? • Type 2: Querying for Specific Data Set • Example: Find all knockout construct plasmids created by researcher Michelle using “Hygromycin” drug resistant plasmid betweenApril 25, 2008 and August 15, 2008 • Type 3: Operations on Provenance Metadata • Example: Were the two cloned samples 65 and 46 prepared under similar conditions – compare the associated provenance information

  12. Provenance Query Operators Four Query Operators – based on Query Classification • provenance () – Closure operation, returns the complete set of provenance metadata for input data entity • provenance_context() - Given set of constraints defined on provenance, retrieves datasets that satisfy constraints • provenance_compare () - adapt the RDF graph equivalence definition • provenance_merge () - Two sets of provenance information are combined using the RDF graph merge

  13. Answering Provenance Queries using provenance () Operator

  14. Outline • Provenance Modeling: Provenir →Parasite Experiment ontology • Provenance Query Infrastructure • Provenance Query Engine • Evaluation Results • Query Optimization: Materialized Provenance Views

  15. Provenance Query Engine • Available as API for integration with provenance management systems • Layer on top of a RDF Data Store (Oracle 10g), requires support for: • Rule-based reasoning • SPARQL query execution • Input: • Type of provenance query operator : provenance () • Input value to query operator: cloned sample 66 • User details to connect to underlying RDF store

  16. Outline • Provenance Modeling: Provenir →Parasite Experiment ontology • Provenance Query Infrastructure • Provenance Query Engine • Evaluation Results • Query Optimization: Materialized Provenance Views

  17. Evaluation Results • Queries expressed in SPARQL • Datasets using real experiment data

  18. Evaluation Results

  19. Outline • Provenance Modeling: Provenir →Parasite Experiment ontology • Provenance Query Infrastructure • Provenance Query Engine • Evaluation Results • Query Optimization: Materialized Provenance Views

  20. Query Optimization: Materialized Provenance Views • Materializes a single logical unit of provenance • Does not require query-rewriting • View updates: addressed by characteristics of provenance • Created using a memoization approach

  21. Provenance Query Engine Architecture QUERY OPTIMIZER TRANSITIVE CLOSURE

  22. Evaluation Results using Materialized Provenance Views

  23. Provenance Management System for Parasite Research

  24. Acknowledgement • Flora Logan – The Wellcome Trust Sanger Institute, Cambridge, UK • Priti Parikh – Kno.e.sis Center, Wright State University • Roger Barga– Microsoft Research, Redmond • Jonathan Goldstein – Microsoft Research, Redmond

  25. Thank you Contact email: satyasahoo@gmail.com Web: http://knoesis.wright.edu/researchers/satya/

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