Institutional Repository
Technical University of Crete
Technical University of Crete
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| URI: | http://purl.tuc.gr/dl/dias/33D84B51-8DF8-49A0-BD42-8492FA242937 | ||
| Year | 2008 | ||
| Type of Item | Peer-Reviewed Journal Publication | ||
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| Bibliographic Citation | G. Cormode and M. Garofalakis, "Approximate continuous querying over distributed streams", ACM Trans. Dat. Syst., vol. 33, no. 2, Jun. 2008. doi:10.1145/1366102.1366106 https://doi.org/10.1145/1366102.1366106 | ||
| Appears in Collections |
While traditional database systems optimize for performance on one-shot query processing, emerging large-scale monitoring applications require continuous tracking of complex data-analysis queries over collections of physically distributed streams. Thus, effective solutions have to be simultaneously space/time efficient (at each remote monitor site), communication efficient (across the underlying communication network), and provide continuous, guaranteed-quality approximate query answers. In this paper, we propose novel algorithmic solutions for the problem of continuously tracking a broad class of complex aggregate queries in such a distributed-streams setting. Our tracking schemes maintain approximate query answers with provable error guarantees, while simultaneously optimizing the storage space and processing time at each remote site, and the communication cost across the network. In a nutshell, our algorithms rely on tracking general-purpose randomized sketch summaries of local streams at remote sites along with concise prediction models of local site behavior in order to produce highly communication- and space/time-efficient solutions. The end result is a powerful approximate query tracking framework that readily incorporates several complex analysis queries (including distributed join and multi-join aggregates, and approximate wavelet representations), thus giving the first known low-overhead tracking solution for such queries in the distributed-streams model. Experiments with real data validate our approach, revealing significant savings over naive solutions as well as our analytical worst-case guarantees.
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