Understanding local structure in ranked datasets

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

Research output: Contribution to conferencePaperpeer-review

4 Scopus citations


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
StatePublished - 2013
Event6th Biennial Conference on Innovative Data Systems Research, CIDR 2013 - Pacific Grove, United States
Duration: 6 Jan 20139 Jan 2013


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


FundersFunder number
Israel Ministry of Science
US-Israel bi-national science foundation
Engineering Research Centers
European Research CouncilFP7/2007-2013, 291071


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