Generalized Deduplication: Bounds, Convergence, and Asymptotic Properties

    Research output: Contribution to book/anthology/report/proceedingArticle in proceedingsResearchpeer-review


    We study a generalization of deduplication, which enables lossless deduplication of highly similar data and show that classic deduplication with fixed chunk length is a special case. We provide bounds on the expected length of coded sequences for generalized deduplication and show that the coding has asymptotic near-entropy cost under the proposed source model. More importantly, we show that generalized deduplication allows for multiple orders of magnitude faster convergence than classic deduplication. This means that generalized deduplication can provide compression benefits much earlier than classic deduplication, which is key in practical systems. Numerical examples demonstrate our results, showing that our lower bounds are achievable, and illustrating the potential gain of using the generalization over classic deduplication. In fact, we show that even for a simple case of generalized deduplication, the gain in convergence speed is linear with the size of the data chunks.
    Original languageEnglish
    Title of host publication2019 IEEE Global Communications Conference, GLOBECOM 2019 - Proceedings
    Publication date2019
    Article number9014012
    ISBN (Electronic)978-1-7281-0962-6
    Publication statusPublished - 2019
    EventIEEE Global Communications (GLOBECOM 2019) - Kona, Hawaii, Kona, United States
    Duration: 8 Dec 201912 Dec 2019


    ConferenceIEEE Global Communications (GLOBECOM 2019)
    LocationKona, Hawaii
    Country/TerritoryUnited States


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