We study the problem of applying adaptive filters for approximate query processing in a distributed stream environment. We propose filter bound assignment protocols with the objective of reducing communication cost. Most previous works focus on value-based queries (e.g., average) with numerical error tolerance. In this paper, we cover entity-based queries (e.g., nearest neighbor) with non-value-based error tolerance. We investigate different nonvalue-based error tolerance definitions and discuss how they are applied to two classes of entity-based queries: non-rank-based and rank-based queries. Extensive experiments show that our protocols achieve significant savings in both communication overhead and server computation.
One of the research issues considered in that area is the design of filter bound assignment protocols with the objective of reducing communication cost (see, e.g., REF ).
Adaptive Stream Filters for Entity-based Queries with Non-Value Tolerance
VLDB
Computer Science