Data driven approximation with bounded resources

Abstract

This paper proposes BEAS, a resource-bounded scheme for querying relations. It is parameterized with a resource ratio α ∈ (0,1], indicating that given a big dataset D , we can only afford to access an α -fraction of D with limited resources. For a query Q posed on D , BEAS computes exact answers Q(D) if doable and otherwise approximate answers, by accessing at most α | D | amount of data in the entire process. Underlying BEAS are (1) an access schema, which helps us identify and fetch the part of data needed to answer Q , (2) an accuracy measure to assess approximate answers in terms of their relevance and coverage w.r.t . exact answers, (3) an Approximability Theorem for the feasibility of resource-bounded approximation, and (4) algorithms for query evaluation with bounded resources. A unique feature of BEAS is its ability to answer unpredictable queries, aggregate or not, using bounded resources and assuring a deterministic accuracy lower bound. Using real-life and synthetic data, we empirically verify the effectiveness and efficiency of BEAS.