Data Driven Approximation with Bounded Resources

This paper proposes BEAS, a resource-bounded scheme for querying relations. It is parameterized with a resource ratio alpha is an element of (0, 1], indicating that given a big dataset D, we can only afford to access an alpha-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 alpha vertical bar D vertical bar 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.