標題: Early Classification of Multivariate Time Series on Distributed and In-Memory Platforms
作者: Tseng, Vincent S.
Huang, Huai-Shuo
Huang, Chia-Wei
Wang, Ping-Feng
Li, Chu-Feng
交大名義發表
National Chiao Tung University
關鍵字: Early classification;Multivariate time series;Parallel and distributed computing;Shapelets
公開日期: 1-Jan-2017
摘要: With the popularity of Internet of Things (IOT) applications, various kinds of active sensors are deployed and multivariate time series datasets are generated rapidly. Early classification of multivariate time series is an emerging topic in data mining due to the wide applications in many domains. The unique part of early classification lies in that it uses only earlier part of time series data to reach classification results with the same accuracy as by methods using complete time series information. Although a number of relevant studies have been presented recently, most of them didn't consider the issues of data scale and execution efficiency simultaneously. The main research issue of this paper falls in how to mine interpretable patterns from multivariate time series data, with which effective classification models can be constructed to ensure the accuracy as well as earliness. To take into account the issues of data scale and execution efficiency simultaneously, we explore distributed in-memory computing techniques and multivariate shapelets-based approaches to construct a Spark-based inmemory mining framework to parallelize the feature extraction process. We implement a framework with Multivariate Shapelets Detection (MSD) method as a based example. Through empirical evaluation on various kinds of sensory datasets, the scalability of the framework is evaluated in terms of efficiency while ensuring the same accuracy and reliability in early classification of multivariate time series. This work is the first one to realize multivariate time series early classification on Spark distributed in-memory computing platform, which can serve as a good base for an extension to other time series classification methods based on shapelet feature extraction.
URI: http://dx.doi.org/10.1007/978-3-319-67274-8_1
http://hdl.handle.net/11536/150801
ISSN: 0302-9743
DOI: 10.1007/978-3-319-67274-8_1
期刊: TRENDS AND APPLICATIONS IN KNOWLEDGE DISCOVERY AND DATA MINING, 2017
Volume: 10526
起始頁: 3
結束頁: 14
Appears in Collections:Conferences Paper