Algebraic Query Optimization for Distributed Top-k Queries

Distributed top-$k$ query processing is increasingly becoming an essential functionality in a large number of emerging application classes. This paper addresses the efficient algebraic optimization of top-$k$ queries in wide-area distributed data repositories where the index lists for the attribute values (or text terms) of a query are distributed across a number of data peers and the computational costs include network latency, bandwidth consumption, and local peer work. We use a dynamic programming approach to find the optimal execution plan using compact data synopses for selectivity estimation that is the basis for our cost model. The optimized query is executed in a hierarchical way involving a small and fixed number of communication phases. We have performed experiments on real web data that show the benefits of distributed top-$k$ query optimization both in network resource consumption and query response time