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A Distributed Multimedia Data Management over the Grid


A Distributed Multimedia Data Management over the Grid Kasturi Chatterjee Advisors for this Project: Dr. Shu-Ching Chen & Dr. Masoud Sadjadi Distributed Multimedia ... – PowerPoint PPT presentation

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Title: A Distributed Multimedia Data Management over the Grid

A Distributed Multimedia Data Management over
the Grid
  • Kasturi Chatterjee
  • Advisors for this Project Dr. Shu-Ching Chen
  • Dr. Masoud Sadjadi
  • Distributed Multimedia Information System
  • School of Computing and Information Sciences
  • Florida International University, Miami, FL
    33199, USA

  • Motivation
  • Why multimedia data ?
  • Why handling and representing multimedia data
  • Why distributed environment ?
  • Why content based image/video retrieval ?
  • Multimedia data management
  • Representation
  • Storage and Indexing
  • Popular retrieval strategies
  • Proposed Work Outline
  • Issues to be addressed
  • Components and Related Work
  • Conclusion

  • Why multimedia data ?
  • Attractive
  • Informative
  • Compact
  • Cheap memory makes storage easy
  • Why handling and representing multimedia data
  • Huge size (a typical 10 sec MPEG video 4M)
  • Temporal and Spatial Information
  • High-level meaning and the semantic gap
  • Multidimensional representation
  • Traditional database incapable of accommodating
    above characteristics

  • Why distributed environment ?
  • Shared storage
  • Shared Resources
  • Shared computing power
  • No single point of failure
  • Why content based image/video retrieval ?
  • unlike traditional data, temporal, spatial and
    semantic content should be considered during
    query of multimedia data
  • Can queries be issued textually for image/video
    databases? MAY BE NOT!
  • Meta data
  • Keywords
  • In Google Images sunset

  • Query
    By Example, Similarity

  • Measurement, Content

  • Interpretation, User Feedback

  • etc.
    to be considered

Multimedia data management
  • Representation
  • Multidimensional Unlike traditional data which
    is uni-dimensional, multimedia data in the form
    of image or video is multidimensional.
  • Semantic Interpretation Multimedia data can
    have varied semantic interpretation.
  • Feature Selection Identifying feature space to
    represent the multimedia data is an important and
    crucial step in MDBMS. Features can be Color,
    Texture or Temporal information etc.
  • The atypical nature of multimedia data needs
    special representation in the form of
    multidimensional feature vectors

Multimedia data management
  • Storage and Indexing
  • Indexing is an integral part of designing a
    database system to reduce
  • computation overhead and optimize retrieval.
  • Multimedia Data Indexing Requirements
  • Multimedia data stored as multidimensional
    feature vector.
  • Need to index a high dimensional feature space.
  • Index structure should map low level
    representation and high level semantic
  • Index structure should handle popular multimedia
    data retrieval strategies like content-based
    image retrieval (CBIR), relevance feedback (RF),
    video event retrievals etc.
  • Existing multidimensional indexing strategies
    fail to fulfill the above
  • requirements efficiently!

Multimedia data management
  • Popular Retrieval Strategies
  • (Content-Based Image/Video Retrieval)

Retrieval Results
Similarity Measurement
Proposed Work Outline
  • A typical Grid Architecture

Source http//gridcafe.web.cern.ch/gridcafe/grida
Proposed Work Outline
  • Research Issues
  • Development of a technique to enable uniform
    representation of the multimedia data
  • Development of an efficient index structure,
    capable of handling multimedia data and support
    applications like CBIR/CBVR, spanning across
    multiple storages over a Grid/distributed
  • Devising a mechanism by which users similarity
    concept across multiple network domains can be
    considered during providing query results
  • In short we envision to develop a distributed
    multimedia storage and
  • management system which will be capable of
    supporting popular retrieval
  • applications like CBIR/CBVR

Proposed Work Outline
  • The development and design of a multimedia data
  • management over grid has two critical components
  • Proper data management which prompts the
    requirement of a distributed multidimensional
    index structure and development of distributed
    retrieval algorithms (distributed k-NN or Range)
    supported by the index structure
  • Efficient retrieval which prompts the
    introduction of techniques to map low level
    features with high level semantic concepts, over
    a distributed environment, to provide relevant
    query results

Proposed Work Outline
  • Concepts to be utilized and Related Works
  • We have developed an index structure, called
    Affinity Hybrid Tree 1, for single node or
    stand alone applications, which is capable of
    indexing multidimensional images/videos and
    support CBIR/CBVR
  • Plan to extend it as the basic indexing and
    storage framework since it proved itself very
    efficient in stand alone environments
  • To capture the high level similarity concepts
    among the users in a distributed environment, we
    will develop a novel architecture called
    Distributed Affinity Capture Model (DACM) based
    on hierarchical markov model mediator 2.

Proposed Work Outline Components
  • Affinity Hybrid Tree

Feature based index mechanism filters the
feature space and reduce the of distance
computations to be performed
Reduce computational overhead
Distance based index mechanism
incorporates the high-level image
relationship as it is without translating
it into its low-level equivalence
Increase retrieved image relevance by capturing
the user concept as it is
Proposed Work Outline Components
  • Building AH-Tree
  • Feature
  • space
  • filtering
  • Semantic
  • relationship
  • introduction

Proposed Work Outline Components
  • Sample Results

Proposed Work Outline Components
  • Hierarchical Markov Model Mediator (HMMM) 2
  • A HMMM is represented by an 8-tuple
  • Where, d ? levels in HMMM
  • S ? multimedia objects in different
  • F ? distinctive features or semantic
    concepts (depending upon the

  • level)
  • A ? Affinity Relationship between
    multimedia objects
  • B ? Features/Concepts at each level
  • ?Initial state probability
  • O ? Weights of importance for the
    lower level features and higher level
  • concepts
  • L ? Link condition between higher
    level and lower level states
  • The model has been used successfully for several
    applications like CBIR and web document
  • clustering

Tentative Road Map
  • Details Literature Review for the following
  • available data management tools and techniques
    in Grid computing
  • peer-to-peer file sharing systems
  • Development of the following algorithms and
  • devise distributed k-NN search supporting
    CBIR/CBVR from within an index structure
  • develop Distributed Affinity Capture Model (DACM)
    to capture users concept of high-level
  • Implementation of the entire system

  • We propose to develop
  • An efficient multimedia data management framework
    over a distributed environment like Grid
  • Develop distributed content-based retrieval
    algorithms which will span across the grid to
  • semantically close query results
  • quickly and efficiently
  • Devise a way to capture users concept of
    similarity across the grid (bridging the gap
    between low-level features and high-level
    semantics is a challenge) with
  • An architecture called Distributed Affinity
    Capture Model (DACM)

Selected References
  • 1 Kasturi Chatterjee and Shu-Ching Chen, "A
    Novel Indexing and Access Mechanism using
    Affinity Hybrid Tree for Content-Based Image
    Retrieval in Multimedia Databases," International
    Journal of Semantic Computing (IJSC), Vol. 1,
    Issue 2, pp. 147-170, June 2007.
  • 2 Mei-Ling Shyu, Shu-Ching Chen, Min Chen,
    Chengcui Zhang, and Chi-Min Shu, "MMM A
    Stochastic Mechanism for Image Database Queries,"
    Proceedings of the IEEE Fifth International
    Symposium on Multimedia Software Engineering
    (MSE2003), pp. 188-195, December 10-12, 2003,
    Taichung, Taiwan, ROC.
  • 3 M.-L. Shyu, S.-C. Chen, and C.
    Haruechaiyasak, C.-M. Shu, and S.-T. Li,
    Disjoint Web
  • Document Clustering and Management in
    Electronic Commerce, the Seventh International
  • Conference on Distributed Multimedia Systems
    (DMS2001), pp. 494-497, 2001.
  • 4 Mei-Ling Shyu, Shu-Ching Chen, Min Chen,
    Chengcui Zhang, Kanoksri Sarinnapakorn,
  • "Image Database Retrieval Utilizing Affinity
    Relationships," accepted for publication, the
  • ACM International Workshop on Multimedia
    Databases (ACM MMDB'03), November 7, 2003,
  • New Orleans, Louisiana, USA.
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