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ACM KDD - Multimedia Data Mining - Call For Papers

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  • Aaron K Baughman
    Multimedia Data Mining 2013 - Call for Paper The 13th International Workshop on Multimedia Data Mining (MDMKDD 2013) August 11, 2013 Chicago, Illinois, USA
    Message 1 of 1 , May 22, 2013
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      Multimedia Data Mining 2013 - Call for Paper

      The 13th International Workshop on Multimedia Data Mining (MDMKDD 2013)

      August 11, 2013
      Chicago, Illinois, USA

      Workshop website:  
      http://sites.google.com/site/mdmkdd2013chicago

         *   *   *
      The MDM/KDD 2013 workshop is in conjunction with the 19th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD-2013).

      Important Dates:
      • Submission Due: May 24, 2013 (Friday)
      • Acceptance Notification: June 16, 2013 (Sunday)
      • Camera-ready Due: June 28, 2013 (Friday)
      • Workshop Date: August 11, 2013 (Sunday)

      Papers accepted for presentation at the workshop will be published in the workshop proceedings and at the ACM digital library.

      Paper submission and reviewing will be handled electronically. Authors should consult the workshop Web site for full details regarding paper preparation and submission guidelines:

      https://sites.google.com/site/mdmkdd2013chicago/submission-instructions
       
      The paper submission site for the MDM/KDD 2013 workshop is:
      https://cmt.research.microsoft.com/MDM2013/.

         *   *   *
      Workshop Topics:

      The theme of this edition of the workshop is "mining largescale rich content in a networked society." Vast
      amount of multimedia are produced, shared, and accessed everyday in various social platforms. These
      multimedia objects (images, videos, texts, tags, etc.) represent rich, multifaceted recordings of human
      behavior in the networked society, which lead to a range of social applications such as, (a) consumer
      behavior forecasting and socialdriven advertising / business, (b) local knowledge discovery (e.g., for tourism
      or shopping), and (c) detection of emergent news events and trends, and so on. In addition to techniques for
      mining single media items, all these applications require new methods for discovering robust features and
      stable relationships among the content of different media modalities and the users, in a dynamic, social
      contextrich, and likely noisy environment.

      Mobile devices with multimedia sensors, such as cameras and geolocation sensors, have further integrated
      multimedia into people's daily life. New features, algorithms, and applications for mining the multimedia data collected at mobile devices can make these data of multiple modalities (image, video, geo, mobile data, etc.) accessible and useful in people’s daily life. Examples of such applications include (a) personal assistant, (b) augmented reality, (c) social applications, (d) entertainment, and so on.

      In addition to the research themes mentioned above, this workshop also welcomes submissions on various
      research topics of multimedia data mining, include but are not limited to the following:

      • Measurement of relevance and user engagement in social media retrieval.
      • Evaluation framework for the quality of rich content mining algorithms.
      • Emerging technology of multimedia data mining for mobile applications.
      • Emergent semantics analysis and topic detection from interrelated multimedia objects.
      • Social based business leveraging multimedia (e.g., social graph mining, social tagging, etc.).
      • Data mining for locationen hanced services (e.g., maps, navigation and GIS systems).
      • Fusion of multimedia features, metadata, user generated content, and social context.
      • Scalable or realtime architecture for largescale media content processing and mining (e.g., parallel
        computing, big data engineering, etc.).
      • Multimedia data mining across platforms, including web and mobile devices.
      • Scalable mobile multimedia computing (e.g., visual search, etc.).
      • Predictive and prescriptive multimedia data modeling.
      • Anomaly and outlier detection in multimedia databases.
      • Mining and modeling multimodal time series.
      • Security and privacy management of multimedia objects.
      • Human computer interfaces for multimedia data mining.

          *   *   *

      Formatting Requirements for Submitted Papers

      All submissions must be in PDF format and must not exceed 10MB in size.

      Papers should be no more than 9 pages total in length. The format is the standard double-column ACM Proceedings Style. Additional information about formatting and style files are available online at: 
      http://www.acm.org/sigs/publications/proceedings-templates

      Papers that do not meet the formatting requirements will be rejected.

      For accepted papers, authors will have the opportunity to revise their papers in response to the reviewers before final submission for publication in the proceedings.

      The paper submission site for MDM/KDD 2013 is: 
       https://cmt.research.microsoft.com/MDM2013/.

      Software demonstrations are welcome. We encourage submissions of ‘greenhouse’ work, which present early stages of cutting-edge research and development.

      Papers accepted for presentation at the workshop will be published in the workshop proceedings and at the ACM digital library.

      For more information regarding submissions, please visit the following page:
      https://sites.google.com/site/mdmkdd2013/submission-instructions 

         *   *   *

      Workshop Co-Chairs:

      Aaron Baughman (aaron.baughman@...), IBM
      Jiang (John) Gao (gao.new@...), Nokia USA
      Tim Pan (jiayu.pan@...), Google USA
      Vincent Oria (vincent.oria@...), New Jersey Institute of Technology, USA
      Yu-Ru Lin (yuruliny@...), Northeastern University, USA



      Aaron Baughman
      Senior Data Analytics/Software Eng.

      Special Events Systems Team
      Golf and Tennis Circuits
      IBM Master Inventor, Invention Disclosure Team Member
      Public Sector Tech Community Leader

      aaron.baughman@... : baaron@...
      w: 1-301-933-0347 c: 1-703-585-2747

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