A preliminary approach on ontology-based visual query formulation for

big data

Ahmet Soylu University of Oslo

MTSR 2013

Thursday, 21 November 2013

Outline

!   Introduction

!   Background

!   Challenges and Requirements

! Optique Approach

!   Discussion and Outlook

!   The Big Picture

Introduction

IT expert Domain expert

Introduction

Query formulation bottleneck

Introduction

Simple'Case'

'''

Complex'Case'

'''

Op.que'Solu.on'

''

Applica'on*

End-user*

End-user*

End-user*

predefined*queries*

informa'on*need* specialized*query*

Applica'on*

Op'que* ontology-based*queries* Query*Transla'on*

translated*queries*

uniform**sources*

disparate**sources*

disparate**sources*

IT*expert*

informal*

limited*

possible*mismatch*

flexible* op'mised*

Introduction

(Laney, 2001)

Query formulation and query evaluation/answering

Introduction

Simple'Case'

'''

Complex'Case'

'''

Op.que'Solu.on'

''

Applica'on*

End-user*

End-user*

End-user*

predefined*queries*

informa'on*need* specialized*query*

Applica'on*

Op'que* ontology-based*queries* Query*Transla'on*

translated*queries*

uniform**sources*

disparate**sources*

disparate**sources*

IT*expert*

informal*

limited*

possible*mismatch*

flexible* op'mised*

Optique: scalability

Query formulation

Query evaluation (answering)

Background

Visual Query Systems and Languages (Catarci, 1997; Epstein, 1991)

Direct manipulation (Shneiderman, 1983)

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

Background

SQL$REWRITE& REWRITE&

Ontology&(OWL)& mappings&

Q$ QI$

disparate&sources&Visual&Query&System&

End>user&SPARQL$ SPARQL$

QII$

query$transforma5on$

Siemens'(GBs/day)&

Statoil'(GBs/day)&

Energy&Tribunes&

Drilling&FaciliEes&

Expressivity&Usability&

User'

System'

Explore&Construct&

IT&expert&

RDBMS&(TBs)&

Visual Query Systems + Ontology-based Data Access (OBDA) (cf. Rodriguez-Muro, 2012; Kogalovsky, 2012)

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)

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?

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, 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

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

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

The Big Picture

!   Ontology and mapping management !   Time and streams !   Query transformation (incl. optimization) !   Distributed query execution (incl. parallelization)

Applica'on* Query*Formula'on*

Ontology*&*Mapping*Management*

Query*Transforma'on*

Mappings*Ontology*

Query*Planning*

results*

End=user*

IT*

Expe

rt*

query*

Stream*adapter* Query*Execu'on* Query*Execu'on*

.*.*.*Site*B*

.*.*.*Site*C*

*Site*A*

.*.*.*

streaming*data*

Q&A

Thanks!

www.optique-project.eu www.ahmetsoylu.com