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- W2399616294 abstract "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 pro- posals are evaluated based on their merits and university depart- ments 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 im- portant, because it enables novel kinds of analysis with the poten- tial 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 incremen- tal evaluation, in combination with state-of-the-art machine learn- ing and data mining techniques, for addressing the effectiveness and efficiency challenges. We describe the key ingredients of a so- lution, and propose aroadmap towards aframework that will enable robust and efficient analysis of large ranked datasets." @default.
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- W2399616294 date "2013-01-06" @default.
- W2399616294 modified "2023-10-16" @default.
- W2399616294 title "Understanding Local Structure in Ranked Datasets" @default.
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