Applications using the Class Frequency Distribution of Maximal Repeats from Tagged Sequential Data

摘要：

Pattern discovery in sequential data mining is valuable for many applications, such as genomic signature (biomarker) identification, trend analysis, sequential logs analysis, user action (behavior) analysis and production line analysis. An approach can extract maximal repeat patterns from tagged sequences based on MapReduce programming model, and meanwhile compute the frequency distribution of these patterns according to the tags. Most of all, the components of sequences may be characters, words or records while the types of tags may be timestamps or classes given by users. This work had applied for a USA patent application as “Wang, Ching-Tu. Method for Extracting Maximal Repeat Patterns and Computing Frequency Distribution Tables. Patent Application Serial Number 15/208,994. 13 July 2016.”

講者簡介：

Dr. Jing-Doo Wang (王經篤) received the BS degree in Computer Science and Information Engineering from the Tatung Institute of Technology in 1989, and the M.S. and Ph.D. degrees Computer Science and Information Engineering from the University of Chung Cheng in 1993 and 2002 respectively. He has been with Asia University since spring 2003, where he is currently an associate professor in the Department of Computer Science and Information Engineering, and also holds a joint appointment with the Department of Bioinformatics and Medical Engineering. His research interests are in the areas of bioinformatics, text mining for trend analysis, class ambiguity analysis, and cloud computing. Recently, he is focusing on developing a scalable approach to extract maximal repeat patterns via MapReduce programming model and applying for the patent.

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