KLEE: A Framework for Distributed Top-k Query Algorithms

This paper addresses the efficient processing 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 present KLEE, a novel algorithmic framework for distributed top-k queries, designed for high performance and flexibility. KLEE makes a strong case for approximate top-k algorithms over widely distributed data sources. It shows how great gains in efficiency can be enjoyed at low result-quality penalties. Further, KLEE affords the query-initiating peer the flexibility to trade-off result quality and expected performance and to trade-off the number of communication phases engaged during query execution versus network bandwidth performance. We have implemented KLEE and related algorithms and conducted a comprehensive performance evaluation. Our evaluation employed real...

• Type: Conference
• Year: 2005
• Where: VLDB
• Authors: Sebastian Michel, Peter Triantafillou, Gerhard Weikum
• Keywords: Database, KLEE, Top-k Queries, VLDB 2005, Wide-area Distributed Data