Understanding local structure in ranked datasets

Julia Stoyanovich, Sihem Amer-Yahia, Susan B. Davidson, Marie Jacob, Tova Milo

    Research output: Contribution to conferencePaperpeer-review


    Ranked data is ubiquitous in real-world applications. Rankings arise naturally when users express preferences about products and services, when voters cast ballots in elections, when funding proposals are evaluated based on their merits and university departments based on their reputation, or when genes are ordered based on their expression levels under various experimental conditions. We observe that ranked data exhibits interesting local structure, representing agreement of subsets of rankers over subsets of items. Being able to model, identify and describe such structure is important, because it enables novel kinds of analysis with the potential of making ground-breaking impact, but is challenging to do effectively and efficiently. We argue for the use of fundamental data management principles such as declarativeness and incremental evaluation, in combination with state-of-the-art machine learning and data mining techniques, for addressing the effectiveness and efficiency challenges. We describe the key ingredients of a solution, and propose a roadmap towards a framework that will enable robust and efficient analysis of large ranked datasets.

    Original languageEnglish (US)
    StatePublished - Jan 1 2013
    Event6th Biennial Conference on Innovative Data Systems Research, CIDR 2013 - Pacific Grove, United States
    Duration: Jan 6 2013Jan 9 2013


    Conference6th Biennial Conference on Innovative Data Systems Research, CIDR 2013
    Country/TerritoryUnited States
    CityPacific Grove

    ASJC Scopus subject areas

    • Hardware and Architecture
    • Information Systems and Management
    • Artificial Intelligence
    • Information Systems


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