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1 fédération de données et de ConnaissancEs Distribuées en Imagerie BiomédicaLE Data fusion, semantic alignment, distributed queries Johan Montagnat CNRS, I3S lab, Modalis team on behalf of the CrEDIBLE consortium CNRS/UNS, laboratoire I3S (UMR7271), équipe MODALIS INRIA/CNRS/UNS, laboratoire I3S (UMR 7271), équipe Wimmics INSERM U1099, laboratoire LTSI, équipe MediCIS U. Picardie, laboratoire MIS, équipe Connaissances CNRS/INSERM/INSA/U. Lyon 1, laboratoire CREATIS (UMR 5220 / U1044) MI CNRS bilan 2013 Paris, 23/1/2014 1

2 Motivations Biomedical data High heterogeneity: images, clinical data, biomarkers, biology... Increasing amount / number of (open) sources Big Data Large-scale medical studies (statistical medical studies, epidemiology...) Need for cross-factors analysis Linked Data Data (re)analysis opportunities Translational research Centralized approaches encounter limitations Multiple data source kinds Large data volumes to transfer / archive / search Sensitive patient data / complex access control policies Need to adopt uniform data model & format Data is de facto distributed over acquisition centers MI CNRS bilan 2013 Paris, 23/1/2014 2

3 Biomedical data mediation & federation Data federation through distributed querying and query rewriting Client Federator (query decomposition, planning & results federation) Remote sub-queries Query-based access to data Site 1 Mediator Site 2 (ETL or query rewriting) Mediator (ETL or query rewriting) Heterogeneous databases schema mediation Medical data & metadata: raw data + models + processing results + models + provenance... MI CNRS bilan 2013 Paris, 23/1/2014 3

4 Domain ontology-based federation Medical domain ontology (reference model) Data querying Client Query-based access to data Federator Remote sub-queries Data alignment Mediator Mediator Site 1 Site 2 MI CNRS bilan 2013 Paris, 23/1/2014 4

5 Challenges and expertise Challenges Representation of data semantics for heterogeneous data sources biomedical ontology building Data federation distributed query engine Data mediation RDB2RDF, ontology alignment Partnership I3S/Modalis Distributed query engine Semantic representation Semantic reference INRIA/I3S/Wimmics LTSI/MediCIS MIS/Connaissances CREATIS Medical applications MI CNRS bilan 2013 Paris, 23/1/2014 5

6 Scientific networking annual workshop Objectives Multidisciplinary workshop gathering experts in biomedical data representation, semantic, distribution, federation, integration... October 2012: data integration, ontologies, data models, reasoning Semantic models are now widely accepted although ontologies resources are not sufficient Existing systems are mostly centralized but strigent need to support multi-centric studies October 2013: data reuse, ontologies, mediation, federation, graphs Both horizontal and vertical data partition schemes need to be addressed Data models in use are often constructed bottom-up Expressivity of the query language is important MI CNRS bilan 2013 Paris, 23/1/2014 6

7 Bibliography study Distributed (semantic) Query Processing Report CrEDIBLE-12-2, november 2012 A. Gaignard PhD thesis, march 2013, U. Nice Sophia Relational data mediation Report I3S FR, november 2013 Submitted to Journal of Web Semantics Ontology of scientific measures (data acquisition, metrology) Report CrEDIBLE-13-1, december MI CNRS bilan 2013 Paris, 23/1/2014 7

8 Reference ontology 3-levels structure: foundational (DOLCE), core, domain Domain-specific rules Particular Inference abilities DataTop ontology Current focus on measurements Derived relational schema Dataset acquisition equipment Endurant Perdurant Action Centres Dataset Investigators Expression processings Conceptualization Inscription Studies Medical image files Subjects Medical image expressions Languages Medical Image formats Datasets Dataset acquisitions Examinations MI CNRS bilan 2013 Paris, 23/1/2014 8

9 Ontology modules Modularized ontology to improve reuse and lightweightness ONL-MR-DA: MR Dataset Acquisition ONL-DP: Data Processing ONL-MSA: Mental State Assessment OntoVIP: Medical Image Simulation Wide diffusion MI CNRS bilan 2013 Paris, 23/1/2014 9

10 Data query and federation engine KGRAM (Knowledge Graph Abstract Machine) Semantic query engine: Full support of SPARQL1.1 Generic interface for heterogeneous backends Flexible architecture facilitating different deployment scenarios Mediation interface to access relational data Federated relational schema derived from the ontology MI CNRS bilan 2013 Paris, 23/1/

11 Distributed Query Processing Query federator decoupled from data sources Asynchronous querying of multiple data sources Query planning and parallel querying MI CNRS bilan 2013 Paris, 23/1/

12 Distributed Query Processing KGRAM query processing Q SELECT?name?date WHERE {?x foaf:name?name.?x dbpedia:birthdate?date. FILTER (CONTAINS (?name, 'Bob')) } MI CNRS bilan 2013 Paris, 23/1/

13 Distributed Query Processing KGRAM query processing Q SELECT?name?date WHERE {?x foaf:name?name.?x dbpedia:birthdate?date. Q1 FILTER (CONTAINS (?name, 'Bob')) } Q2 MI CNRS bilan 2013 Paris, 23/1/

14 Distributed Query Processing KGRAM query processing Q SELECT?name?date WHERE {?x foaf:name?name.?x dbpedia:birthdate?date. Q1 FILTER (CONTAINS (?name, 'Bob')) } Q2 Asynchronous execution Interface KGRAM core Q #1 #2 MI CNRS bilan 2013 Paris, 23/1/

15 Distributed Query Processing KGRAM query processing Q SELECT?name?date WHERE {?x foaf:name?name.?x dbpedia:birthdate?date. Q1 FILTER (CONTAINS (?name, 'Bob')) } Q2 Asynchronous execution Interface KGRAM core Q #1 #2 MI CNRS bilan 2013 Paris, 23/1/

16 Distributed Query Processing KGRAM query processing Q SELECT?name?date WHERE {?x foaf:name?name.?x dbpedia:birthdate?date. Q1 FILTER (CONTAINS (?name, 'Bob')) } Q2 Asynchronous execution Interface KGRAM core #1 Q Q1 #2 MI CNRS bilan 2013 Paris, 23/1/

17 Distributed Query Processing KGRAM query processing Q SELECT?name?date WHERE {?x foaf:name?name.?x dbpedia:birthdate?date. Q1 FILTER (CONTAINS (?name, 'Bob')) } Q2 Asynchronous execution Interface KGRAM core #1 Q Q1 #2 MI CNRS bilan 2013 Paris, 23/1/

18 Distributed Query Processing KGRAM query processing Q SELECT?name?date WHERE {?x foaf:name?name.?x dbpedia:birthdate?date. Q1 FILTER (CONTAINS (?name, 'Bob')) } Q2 Asynchronous execution Interface KGRAM core Q Q1 Q2' #1 #2 MI CNRS bilan 2013 Paris, 23/1/

19 Distributed Query Processing KGRAM query processing Q SELECT?name?date WHERE {?x foaf:name?name.?x dbpedia:birthdate?date. Q1 FILTER (CONTAINS (?name, 'Bob')) } Q2 Asynchronous execution Interface KGRAM core Q Q1 Q2' #1 #2 MI CNRS bilan 2013 Paris, 23/1/

20 Distributed Query Processing KGRAM query processing Q SELECT?name?date WHERE {?x foaf:name?name.?x dbpedia:birthdate?date. Q1 FILTER (CONTAINS (?name, 'Bob')) } Q2 Asynchronous execution Interface KGRAM core #1 Q Q1 Q2' Q2'' #2 MI CNRS bilan 2013 Paris, 23/1/

21 Distributed Query Processing KGRAM query processing Q SELECT?name?date WHERE {?x foaf:name?name.?x dbpedia:birthdate?date. Q1 FILTER (CONTAINS (?name, 'Bob')) } Q2 Asynchronous execution Interface KGRAM core #1 Q Q1 Q2' Q2'' #2 MI CNRS bilan 2013 Paris, 23/1/

22 Distributed Query Processing KGRAM query processing Q SELECT?name?date WHERE {?x foaf:name?name.?x dbpedia:birthdate?date. Q1 FILTER (CONTAINS (?name, 'Bob')) } Q2 Asynchronous execution Interface KGRAM core #1 Q Q1 Q2' Q2'' #2 MI CNRS bilan 2013 Paris, 23/1/

23 Distributed Query Processing KGRAM query processing Q SELECT?name?date WHERE {?x foaf:name?name.?x dbpedia:birthdate?date. Q1 FILTER (CONTAINS (?name, 'Bob')) } Q2 Asynchronous execution Interface KGRAM core #1 Q Q1 Q2' Q2'' #2 MI CNRS bilan 2013 Paris, 23/1/

24 Performance results Mixed (relational / semantic) stores querying FedBench standard benchmark MI CNRS bilan 2013 Paris, 23/1/

25 Exploitation in ANR GINSENG (epidemiology) Partnership with GINSENG consortium and Mnemotix SME Federation of heterogeneous epidemiology repositories Multiple epidemiology data acquisition networks Cross-correlation with external data (e.g. demographic: IGN) Mediation of the EHR (Electronic Health Record) data schema MI CNRS bilan 2013 Paris, 23/1/

26 Collaboration with Pitié Salpétrière CAC database Neurodegenerative diseases (esp. Alzheimer's) Mediation of relational CAC schema Comparative study of different relational-to-rdf mediation techniques R2RML transformation language Test case for various RDB2RDF conversion tools MI CNRS bilan 2013 Paris, 23/1/

27 Perspectives DataTop ontology development Data distribution (spatial and temporal) and data collections structure Alzheimer disease Query engine architecture Code modularity for alternative optimization strategy implementation Query performance optimization Query plan improvement (especially when dealing both with horizontal and vertical partitioning) Mediation Dynamic mediation of data sources Integration of new medical data sources MI CNRS bilan 2013 Paris, 23/1/

28 Perspectives: collaborations Université de Laval au Québec Ontology design: Alzheimer's disease data ANR CONTINT 2013 BIOMIST Industrial system for large databases management (Product Lifecycle Management - PLM) Application to medical databases in the area of brain functions analysis France Life Imaging excellence infrastructure National-level infrastructure for medical data sharing and analysis MI CNRS bilan 2013 Paris, 23/1/

29 Publications Peer-reviewed Medical domain: idash image informatics workshop, sept. 2012; DCICTIA-MICCAI workshop, oct Distributed Query Processing: Web Intelligence, dec. 2012; Journal of Web Semantics (submitted) Mediation: Journal of Web Semantics (submitted) PhD thesis A. Gaignard (march 2013, U. Nice Sophia Antipolis) N. Cerezo (december 2013, U. Nice Sophia Antipolis) Research reports Workshop summaries: #12-1, #14-1 Annual reports: #12-3, #13-3 Bibliography studies: #12-2, I3S/ FR MI CNRS bilan 2013 Paris, 23/1/

30 Conclusions Query-based data federation grounded on semantic web standards (SPARQL, RDF, RDFS) Emphasis on query language expressivity Support for both horizontal and vertical data partitioning Broad applicability (given that ontologies are available) Ontology-based Reference model for data alignment and query terms Currently using Extract-Transform-Load Static conversion of data sources in RDF format Work on dynamic data mediation on-going Optimization work on-going Flexible software architecture Distributed query processing optimization MI CNRS bilan 2013 Paris, 23/1/

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