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Diversifying Query Results on Semi-Structured Data Diversifying Query Results on Semi-Structured Data

Diversifying Query Results on Semi-Structured Data - PowerPoint Presentation

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Uploaded On 2016-10-13

Diversifying Query Results on Semi-Structured Data - PPT Presentation

Md Mahbub Hasan University of California Riverside XML Document School UToronto PhDThesis First Name Author Last Name Michalis Faloutsos PhDThesis First Name Author Last Name Christos ID: 475163

author faloutsos bib results faloutsos author results bib phdthesis michalis school utoronto return christos networking title paper distance diverse

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Slide1

Diversifying Query Results on Semi-Structured Data

Md. Mahbub HasanUniversity of California, RiversideSlide2

XML Document

School

UToronto

PhDThesis

First Name

Author

Last Name

Michalis

Faloutsos

PhDThesis

First Name

Author

Last Name

Christos

Faloutsos

School

UToronto

Paper

First Name

Author

Last Name

Michalis

Faloutsos

Title

Networking

BibSlide3

QueryFind all Bibliography records related to Faloutsos

BibFaloutsos

//Bib//

Faloutsos

Twig Pattern

XPath

ExpressionSlide4

Results

School

UToronto

PhDThesis

First Name

Author

Last Name

Michalis

Faloutsos

PhDThesis

First Name

Author

Last Name

Christos

Faloutsos

School

UToronto

Paper

First Name

Author

Last Name

Michalis

Faloutsos

Title

Networking

BibSlide5

ProblemSuppose we can return the user only two results(

k = 2)Which two results we should return?Slide6

Which Two Results We Should Return?

School

UToronto

PhDThesis

First Name

Author

Last Name

Michalis

Faloutsos

PhDThesis

First Name

Author

Last Name

Christos

Faloutsos

School

UToronto

Paper

First Name

Author

Last Name

Michalis

Faloutsos

Title

Networking

BibSlide7

SolutionSuppose we can return the user only two

results( k = 2)Which two results we should return?Return the results that are most diverse to each otherThe idea is to help the user to better understand/explore the result setSlide8

Diversity ProblemCan be divided into two

subproblemsHow to compute the distance between two results?How to find k most diverse results efficiently from the set of candidate answers?Slide9

How to Compute the Distance between Two Results?Two types of differences between results

Structural differenceContent differenceSlide10

Structural Differences

School

UToronto

PhDThesis

First Name

Author

Last Name

Michalis

Faloutsos

Bib

PhDThesis

First Name

Author

Last Name

Christos

Faloutsos

School

UToronto

Bib

Paper

First Name

Author

Last Name

Michalis

Faloutsos

Title

Networking

BibSlide11

Content Differences

School

UToronto

PhDThesis

First Name

Author

Last Name

Michalis

Faloutsos

Bib

PhDThesis

First Name

Author

Last Name

Christos

Faloutsos

School

UToronto

Bib

Paper

First Name

Author

Last Name

Michalis

Faloutsos

Title

Networking

BibSlide12

Finding Diverse ResultsNaïve Approach

Compute all pair-wise distances of the resultsFind the k-result subset with maximum diversityChallenges to improve the naïve approachReduce the number of distance computationsPrune large fraction of k-result subsetsSlide13

ConclusionDistance Measure for Structural Query results

Novel and EfficientConsiders both Structural and Content InformationDiversification AlgorithmHeuristic approach to improve the naïve algorithmFuture WorkConsider approximate matchesApproximation in structureApproximation in valueSlide14

Thank You!