mining and querying multimedia data fan guo sep 19, 2011 committee members: christos faloutsos,...

Mining and Querying Multimedia DataFan GuoSep 19, 2011Committee Members:Christos Faloutsos, ChairEric P. XingWilliam W. CohenAmbuj K. Singh, University of California at Santa Babara

What this talk is about?

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What this talk is about?

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Going Multimedia

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Beyond Text and Images

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Thesis Outline

MiningM1: MultiAspectForensics

M2: QMAS

Querying

Q1: Click Models

Q2: C-DEM

Q3: BEFH

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Thesis Outline

MiningM1: MultiAspectForensics

M2: QMAS

Querying

Q1: Click Models

Q2: C-DEM

Q3: BEFH

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Mining Multimedia Data (1)

•Labeling Satellite Imagery

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Input Output

Mining Multimedia Data (2)

•Network Traffic Log Analysis

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Mining Multimedia Data (3)

•Web Knowledge Base

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Mining Multimedia Data

•Data-driven problem solving over multiple modes at a non-trivial scale.

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Thesis Outline

MiningM1: MultiAspectForensics

M2: QMAS

Querying

Q1: Click Models

Q2: C-DEM

Q3: BEFH

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Querying Multimedia Data (1)

•A querying system provides an interface to retrieve records that best match users’ information need.

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Querying Multimedia Data (1)

•Here is another example:

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https://www.facebook.com/pages/browser.php

Querying Multimedia Data (1)

•May be transformed into a graph search problem

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Querying Multimedia Data (2)

•Calibrate ranking from user feedback

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Querying Multimedia Data (2)

•Calibrate ranking from user feedback

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Thesis Outline

MiningM1: MultiAspectForensics

M2: QMAS

Querying

Q1: Click Models

Q2: C-DEM

Q3: BEFH

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Data

•Large-Scale Heterogeneous Networks

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Port

198.129.1.2131.243.2.10

131.243.2.5

128.3.10.40 128.3.1.50

IP-source IP-destination

80 (HTTP)

80 (HTTP)

993 (IMAP)

Goal

•How can we automatically detect and visualize patterns within a local community of nodes?

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Preliminary

•Tensor for high-order data representation▫3 data modes: source IP, destination IP,

port #

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Approach

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Data Decomposition

•The canonical polyadic (CP) decomposition can factor tensor into a sum of rank-1 tensors

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Data Decomposition

•A special case is Singular Value Decomposition

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Attribute Plot

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How to compute?

Spike Detection

•Iteratively search for spikes in the histogram plot along each data mode.

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“ “” ”

Substructure Discovery

•Focus on part of the data within the spike

•Categorize into a few subgraph patterns

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Pattern 1: Generalized Star (1)

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IP-src’s sending

packets to the same IP-

dst & the same portTypical

client/server system

Pattern 1: Generalized Star (1)

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A ‘bar’ in a carefully

reordered tensor

Pattern 1: Generalized Star (2)

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Extending along “Port-Number”

Pattern 1: Generalized Star (2)

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Port scanning or P2P

Port numbers used in

packets from the same IP-

src to the same IP-dst

Pattern 2: Generalized Bipartite-Core (1)

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A ‘plane’ in a carefully

reordered tensor

Pattern 2: Generalized Bipartite-Core (1)

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IP-src’s sending

packets to the same IP-dst’s & the same portClients

talking to a shared server

pool

Pattern 2: Generalized Bipartite-Core (2)

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A ‘plane’ in a carefully

reordered tensor

Pattern 2: Generalized Bipartite-Core (2)

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IP-src’s sending

packets over multiple

ports to one IP-dstA multi-

purpose windows server

M1: MultiAspectForensics

•Automatically detects novel patterns in heterogenous networks

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Thesis Outline

MiningM1: MultiAspectForensics

M2: QMAS

Querying

Q1: Click Models

Q2: C-DEM

Q3: BEFH

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QMAS: Mining Satellite Imagery (1)•Low-labor labeling

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Input Output

QMAS: Mining Satellite Imagery (2)•Low-labor labeling•Identification of Representatives

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QMAS: Mining Satellite Imagery (2)•Low-labor labeling•Identification of Representatives and

Outliers

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QMAS: Mining Satellite Imagery (2)•Low-labor labeling•Identification of Representatives and

Outliers

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QMAS: Mining Satellite Imagery (3)•Low-labor labeling•Identification of Representatives and

Outliers•Linear in time & space

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Thesis Outline

MiningM1: MultiAspectForensics

M2: QMAS

Querying

Q1: Click Models

Q2: C-DEM

Q3: BEFH

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Web Search

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User Clicks as Quality Feedback

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# of total clicks

Motivation

•Leverage the signal from click data to improve search ranking.

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Click Through Rate (CTR)

•CTR = # of Clicks / # of Impressions

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Position Bias

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Relevance of Web Document

•Relevance = CTR @ Position 1

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# Clicks @ Position 1# Impressions @ Position 1

=

Problem Definition

•Estimate the relevance of web documents given clicks and their positions.

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Design Goals / Constraints

•Scalable: single-pass, easy to parallel.

•Incremental: real-time updates possible.

•Accurate: consistent with past and future observations.

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Approach

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User Behavior Model

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Last Clicked Position

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Empirical Results

•Click data after pre-processing▫110K distinct queries, 8.8M query sessions.

•Training time: <6 mins

•Online update:▫Bump impression and click counters▫No data retention required

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Empirical Results

•Higher log-likelihood indicates better quality.

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27% accuracy in prediction2% improvement over ICM, the baseline model

Empirical Results

•Position-bias visualized

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Ground Truth

DCM

Scaling to Terabytes

•265TB data, 1.15B document relevance results,running time on wall clock ~ 3 hours

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Q1: Click Models

•A statistical approach to leveraging click data for better ranking aware of position-bias.

•They are incremental, more accurate than the baseline, scaling to almost petabyte-scale data.

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Thesis Outline

MiningM1: MultiAspectForensics

M2: QMAS

Querying

Q1: Click Models

Q2: C-DEM

Q3: BEFH

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Q2: C-DEM

•A flexible query interface for 3-mode data: images, genes, annotation terms.

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Q2: C-DEM

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Images

Terms Genes

Q2: C-DEM

•Solution: random walk with restart on graphs.

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Thesis Outline

MiningM1: MultiAspectForensics

M2: QMAS

Querying

Q1: Click Models

Q2: C-DEM

Q3: BEFH

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Q3: BEFH (1)• Bayesian exponential family harmonium• Deriving topical representations for

multimedia corpora (e.g., video snapshots and captions)

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Input Model

Q3: BEFH (2)• Bayesian exponential family harmonium• Deriving topical representations for

multimedia corpora (e.g., video snapshots and captions)

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Validation – Synthetic Data

Validation – TRECVID Data

Better Quality

Better Quality

Thesis Outline

MiningM1: MultiAspectForensics

M2: QMAS

Querying

Q1: Click Models

Q2: C-DEM

Q3: BEFH

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Conclusion

•Data-driven research under the theme of pattern mining and similarity querying.

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Conclusion

•Data-driven research under the theme of pattern mining and similarity querying.

•An array of practical tasks addressed:▫Internet traffic surveillance (M1)

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Conclusion

•Data-driven research under the theme of pattern mining and similarity querying.

•An array of practical tasks addressed:▫Internet traffic surveillance (M1)▫Satellite image analysis (M2)

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Conclusion

•Data-driven research under the theme of pattern mining and similarity querying.

•An array of practical tasks addressed:▫Internet traffic surveillance (M1)▫Satellite image analysis (M2)▫Web search (Q1)

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Conclusion

•Data-driven research under the theme of pattern mining and similarity querying.

•An array of practical tasks addressed:▫Internet traffic surveillance (M1)▫Satellite image analysis (M2)▫Web search (Q1)▫…

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Thank You!

•http://www.cs.cmu.edu/~fanguo/dissertation/

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