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Please use this identifier to cite or link to this item: http://hdl.handle.net/10445/5998

Title: A Method for Sensor Data Prediction Based on Correlations among Multiple Data Streams
Authors: Shiraishi, Yoh
Arai, Kenji
Takahashi, Osamu
Abstract: By developing sensor network technologies, researches on data stream processing and stream mining have received much attention. Stream data prediction which predicts future data pattern by analyzing tendency of past data stream is one of streammining techniques. In a sensor network deployed in the real-world such as a city, a building and a room, data stream from a certain sensor node may relate to that from other ones. It is expected to improve the accuracy on sensor data prediction by considering correlations of these sensor data streams. In this paper, we propose a method for sensor data prediction based on correlations of multiple sensor data streams. This method extracts the feature quantities from partial sequences of each data stream and classifies these sequences into multiple groups by a clustering algorithm. The classified group is a cluster that expresses a pattern from the stream. Our method uses correlations among these clusters from different kinds of data streams and correlations between the past sequences on a stream in order to predict future data pattern. This paper describes an overview of our method for sensor data prediction and reports the results of the preliminary experiment. 
Research Achievement Classification: 国際会議/International Conference
Type: Conference Paper
Peer Review: あり/yes
Solo/Joint Author(s): 共著/joint
Spage: 25
Epage: 30
Date: Oct-2010
Publisher: IEEE
Appears in Collections:Takahashi, Osamu

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