IDEAS home Printed from https://ideas.repec.org/a/spr/jcomop/v32y2016i4d10.1007_s10878-015-9930-6.html
   My bibliography  Save this article

Approximate sorting of data streams with limited storage

Author

Listed:
  • Farzad Farnoud

    (California Institute of Technology)

  • Eitan Yaakobi

    (California Institute of Technology)

  • Jehoshua Bruck

    (California Institute of Technology)

Abstract

We consider the problem of approximate sorting of a data stream (in one pass) with limited internal storage where the goal is not to rearrange data but to output a permutation that reflects the ordering of the elements of the data stream as closely as possible. Our main objective is to study the relationship between the quality of the sorting and the amount of available storage. To measure quality, we use permutation distortion metrics, namely the Kendall tau, Chebyshev, and weighted Kendall metrics, as well as mutual information, between the output permutation and the true ordering of data elements. We provide bounds on the performance of algorithms with limited storage and present a simple algorithm that asymptotically requires a constant factor as much storage as an optimal algorithm in terms of mutual information and average Kendall tau distortion. We also study the case in which only information about the most recent elements of the stream is available. This setting has applications to learning user preference rankings in services such as Netflix, where items are presented to the user one at a time.

Suggested Citation

  • Farzad Farnoud & Eitan Yaakobi & Jehoshua Bruck, 2016. "Approximate sorting of data streams with limited storage," Journal of Combinatorial Optimization, Springer, vol. 32(4), pages 1133-1164, November.
    • Handle: RePEc:spr:jcomop:v:32:y:2016:i:4:d:10.1007_s10878-015-9930-6
    DOI: 10.1007/s10878-015-9930-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10878-015-9930-6
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10878-015-9930-6?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Shieh, Grace S., 1998. "A weighted Kendall's tau statistic," Statistics & Probability Letters, Elsevier, vol. 39(1), pages 17-24, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tahani Coolen-Maturi, 2016. "New weighted rank correlation coefficients sensitive to agreement on top and bottom rankings," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(12), pages 2261-2279, September.
    2. Lee, Paul H. & Yu, Philip L.H., 2012. "Mixtures of weighted distance-based models for ranking data with applications in political studies," Computational Statistics & Data Analysis, Elsevier, vol. 56(8), pages 2486-2500.
    3. Pinto Da Costa, Joaquim & Roque, Luís A.C. & Soares, Carlos, 2015. "The weighted rank correlation coefficient rW2 in the case of ties," Statistics & Probability Letters, Elsevier, vol. 99(C), pages 20-26.
    4. Kung, Yi-Hung & Lin, Chang-Ting & Shih, Yu-Shan, 2012. "Split variable selection for tree modeling on rank data," Computational Statistics & Data Analysis, Elsevier, vol. 56(9), pages 2830-2836.
    5. Agostino Tarsitano & Rosetta Lombardo, 2011. "An Exhaustive Coefficient Of Rank Correlation," Working Papers 201111, Università della Calabria, Dipartimento di Economia, Statistica e Finanza "Giovanni Anania" - DESF.
    6. Jie Lin & Donald A. Adjeroh & Bing-Hua Jiang & Yue Jiang, 2018. "fastWKendall: an efficient algorithm for weighted Kendall correlation," Computational Statistics, Springer, vol. 33(4), pages 1823-1845, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:jcomop:v:32:y:2016:i:4:d:10.1007_s10878-015-9930-6. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.