Compressing semantic information with varying priorities

Basak Guler, Aylin Yener

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    2 Scopus citations


    Semantics of communicated data can lead to conclusions with varying degrees of priorities. Depending on the interests of the communicating parties, some facts lead to conclusions that carry a high risk when ignored, and others may not be worth the resources to share the facts leading to those uninteresting conclusions. This paper studies the worst-case semantic data compression problem for sharing facts that lead to conclusions with such varying priorities. We establish the performance bounds by utilizing the partial dependencies between the ideas and the priority distributions on the conclusions. We show that multiple term descriptions of the facts and conclusions improve the compression performance when combined with judicious partitioning of the fact space.

    Original languageEnglish (US)
    Title of host publicationProceedings - DCC 2014
    Subtitle of host publication2014 Data Compression Conference
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Number of pages10
    ISBN (Print)9781479938827
    StatePublished - 2014
    Event2014 Data Compression Conference, DCC 2014 - Snowbird, UT, United States
    Duration: Mar 26 2014Mar 28 2014

    Publication series

    NameData Compression Conference Proceedings
    ISSN (Print)1068-0314


    Other2014 Data Compression Conference, DCC 2014
    Country/TerritoryUnited States
    CitySnowbird, UT

    All Science Journal Classification (ASJC) codes

    • Computer Networks and Communications


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