1PalGov © 2011

أكاديمية الحكومة اإللكترونية الفلسطينيةThe Palestinian eGovernment Academy

www.egovacademy.ps

Tutorial II: Data Integration and Open Information Systems

Session 13.2

GAV and LAV Integration

Dr. Mustafa Jarrar

University of Birzeit

mjarrar@birzeit.edu

www.jarrar.info

2PalGov © 2011

About

This tutorial is part of the PalGov project, funded by the TEMPUS IV program of the

Commission of the European Communities, grant agreement 511159-TEMPUS-1-

2010-1-PS-TEMPUS-JPHES. The project website: www.egovacademy.ps

University of Trento, Italy

University of Namur, Belgium

Vrije Universiteit Brussel, Belgium

TrueTrust, UK

Birzeit University, Palestine

(Coordinator )

Palestine Polytechnic University, Palestine

Palestine Technical University, PalestineUniversité de Savoie, France

Ministry of Local Government, Palestine

Ministry of Telecom and IT, Palestine

Ministry of Interior, Palestine

Project Consortium:

Coordinator:

Dr. Mustafa Jarrar

Birzeit University, P.O.Box 14- Birzeit, Palestine

Telfax:+972 2 2982935 mjarrar@birzeit.edu

3PalGov © 2011

© Copyright Notes

Everyone is encouraged to use this material, or part of it, but should

properly cite the project (logo and website), and the author of that part.

No part of this tutorial may be reproduced or modified in any form or by

any means, without prior written permission from the project, who have

the full copyrights on the material.

Attribution-NonCommercial-ShareAlike

CC-BY-NC-SA

This license lets others remix, tweak, and build upon your work non-

commercially, as long as they credit you and license their new creations

under the identical terms.

PalGov © 2011 4

Tutorial Map

Topic h

Session 1: XML Basics and Namespaces 3

Session 2: XML DTD’s 3

Session 3: XML Schemas 3

Session 4: Lab-XML Schemas 3

Session 5: RDF and RDFs 3

Session 6: Lab-RDF and RDFs 3

Session 7: OWL (Ontology Web Language) 3

Session 8: Lab-OWL 3

Session 9: Lab-RDF Stores -Challenges and Solutions 3

Session 10: Lab-SPARQL 3

Session 11: Lab-Oracle Semantic Technology 3

Session 12_1: The problem of Data Integration 1.5

Session 12_2: Architectural Solutions for the Integration Issues 1.5

Session 13_1: Data Schema Integration 1

Session 13_2: GAV and LAV Integration 1

Session 13_3: Data Integration and Fusion using RDF 1

Session 14: Lab-Data Integration and Fusion using RDF 3

Session 15_1: Data Web and Linked Data 1.5

Session 15_2: RDFa 1.5

Session 16: Lab-RDFa 3

Intended Learning Objectives

A: Knowledge and Understanding

2a1: Describe tree and graph data models.

2a2: Understand the notation of XML, RDF, RDFS, and OWL.

2a3: Demonstrate knowledge about querying techniques for data

models as SPARQL and XPath.

2a4: Explain the concepts of identity management and Linked data.

2a5: Demonstrate knowledge about Integration &fusion of

heterogeneous data.

B: Intellectual Skills

2b1: Represent data using tree and graph data models (XML &

RDF).

2b2: Describe data semantics using RDFS and OWL.

2b3: Manage and query data represented in RDF, XML, OWL.

2b4: Integrate and fuse heterogeneous data.

C: Professional and Practical Skills

2c1: Using Oracle Semantic Technology and/or Virtuoso to store

and query RDF stores.

D: General and Transferable Skills2d1: Working with team.

2d2: Presenting and defending ideas.

2d3: Use of creativity and innovation in problem solving.

2d4: Develop communication skills and logical reasoning abilities.

5PalGov © 2011

Module ILOs

After completing this module students will be able to:

- Understand and apply GAV and LAV integration.

6PalGov © 2011

More about GAV and LAV Integration

Mapping in GAV:

• A GAV mapping is a set of queries on local sources S1, S2,

.., Sn (that contain real data!!), one for each element g of

the global schema.

• Such queries can be expressed in SQL or else in a formal

logic. We will follow the first approach

• g = SQL command (S1, S2, …,Sn)

• This means that the mapping tells us exactly how the

element g is computed from the local sources

7PalGov © 2011

More about GAV and LAV Integration

Mapping in LAV:

• A LAV mapping is a set of queries on the global schema

(that contains virtual data), one for each local source (that

contains real data!!).

• Si = SQL command (GS).

• In LAV, views express how sources contribute to the

global schema (and the related virtual db instance).

8PalGov © 2011

EXAMPLE

Global Schema: GProf (Name, age)

S1 Name Age

Khaled 24

Munir 51

S2 Name Age

Layla 56

Khaled 24

Expected extension

GProf Name Age

Khaled 24

Munir 51

Layla 56

Source S1 contains a first set of

professors

Source S2 contains a second set of

professors

Schema: S1(Name, Age)

Schema: S1(Name, Age)

9PalGov © 2011

CREATE VIEW GProf ASSELECT S1.Name as Name, S1.Age as Age FROM S1UNIONSELECT S2.Name AS Name, S2.Age AS Age FROM S2

The extension of this view is

Let’s define the global schemas as views on data sources

GProf Name Age

Khaled 24

Munir 51

Layla 56

EXAMPLE – GAV Mapping

S1 Name Age

Khaled 24

Munir 51

S2 Name Age

Layla 56

Khaled 24

Expected extension

GProf Name Age

Khaled 24

Munir 51

Layla 56

This view is called

‘EXACT’ because it is

exactly equal to the

expected extension

10PalGov © 2011

CREATE VIEW GProf ASSELECT S1.Name as Name, S1.Age as Age FROM S1UNIONSELECT S2.Name AS Name, S2.Age AS Age FROM S2

S1 Name Age

Khaled 24

Munir 51

S2 Name Age

Layla 56

Khaled 24

Expected extension

GProf Name Age

Khaled 24

Munir 51

Layla 56

We want to query the global schema to extract names of profs that are older than 50 years.

LET’S QUERY!

Select GProf.Name

From GProf

Where Age > 50

EXAMPLE – GAV Mapping

11PalGov © 2011

CREATE VIEW GProf ASSELECT S1.Name as Name, S1.Age as Age FROM S1UNIONSELECT S2.Name AS Name, S2.Age AS Age FROM S2

EXAMPLE – GAV Mapping

S1 Name Age

Khaled 24

Munir 51

S2 Name Age

Layla 56

Khaled 24

Expected extension

GProf Name Age

Khaled 24

Munir 51

Layla 56

TRY TO EXECUTE THE QUERY:

Select GProf.Name

From GProf

Where Age > 50

You should have performed the following process:

Substitution of Gprof with the definition of the viewSelect GProf.Name

From Select S1.Name, S1.Age from S1 Union …

Where Age > 50

12PalGov © 2011

CREATE VIEW GProf ASSELECT S1.Name as Name, S1.Age as Age FROM S1UNIONSELECT S2.Name AS Name, S2.Age AS Age FROM S2

EXAMPLE – GAV Mapping

S1 Name Age

Khaled 24

Munir 51

S2 Name Age

Layla 56

Khaled 24

Expected extension

GProf Name Age

Khaled 24

Munir 51

Layla 56

TRY TO EXECUTE THE QUERY:

Select GProf.Name

From GProf

Where Age > 50

GProf Name Age

Munir 51

Layla 56

Results

13PalGov © 2011

CREATE VIEW GProf ASSELECT S1.Name as Name, S1.Age as Age FROM S1UNIONSELECT S2.Name AS Name, S2.Age AS Age FROM S2

EXAMPLE – GAV Mapping

S1 Name Age

Khaled 24

Munir 51

S2 Name Age

Layla 56

Khaled 24

Expected extension

GProf Name Age

Khaled 24

Munir 51

Layla 56

The query is expressed and executed by the

mediator naturally, since in GAV, to execute

the query we only have to substitute the

references to Gprof in the query with the

mapping of Gprof in terms of local schemas

(this operation is called unfolding).

How is the query executed:

14PalGov © 2011

EXAMPLE – LAV Mapping

S1 Name Age

Khaled 24

Munir 51

S2 Name Age

Layla 56

Khaled 24

Expected extension

GProf Name Age

Khaled 24

Munir 51

Layla 56

Here the mapping describes the

contribution of the local sources to the

expected extension of the global schema

Create View S1 (Name, Age) asSelect GProf.Name as S1.Name, GProf.Age as S1.AgeFrom GProf

S1 (Name, Age)

15PalGov © 2011

EXAMPLE – LAV Mapping

S1 Name Age

Khaled 24

Munir 51

S2 Name Age

Layla 56

Khaled 24

Expected extension

GProf Name Age

Khaled 24

Munir 51

Layla 56

Here the mapping describes the

contribution of the local sources to the

expected extension of the global schema

Create View S1 (Name, Age) asSelect GProf.Name as S1.Name, GProf.Age as S1.AgeFrom GProf

S1 (Name, Age)

Create View S2 (Name,Age) asSelect GProf.Name as S2.Name, GProf.Age as S2.Age

From GProf

S2 (Name, Age)

16PalGov © 2011

EXAMPLE – LAV Mapping

S1 Name Age

Khaled 24

Munir 51

S2 Name Age

Layla 56

Khaled 24

Expected extension

GProf Name Age

Khaled 24

Munir 51

Layla 56

Let’s see the mapping as a query on the

global schema. In this case the mediator in

query execution can’t perform the unfolding

operation since the mapping is in the opposite

direction!!!

So, the mediator has to perfrom a reasoning.

The mediator may adopt a strategy in which,

starting from the definitions of the mappings,

looks for names of professors in both views

and subsequently fuses the results

Query Execution:

17PalGov © 2011

References

• Carlo Batini: Course on Data Integration. BZU IT Summer School

2011.

• Stefano Spaccapietra: Information Integration. Presentation at the IFIP

Academy. Porto Alegre. 2005.

• Chris Bizer: The Emerging Web of Linked Data. Presentation at SRI

International, Artificial Intelligence Center. Menlo Park, USA. 2009.