Distributed stream networks continuously track the global score of the data and alert whenever a given threshold is crossed. The global score is computed by applying a scoring function over the aggregated streams. However, the sheer volume and dynamic nature of the streams impose excessive communication overhead. Most recent approaches eliminate the need for continuous communication, by using local constraints assigned at the individual streams. These constraints guarantee that as long as no constraint is violated, the threshold is not crossed, and therefore no communication is necessary. Regrettably, local constraint violations become more and more frequent as the network grows and, in the presence of such violations, communication is inevitable. In this paper, we show that in most cases the violations can be resolved efficiently. Although our solution requires only a reduced subset of the network streams, finding the minimum resolving set is NP-hard. Through analysis of the probability for resolution, we suggest methods to select the resolving set so as to minimize the expected communication overhead and the expected latency of the process. Experimental results with both synthetic and real-life data sets demonstrate that our methods yield considerable improvements over existing approaches.
|Title of host publication||Computational Intelligence, Cyber Security and Computational Models. Models and Techniques for Intelligent Systems and Automation - Third International Conference, ICC3 2017, Proceedings|
|Editors||Arumugam Subramaniam, Manuel Grana, Geetha Ganapathi, Suresh Balusamy, Rajamanickam Natarajan, Periakaruppan Ramanathan|
|Number of pages||28|
|State||Published - 2018|
|Event||3rd International Conference on Computational Intelligence, Cyber Security, and Computational Models, ICC3 2017 - Coimbatore, India|
Duration: 14 Dec 2017 → 16 Dec 2017
|Name||Communications in Computer and Information Science|
|Conference||3rd International Conference on Computational Intelligence, Cyber Security, and Computational Models, ICC3 2017|
|Period||14/12/17 → 16/12/17|
Bibliographical notePublisher Copyright:
© 2018, Springer Nature Singapore Pte Ltd.
ASJC Scopus subject areas
- Computer Science (all)
- Mathematics (all)