This paper addresses the problem of continuous aggregate nearest-neighbor (CANN) queries for moving objects in spatio-temporal data stream management systems. A CANN query specifies a set of landmarks, an integer k, and an aggregate distance function f (e. g., min, max, or sum), where f computes the aggregate distance between a moving object and each of the landmarks. The answer to this continuous query is the set of k moving objects that have the smallest aggregate distance f. A CANN query may also be viewed as a combined set of nearest neighbor queries. We introduce several algorithms to continuously and incrementally answer CANN queries. Extensive experimentation shows that the proposed operators outperform the state-of-the-art algorithms by up to a factor of 3 and incur low memory overhead.
Bibliographical noteFunding Information:
This work is supported in part by the National Science Foundation under Grant Numbers IIS-0811954, III-1117766, IIS-0964639, IIS-0811935, CNS-0708604, IIS-0952977 (NSF CAREER) and by a Microsoft Research Gift.
- Aggregate nearest neighbor
- Continuous query
- Spatio-temporal query