STAGGER: Periodicity Mining of Data Streams Using Expanding Sliding Windows

Sensor devices are becoming ubiquitous, especially in measurement and monitoring applications. Because of the real-time, append-only and semi-infinite natures of the generated sensor data streams, an online incremental approach is a necessity for mining stream data types. In this paper, we propose STAGGER: a one-pass, online and incremental algorithm for mining periodic patterns in data streams. STAGGER does not require that the user pre-specify the periodicity rate of the data. Instead, STAGGER discovers the potential periodicity rates. STAGGER maintains multiple expanding sliding windows staggered over the stream, where computations are shared among the multiple overlapping windows. Small-length sliding windows are imperative for early and real-time output, yet are limited to discover short periodicity rates. As streamed data arrives continuously, the sliding windows expand in length in order to cover the whole stream. Larger-length sliding windows are able to discover longer perio...

• Type: Conference
• Year: 2006
• Where: ICDM
• Authors: Mohamed G. Elfeky, Walid G. Aref, Ahmed K. Elmagarmid
• Keywords: Data Mining, Data Streams, ICDM 2006, Periodicity Rates, STAGGER