A preliminary approach on ontologybased visual query formulation for
big data
Ahmet Soylu
University of Oslo
MTSR 2013
Thu...
Outline
!   Introduction
!   Background
!   Challenges and Requirements
!

Optique Approach

!   Discussion and Outlook
! ...
Introduction

IT expert

Domain expert
Introduction

Query formulation bottleneck
Introduction

Simple'
Case'
'
'
'
Complex'
Case'
'
'
'
Op.que'
Solu.on'
'
'

predefined*queries*

Applica'on*

limited*

En...
Introduction

Query formulation and query evaluation/answering

(Laney, 2001)
Introduction
Optique: scalability
Simple'
Case'
'
'
'
Complex'
Case'
'
'
'
Op.que'
Solu.on'
'
'

predefined*queries*

Appli...
Background
Visual Query Systems and Languages
Direct manipulation (Shneiderman, 1983)

(Catarci, 1997; Epstein, 1991)
Background

!   Early approaches: database schema, object-oriented
models etc. (e.g., QBE, QBD*, TableTalk etc. )
!   Unna...
Background
Visual Query Systems +
Ontology-based Data Access (OBDA)

query$transforma5on$

Visual&Query&System&

Q$
SPARQL...
Challenges and Requirements

!   Two main pillars:
!   Expressiveness
!   Usability: effectiveness, efficiency, user satis...
Challenges and Requirements
!   Expressiveness: end-user perspective
!   What domain constructs to communicate? (e.g., sub...
Optique approach
Optique approach
Optique approach: architecture
!   Widget-based mashup: flexible and extensible
Optique approach: design
!   A visual query system
!   Multi-paradigm
!   Diagram, list, form etc.
!   Query by Navigation...
Discussion and Outlook

!   Expressiveness
!   categories of queries,
!   1st level: linear and tree-shaped conjunctive qu...
Discussion and Outlook

!   Usability
!   Interactive visualizations
!   Gradual and iterative (e.g., node retraction and ...
The Big Picture
Ontology and mapping management
Time and streams
Query transformation (incl. optimization)
Distributed que...
Q&A

Thanks!
www.optique-project.eu
www.ahmetsoylu.com
Upcoming SlideShare
Loading in …5
×

A preliminary approach on ontologybased visual query formulation for big data

438 views

Published on

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

Published in: Education, Technology
1 Comment
0 Likes
Statistics
Notes
  • thanks for sharing..As i said to you the presentation was wonderfull and very inlightening..Keep up the good work.

    cheers

    Evangelia
       Reply 
    Are you sure you want to  Yes  No
    Your message goes here
  • Be the first to like this

No Downloads
Views
Total views
438
On SlideShare
0
From Embeds
0
Number of Embeds
6
Actions
Shares
0
Downloads
8
Comments
1
Likes
0
Embeds 0
No embeds

No notes for slide

A preliminary approach on ontologybased visual query formulation for big data

  1. 1. A preliminary approach on ontologybased visual query formulation for big data Ahmet Soylu University of Oslo MTSR 2013 Thursday, 21 November 2013
  2. 2. Outline !   Introduction !   Background !   Challenges and Requirements ! Optique Approach !   Discussion and Outlook !   The Big Picture
  3. 3. Introduction IT expert Domain expert
  4. 4. Introduction Query formulation bottleneck
  5. 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. 6. Introduction Query formulation and query evaluation/answering (Laney, 2001)
  7. 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. 8. Background Visual Query Systems and Languages Direct manipulation (Shneiderman, 1983) (Catarci, 1997; Epstein, 1991)
  9. 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. 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. 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. 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. 13. Optique approach
  14. 14. Optique approach
  15. 15. Optique approach: architecture !   Widget-based mashup: flexible and extensible
  16. 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. 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. 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. 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. 20. Q&A Thanks! www.optique-project.eu www.ahmetsoylu.com

×