Browsing by Author "Li, Chu-Feng"

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A Scalable Complex Event Analytical System with Incremental Episode Mining over Data Streams
(2016)
Tseng, Jerry C. C.
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Gu, Jia-Yuan
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Tseng, Vincent S.
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Wang, P. F.
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Chen, Ching-Yu
Episode pattern mining is a very powerful technique to get high-valued information for people to solve real-life cross-disciplinary problems, such as for the analysis of manufacturing, stock markets, weather records and so on. As data grows, the mining process must be re-triggered again and again to obtain the most updated information. However, periodically re-mining the full dataset is not cost-effective, and thus a number of incremental mining approaches arise for the growing data. However, to our best knowledge, there exist few studies targeted on the problem of incremental episode mining. Moreover, streaming data of complex events is more and more popular because digital sensors always collect data around us in this big data age. Now the challenge is not only mining valuable episode patterns of incremental dataset, but also mining episode patterns over data streams of complex events. To address this research problem, we adopt the Lambda Architecture to design a scalable complex event analytical system that could be used to facilitate the incremental episode mining process over complex event sequences of data streams. Apache Spark and Apache Spark Streaming are applied as the development framework of the batch layer and the speed layer, respectively. To take both the efficiency and accuracy into consideration, we develop a series of modules and three algorithms, namely, batch episode mining, delta episode mining and pattern merging. Results from the experimental validation on a real dataset show that the proposed system carries high scalability and delivers excellent performance in terms of efficiency and accuracy.
Early Classification of Multivariate Time Series on Distributed and In-Memory Platforms
(2017-01-01)
Tseng, Vincent S.
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Huang, Huai-Shuo
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Huang, Chia-Wei
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Wang, Ping-Feng
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Li, Chu-Feng
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.