A preliminary approach on ontologybased visual query formulation for big data - MTSR 2013

1. A preliminary approach on ontologybased visual query formulation for
big data
Ahmet Soylu
University of Oslo
MTSR 2013
Thursday, 21 November 2013

2. Outline
! Introduction
! Background
! Challenges and Requirements
!
Optique Approach
! Discussion and Outlook
! The Big Picture

3. Introduction
IT expert
Domain expert

4. Introduction
Query formulation bottleneck

5. Introduction
Simple'
Case'
'
'
'
Complex'
Case'
'
'
'
Op.que'
Solu.on'
'
'
predefined*queries*
Applica'on*
limited*
End-user*
possible*
mismatch*
informa'on*need*
informal*
specialized*query*
IT*expert*
End-user*
Op'que*
End-user*
uniform**
sources*
Applica'on*
ontology-based*queries*
flexible*
Query*
Transla'on*
translated*queries*
op'mised*
disparate**
sources*
disparate**
sources*

6. Introduction
Query formulation and query evaluation/answering
(Laney, 2001)

7. Introduction
Optique: scalability
Simple'
Case'
'
'
'
Complex'
Case'
'
'
'
Op.que'
Solu.on'
'
'
predefined*queries*
Applica'on*
limited*
End-user*
possible*
mismatch*
Queryinforma'on*need* informal*
formulation
specialized*query*
IT*expert*
End-user*
Op'que*
End-user*
uniform**
sources*
Applica'on*
ontology-based*queries*
flexible*
Query*
Transla'on*
translated*queries*
op'mised*
disparate**
sources*
disparate**
sources*
Query evaluation (answering)

8. Background
Visual Query Systems and Languages
Direct manipulation (Shneiderman, 1983)
(Catarci, 1997; Epstein, 1991)

9. Background
! Early approaches: database schema, object-oriented
models etc. (e.g., QBE, QBD*, TableTalk etc. )
! Unnatural: flattening & scattering (i.e., normalization/join)
! Ontology-based approaches
(e.g., Catarci, 2004; Barzdins, 2009)
! Natural: knowledge representation & reasoning
! Current work suffer from lack of ontology-based data
access (OBDA) frameworks and remain at experimental
stages

10. Background
Visual Query Systems +
Ontology-based Data Access (OBDA)
query$transforma5on$
Visual&Query&System&
Q$
SPARQL$
End>user&
User'
System'
Explore&
Construct&
Expressivity&
Usability&
REWRITE&
QI$
SPARQL$
REWRITE&
(cf. Rodriguez-Muro, 2012; Kogalovsky, 2012)
disparate&sources&
QII$
SQL$
Energy&Tribunes&
Siemens'
(GBs/day)&
RDBMS&
(TBs)&
Drilling&FaciliEes&
Ontology&
(OWL)&
mappings&
Statoil'
(GBs/day)&
IT&expert&

11. Challenges and Requirements
! Two main pillars:
! Expressiveness
! Usability: effectiveness, efficiency, user satisfaction
! Main data access activities:
! Exploration (i.e., understanding the reality of interest)
! Construction (i.e., formulation)

12. Challenges and Requirements
! Expressiveness: end-user perspective
! What domain constructs to communicate? (e.g., subclass,
disjoint classes etc.)
! What query constructs types to express? (e.g., topological
and non-topological)
! Usability: discern, comprehend, and communicate
! What representation paradigms, interaction styles and visual
attributes?
! How to avoid large and incomprehensible views?
! How to orient user in a large conceptual space?
! How to alleviate Big Data affect?

13. Optique approach

14. Optique approach

15. Optique approach: architecture
! Widget-based mashup: flexible and extensible

16. Optique approach: design
! A visual query system
! Multi-paradigm
! Diagram, list, form etc.
! Query by Navigation, range selection etc.
! View and Overview
! Faceted search: Amazon, eBay etc.
! data intensive
! hard to join concepts
! good at selection and projection
! Navigational approach: the Web
! hard to do selection and projection
! good at join

17. Discussion and Outlook
! Expressiveness
! categories of queries,
! 1st level: linear and tree-shaped conjunctive queries
! 2nd level: disjunctive queries, cyclic queries, and aggregation
! 3rd level: negation, aggregation, and universal quantifiers
! A layered/spiral approach
! A VQS is likely to be less expressive than the underlying
formal textual language

18. Discussion and Outlook
! Usability
! Interactive visualizations
! Gradual and iterative (e.g., node retraction and expansion)
! Collaborative Query formulation and query reuse
! Big Data effect:
! Adaptation and recommendations
! Schema clustering and summarization
! Widgets for context-tailored representations
! Reactive Scenarios

19. The Big Picture
Ontology and mapping management
Time and streams
Query transformation (incl. optimization)
Distributed query execution (incl. parallelization)
IT*
Expert*
Query*
Formula'on*
query*
Applica'on*
results*
End=user*
!
!
!
!
Ontology*&*Mapping*
Management*
Ontology*
Mappings*
Query*Transforma'on*
Query*Planning*
Stream*adapter*
streaming*data*
*
Site*A*
Query*Execu'on*
.*.*.*
Site*B*
.*.*.*
Query*Execu'on*
.*.*.*
Site*C*

20. Q&A
Thanks!
www.optique-project.eu
www.ahmetsoylu.com