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iTrails: Pay-as-you-go Information Integration in Dataspaces. Presented By Marcos Vaz Salles, Jens Dittrich, Shant Karakashian, Olivier Girard, Lukas Blunschi ETH Zurich 2008-02-22 Summerized By Sungchan Park. Problem: Querying Several Sources. Solution #1: Use a Search Engine. - PowerPoint PPT Presentation
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iTrails: Pay-as-you-go Information Integration in iTrails: Pay-as-you-go Information Integration in DataspacesDataspaces
Presented By Marcos Vaz Salles, Jens Dittrich, Shant Karakashian, Olivier Girard, Lukas Blunschi
ETH Zurich
2008-02-22
Summerized By Sungchan Park
Copyright 2008 by CEBT
Problem: Querying Several SourcesProblem: Querying Several Sources
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Copyright 2008 by CEBT
Solution #1: Use a Search EngineSolution #1: Use a Search Engine
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Solution #2: Use an Information Integration Solution #2: Use an Information Integration SystemSystem
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iTrail Core IdeaiTrail Core Idea
Is there an integration solution in-between these two extremes?
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Copyright 2008 by CEBT
iTrail Core IdeaiTrail Core Idea
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Is there an integration solution in-between these two extremes?
Declaratively add lightweight ‘hints’ to a search engine thus allowing gradual enrichment of loosely integrated data sources
Copyright 2008 by CEBT
Example ScenarioExample Scenario
Query
“pdf yesterday”
Hints(Trails)
1. The date attribute is mapped to modified attribute
2. The date attribute is mapped to received attribute
3. The yesterday keyword is mapped to a query for values of the date attribute equal to the date of yesterday
4. The pdf keyword is mapped to a query for elements whose names end in pdf
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Where hints come from?Where hints come from?
Given by the user
Explicitly
Via Relevance Feedback
(Semi-)Automatically
Information extraction techniques
Automatic schema matching
Ontologies and thesauri (e.g., wordnet)
User communities (e.g., trails on gene data, bookmarks)
All these aspects are beyond the scope of this paper
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Data and Query ModelData and Query Model
Data Model
Assume that all data is represented by a logical graph G
Query also represented by graph
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Query SyntaxQuery Syntax
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Query ExampleQuery Example
“//Home/projects//*[“Mike”]”
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Basic Form of a TrailBasic Form of a Trail
An unidirectional trail
An bidirectional trail
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Trail ExampleTrail Example
Trails in an example scenario
Trails
Given query
– “pdf yesterday”
Transformed query
– “//*.pdf[modified=yesterday() OR received=yesterday() ].”
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iTrail Query ProcessingiTrail Query Processing
1. Matching
2. Transforming
3. Merging
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iTrail Query Processing ExampleiTrail Query Processing Example
Given Query
Q1 = //home/projects//* [“Mike”]
Trail
Ψ8 := //home/*.name ->
//calendar//*.tuple.category
Resulting Query
Q1{Ψ8} = //home/projects/*[“Mike”] U
//calendar//*[category=“project”]//*.[“Mike”]
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Utilizing G. Miklau and D. Suciu. Containment and Equivalence for an Xpath Fragment. In PODS, 2002.
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Applying Multiple TrailApplying Multiple Trail
MMCA(Multiple Match Colouring Algorithm) algorithm
Trail can be applied infinitely
To prevent infinite recursion, a trail should not be rematched to nodes in a logical plan generated by itself
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Other IssuesOther Issues
Trail Pruning
Problem: MMCA is exponential in number of levels
Solution: Trail Pruning
– Prune by number of levels
– Prune by top-K trails matched in each level
Give weight and prob. to trails
– Prune by both top-K trails and number of levels
Trail Indexing
Precompute trail expressions in order to speed up query processing
Trail materialization
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ExperimentsExperiments
Setting
Configured iMeMex to act in three modes
– Baseline: Graph / IR search engine
– iTrails: Rewrite search queries with trails
– Perfect Query: Semantics-aware query
Data
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Experiment, QualityExperiment, Quality
Compare with baseline
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Experiment, overheadExperiment, overhead
Compare with perfect query
Overhead is not negligible
However, this can be fixed by exploiting trail materializations
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Experiment, Scalability #1Experiment, Scalability #1
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Rewrite Time
Query-rewrite time can be controlled with pruning
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Experiment, Scalability #2Experiment, Scalability #2
Quality
Pruning improves precision
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ConclusionConclusion
Our Contributions
iTrails: generic method to model semantic relationships (e.g. implicit meaning, bookmarks, dictionaries, thesauri,attribute matches, ...)
We propose a framework and algorithms for Pay-as-you-go Information Integration
Smooth transition between search and data integration
Future Work
Trail Creation
– Use collections (ontologies, thesauri, wikipedia)
– Work on automatic mining of trails from the dataspace
Other types of trails
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