Ranking Distributed Probabilistic Data

Ranking queries are essential tools to process large amounts of probabilistic data that encode exponentially many possible deterministic instances. In many applications where uncertainty and fuzzy information arise, data are collected from multiple sources in distributed, networked locations, e.g., distributed sensor fields with imprecise measurements, multiple scientific institutes with inconsistency in their scientific data. Due to the network delay and the economic cost associated with communicating large amounts of data over a network, a fundamental problem in these scenarios is to retrieve the global top-k tuples from all distributed sites with minimum communication cost.

Provided by: Association for Computing Machinery Topic: Big Data Date Added: Jul 2009 Format: PDF

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