Provenance management framework
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Provenance Management Framework. Satya S. Sahoo Kno.e.sis Center, Wright State University In Collaboration with Tarleton Lab, University of Georgia and Microsoft Research http://knoesis.wright.edu/research/semsci/application_domain/sem_prov /. Outline. Provenance – Introduction

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Provenance Management Framework

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Provenance management framework

Provenance Management Framework

Satya S. Sahoo

Kno.e.sis Center, Wright State University

In Collaboration with Tarleton Lab, University of Georgia and Microsoft Research

http://knoesis.wright.edu/research/semsci/application_domain/sem_prov/


Outline

Outline

  • Provenance – Introduction

  • Provenance Representation

  • Classification of Provenance Queries & Query Operators

  • Provenance Query Engine

  • T.cruzi SPSE Provenance Management System


Provenance introduction

Provenance – Introduction

Gene Name

  • 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

  • Application of Provenance Metadata beyond verification and validation –eScience Data Management

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


Outline1

Outline

  • Provenance – Introduction

  • Provenance Representation

  • Classification of Provenance Queries & Query Operators

  • Provenance Query Engine

  • T.cruzi SPSE Provenance Management System


Provenir ontology

Provenir ontology

Gene Name

  • A Common Provenance Model defined in OWL-DL – Provenir ontology

  • Provenance Metadata as RDF – allows use of Semantic Web Reasoning Framework

  • A Suite of Domain-specific Provenance ontologies - Provenir as Common Reference Model

  • Three Base Classes – 8 specialized Sub-classes, Eleven Foundational Relations – reuse of Relation Ontology

Sequence

Extraction

contained_in

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


Domain specific provenance parasite experiment ontology

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


Outline2

Outline

  • Provenance – Introduction

  • Provenance Representation

  • Classification of Provenance Queries & Query Operators

  • Provenance Query Engine

  • T.cruzi SPSE Provenance Management System


Provenance query classification

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 = 65?

  • 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


Provenance query operators

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


Outline3

Outline

  • Provenance – Introduction

  • Provenance Representation

  • Classification of Provenance Queries & Query Operators

  • Provenance Query Engine

  • T.cruzi SPSE Provenance Management System


Provenance query engine

Provenance Query Engine

  • Support Provenance Query Operators over a RDF store

  • Provenance Query Engine based on Jena plug-in for Oracle RDF store (support for SPARQL specification)

  • Developed as an API, compatible with any RDF store with support for Rules

  • Maps Query Operators to Domain-specific Provenance ontology – uses RDFS Entailment Rules

  • Query Optimization: Defined a new class of materialized views called Materialized Provenance Views (MPV)

  • MPV defined by Provenir ontology


Outline4

Outline

  • Provenance – Introduction

  • Provenance Representation

  • Classification of Provenance Queries & Query Operators

  • Provenance Query Engine

  • T.cruzi SPSE Provenance Management System


T cruzi spse provenance management system

T.cruzi SPSE Provenance Management System


Conclusions

Conclusions

  • A Common Model of Provenance – Interoperable, Consistent Interpretation and well-defined Semantics

  • Categorization of Provenance Queries – Query Operators

  • Provenance Query Engine

  • Application of Provenance Metadata beyond Verification and Validation – eScience Data Management

PROVENANCE

ALGEBRA

MATERIALIZED

PROVENANCE

VIEW


Acknowledgement

Acknowledgement

  • D. Brent Weatherly – Tarleton Lab, University of Georgia

  • Flora Logan – The Wellcome Trust Sanger Institute

  • Roger Barga– Microsoft Research

  • Jonathan Goldstein – Microsoft Research

  • RaghavaMutharaju– Kno.e.sis Center, Wright State University

  • PramodAnantharam- Kno.e.sis Center, Wright State University


More resources at

More Resources at:

  • Satya S. Sahoo et. al, "Where did you come from...Where did you go?" An Algebra and RDF Query Engine for Provenance, (http://knoesis.wright.edu/library/resource.php?id=00706)

  • Trykipedia: A Wiki-based public resource for Parasite Researchers http://knoesis.wright.edu/trykipedia

  • Provenance Management Framework: http://knoesis.wright.edu/research/semsci/application_domain/sem_prov/

  • T.cruzi Semantic Problem Solving Environment: http://knoesis.wright.edu/research/semsci/application_domain/sem_life_sci/tcruzi_pse/


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