1. Cooperative Answering Systems in Big Data BIG DATA – 2014, Chasseneuil, France Géraud FOKOU, Stéphane JEAN, Allel HADJALI LIAS/ENSMA-University of Poitiers, FRANCE

2. 2 BIG DATA 19 -21 November 2014, Chasseneuil, FRANCE BIG DATA CONTEXT  Increase of Data Production o Sensoring Data, E.Business, Social Network  Diversification of Data Structuration o Unstrutured, semi-structured, Structured data  Distribution of data through multiple and distinct data sources

3. 3 BIG DATA 19 -21 November 2014, Chasseneuil, FRANCE BIG DATA RETRIEVING  From 4-V to 5-V in Big Data: Visualisation o Retrieving, querying Big Data Objectives Efficiency : Speed of Process Effectiveness: Answers Quality Big dataBig answers set Plethoric Answers Problem: Big dataEmpty answers set Empty Answer Problem:

4. 4 BIG DATA 19 -21 November 2014, Chasseneuil, FRANCE CONTEXT AND PROBLEMATIC  Context  Structuration : Semantic Data Data Format : RDFS, OWL, N3,… Physical represenation Storage : Triplet or Vertical, Horizontal and Binaire. Query language : SQL, SPARQL and Hybrid Language  Problematic  Empty Answers Set: Return Alternative Answers L 1 : Lack of relaxation control → O 1 : Definition of relaxation operators with control parameters L 2 : Instance-independent ranking → O 2 : Our ranking function depends both on instances and queries L 3 : Integration in query language → O 3 : A SPARQL extension implemented on top of Jena

5. 5 BIG DATA 19 -21 November 2014, Chasseneuil, FRANCE CONTRIBUTIONS

6. 6 BIG DATA 19 -21 November 2014, Chasseneuil, FRANCE Contributions: Relaxation Operators  Relaxation Operators  Based on Relation between Data Order Relation (Order in Integer Set) Conceptual relation (Generalization)  Similarity between query Based on value distance Based on Conceptual/Structural distance  Operators Proposed Clause de Relaxation: APPROX(OP, TopK) Relaxation de prédicat : PRED(Q, Prop, epsilon) Généralisation: GEN(Q, C, level) Substitution: SIB(Q,C,[C1, C2,…, Cn]) Agrégation of operators : AND Select ?Title Where {(?movie rdf:type Drama). (?movie mo:Title ?Title). (?movie mo:start 4)} APPROX { GEN (Drama, 1) AND (PRED (Start, δ)}

7. 7 BIG DATA 19 -21 November 2014, Chasseneuil, FRANCE Contributions: Data Distance  Data Distance Ranking Relaxed Queries and alterntives answers

8. 8 BIG DATA 19 -21 November 2014, Chasseneuil, FRANCE Contributions: Relaxation Strategies  Relaxation Strategies  Using MFS (Minimal Failing Subqueries) Query as conjunction of criteria Finding all the minimal conjunction of criteria which return an empty answers set  Interactive Relaxation User based strategy Return advice for refining query or most similar answers Ask the queries refined  Using XSS ( maXimal Success Subqueries) Query as conjunction of criteria Finding all the maximal conjunction of criteria which not return an empty answers set  Automatic Relaxation Base on the similarity and the distance Finding all relaxed queries more similar than the original query Find the nearest answers to the abstract model answer wanted

9. 9 BIG DATA 19 -21 November 2014, Chasseneuil, FRANCE Perspectives  Performance  Optimization of the relaxation process by using the database statistics to find the optimal step of relaxation: Selectivity  Multiple-query optimization by using the similarity between the original query and the relaxed queries  Optimization of the relaxation process to quickly find a set of alternative answers  User-aware relaxation process  Leveraging user profiles/preferences to customize the relaxation process

10. 10 BIG DATA 19 -21 November 2014, Chasseneuil, FRANCE Publications and References  Géraud FOKOU, Un Framework pour la relaxation des requêtes dans les bases de données du Web Sémantique, Actes VII ièmes Forum Jeunes Chercheurs, XXXII ièmes Congrès INFORSID 2014 (FJC-INFORSID 2014)  Géraud FOKOU, Stéphane JEAN, Allel HADJALI, Endowing Semantic Query Languages with Advanced Relaxation Capabilities, Proceedings of the 21st International Symposium on Methodologies for Intelligent Systems (ISMIS 2014), 2014  Stéphane JEAN, Allel HADJALI, Ammar M., Towards a Cooperative Query Language for Semantic Web Database Queries,On the Move to Meaningful Internet Systems : OTM 2013 Conferences, Springer Berlin Heidelberg, September  Corby O., Dieng-Kuntz R., Faron-Zucker C., Gandon F. L., Searching the Semantic Web : Approximate Query Processing Based on Ontologies, IEEE Intelligent Systems, 2006.  Godfrey P., Minimization in cooperative response to failing database queries, IJCIS, 1997.  Hogan A., Mellotte M., Powell G., Stampouli D., Towards Fuzzy Query-Relaxation for RDF, ESWC’12, 2012.  Huang H., Liu C., Zhou X., Approximating query answering on RDF databases, Journal of World Wide Web, 2012.  Hurtado C. A., Poulovassilis A., Wood P. T., Query Relaxation in RDF, JODS, 2008.  Poulovassilis A., Wood P. T., Combining Approximation and Relaxation in Semantic Web Path Queries, Proceedings of the 9th International Semantic Web Conference (ISWC’10), 2010.  Hai Huang, Chengfei Liu, and Xiaofang Zhou. Approximating query answering on rdf databases. World Wide Web, January 2012.  Islam M. S., Liu C., Zhou R., On Modeling Query Refinement by Capturing User Intent Through Feedback, Proceedings of the Twenty- Third Australasian Database Conference - Volume 124, ADC ’12, Australian Computer Society, Inc., Darlinghurst, Australia, Australia, 2012.  Jannach D., Finding Preferred Query Relaxations in Content-Based Recommenders, Intelligent Techniques and Tools for Novel System Architectures, vol. 109, Springer Berlin, Heidelberg, p. 81-97, September.

11. 11 BIG DATA 19 -21 November 2014, Chasseneuil, FRANCE Thank you for your attention … Web site : http://www.lias-lab.fr