1 context-based exploitation of data warehouses yeow wei choong 1, dominique laurent 2, arnaud...
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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
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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
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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
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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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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