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1 Context-based Exploitation of Data Warehouses Yeow Wei Choong 1 , Dominique Laurent 2 , Arnaud Giacometti 3 , Patrick Marcel 3 , Elsa Negre 3 , Nicolas Spyratos 4 1: HELP University College, Kuala Lumpur, Malaysia 2: ETIS, Université de Cergy-Pontoise, France 3: LI, Université François-Rabelais de Tours, France 4: LRI, Université Paris-Sud, France [email protected]

1 Context-based Exploitation of Data Warehouses Yeow Wei Choong 1, Dominique Laurent 2, Arnaud Giacometti 3, Patrick Marcel 3, Elsa Negre 3, Nicolas Spyratos

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Page 1: 1 Context-based Exploitation of Data Warehouses Yeow Wei Choong 1, Dominique Laurent 2, Arnaud Giacometti 3, Patrick Marcel 3, Elsa Negre 3, Nicolas Spyratos

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Context-based Exploitation of Data Warehouses

Yeow Wei Choong1, Dominique Laurent2, Arnaud Giacometti3, Patrick Marcel3, Elsa Negre3, Nicolas

Spyratos4

1: HELP University College, Kuala Lumpur, Malaysia2: ETIS, Université de Cergy-Pontoise, France

3: LI, Université François-Rabelais de Tours, France4: LRI, Université Paris-Sud, France

[email protected]

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Outline

1) What is the problem?: Motivations and Intuitions

2) How to deal with the problem?: Our model The Data Level The System Level

3) How to solve the problem?: Exploitation of our model

Conclusion and Future work

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Motivations and Intuitions (1)

Problem: How to…

Describe/exploit an analysis in an OLAP context:

Launch/browse queries Organize/reuse/share an analysis Discover authorities, frequently asked

queries Provide recommendations to the user

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Motivations and Intuitions (2)

Motivating example:

2 user-analysts: Elsa Yeow Wei

2 data cubes: Tourism Agriculture

1 base of analyses sessions

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Motivations and Intuitions (3)

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Motivations and Intuitions (4)

The Query over the Context Base

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Motivations and Intuitions (5)

The Navigated Context

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Motivations and Intuitions (6)

The Edited Context

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Outline

1) What is the problem?: Motivations and Intuitions

2) How to deal with the problem?: Our model The Data Level The System Level

3) How to solve the problem?: Exploitation of our model

Conclusion and Future work

Page 10: 1 Context-based Exploitation of Data Warehouses Yeow Wei Choong 1, Dominique Laurent 2, Arnaud Giacometti 3, Patrick Marcel 3, Elsa Negre 3, Nicolas Spyratos

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Our Framework

The Data Level: Based on the model proposed by Theodorakis,

Analyti, Constantopoulos, Spyratos (ER’99, IS 2002)

Uses the relational model under the logic programming perspective

The System Level: Specifies how the data can be browsed and

edited

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Outline

1) What is the problem?: Motivations and Intuitions

2) How to deal with the problem?: Our model The Data Level The System Level

3) How to solve the problem?: Exploitation of our model

Conclusion and Future work

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The Data Level: Model (1) The relations:

Objects: 3-ary relation objects(oid, a, v) objects(2,’topic’,’Transport,Year’)

Contexts: Binary relation contexts(cid,oid) contexts(1,2)

References: 4-ary relation references(oid1,oid2,a, v) references(2,3,’refines’,’Transport’)

2

3

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The Data Level: Model (2) The Context Base (CB):

Schema: Objects, contexts, references Instance: Finite set of facts

Querying the CB: DatalogJ under the stratified semantic to express

recursion and relational division

objectscontexts

references

objects_acontexts_a

references_a

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The Data Level: Language

Example of DatalogJ program: «  Objects with topic dealing with Tourism

but not with Borneo»

objects_a(x,’topic’,z) <- objects(x,’topic’,z), substring(z, ‘Tourism’), ¬substring(z, ‘Borneo’)

objects_a(x,s,t) <- objects(x,s,t), objects_a(x,’topic’,z) contexts_a(c,x) <- objects_a(x,s,t), contexts(c,x) references_a(x,x1,y1,z1) <- objects_a(x,s,t),

references(x,x1,y1,z1)

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Outline

1) What is the problem?: Motivations and Intuitions

2) How to deal with the problem?: Our model The Data Level The System Level

3) How to solve the problem?: Exploitation of our model

Conclusion and Future work

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The System Level: Model System = <Base, State>

Base = instance of CB State = <P, onav, oed>

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The System Level: Language (1)

Navigation system operations

Operators to change the navigated object

Operator to change the set of contexts

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The System Level: Language (2)

Edition system operations

Operators to create a new object

Operators to add a descriptor or a reference

copyObject: Duplicates the navigated object in the edited context

Example

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The System Level: Language (3)

copyObject

Page 20: 1 Context-based Exploitation of Data Warehouses Yeow Wei Choong 1, Dominique Laurent 2, Arnaud Giacometti 3, Patrick Marcel 3, Elsa Negre 3, Nicolas Spyratos

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Outline

1) What is the problem?: Motivations and Intuitions

2) How to deal with the problem?: Our model The Data Level The System Level

3) How to solve the problem?: Exploitation of our model

Conclusion and Future work

Page 21: 1 Context-based Exploitation of Data Warehouses Yeow Wei Choong 1, Dominique Laurent 2, Arnaud Giacometti 3, Patrick Marcel 3, Elsa Negre 3, Nicolas Spyratos

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Exploitation (1)

Exploiting the descriptors: Descriptors associated with Objects:

Topic added and updated Code by the user

Launched added and updated Browsed by the system Result

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Exploitation (2)

Exploiting the descriptors: Descriptors associated with Objects:

Example of use:

What queries have been launched more than 10 times:

objects_a(o1,a,v) <- objects(o1,’launched’,x), x>10, objects(o1,a,v)contexts_a(c,o1) <- objects(o1,’launched’,x), x>10, contexts(c,o1)references_a(o1,o2,a,v) <- objects(o1,’launched’,x), x>10,

references(o1,o2,a,v)

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Exploitation (3)

Exploiting the descriptors: Descriptors associated with

References:

Intra-context references: Order of importance Query containment Query logs

Inter-context references: Comes-from Copied-to

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Exploitation (4) Exploiting the references:

Authority , Hub, Initiator:

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Exploitation (5) Recommendations:

The idea: To exploit particular links between queries

Example: What are the recommendations started from o1 ?

o2o1 o3aComes-from

o3b

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Outline

1) What is the problem?: Motivations and Intuitions

2) How to deal with the problem?: Our model The Data Level The System Level

3) How to solve the problem?: Exploitation of our model

Conclusion and Future work

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Conclusion and Future work (1)

Conclusion:

A model for OLAP analysis: Sharing Browsing OLAP queries Reusing

Data Level: To organize OLAP queries System Level: To represent the interface

and how the user can interact with the system

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Future work: Queries as first class citizen

Extending the manipulation language and the navigation language

Consider other recommendations

Implementing our model

Conclusion and Future work (2)

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Thanks for your attention

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Exploitation Recommendations:

Example: What are the recommendations started from o1 ?

ans(o2) <- objects(o2,_,_), references(o1,o2,’copied-to’,y)objects_a(o3,a,v) <- ans(o2), references(o2,o3,z,t), objects(o3,a,v)contexts_a(c,o3) <- objects_a(o3,a,v), contexts(c,o3)references_a(o3,o4,w,x) <- objects_a(o3,a,v), objects(o4,_,_), references(o3,o4,w,x)

o2o1 o3aComes-from

o3b