Multiple Continuous Outlier Detection over Data Stream

Rui Zhu, Meiyu Guo, Anzhen Zhang, Tao Qiu, Chuanyu Zong, Jiajia Li, Hong Jiang, Bin Wang

Abstract: This paper studies the problem of Multiple Continuous Outlier Detection (MCOD for short) over data stream, a fundamental problem in the domain of streaming data management. Let \(\mathcal {S}\) be the set of streaming data, and \(\mathcal {Q}\) be the outlier detection query (query for short) workload. It contains a set of queries with different query parameters. Each query q(n, s, k, r) within \(\mathcal {Q}\) monitors objects in \(\mathcal {S}\) that are generated within the last q(n) time units. Whenever q(s) time units pass, q will return outliers within the range threshold r that do not satisfy k neighbor thresholds to the system. Some efforts are proposed to support MCOD, but they incur highly running cost both in computational and space, which cannot efficiently work under data stream.