Detecting Current Outliers: Continuous Outlier Detection over Time-Series Data Streams

Autor:	Hiroyuki Kitagawa, Kozue Ishida
Rok vydání:	2008
Předmět:	Data stream Ubiquitous computing Data stream mining Computer science Outlier Anomaly detection STREAMS Data mining Time series computer.software_genre computer
Zdroj:	Lecture Notes in Computer Science ISBN: 9783540856535 DEXA
DOI:	10.1007/978-3-540-85654-2_26
Popis:	The development of sensor devices and ubiquitous computing have increased time-series data streams. With data streams, current data arrives continuously and must be monitored. This paper presents outlier detection over data streams by continuous monitoring. Outlier detection is an important data mining issue and discovers outliers, which have features that differ profoundly from other objects or values. Most existing outlier detection techniques, however, deal with static data, which is computationally expensive. Specifically, for outlier detection over data streams, real-time response is very important. Existing techniques for static data, however, are fraught with many meaningless processes over data streams, and the calculation cost is too high. This paper introduces a technique that provides effective outlier detection over data streams using differential processing, and confirms effectiveness.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::1d20de610de1e249a30401afed0e97a3 https://doi.org/10.1007/978-3-540-85654-2_26 Zobrazit plný text záznamu