A Service Composition Approach Based on Sequence Mining for Migrating E-learning Legacy System to SOA

作     者：Zhuo Zhang Dong-Dai Zhou Hong-Ji Yang Shao-Chun Zhong 

作者机构：Software School in Northeast Normal University Changchun 130024 PRC Software Technology Research Laboratory De Montfort University Leicester LE1 9BH UK Institute of Ideal Information and Technology in Northeast Normal University Changchun 130024 PRC Engineering Research Center of E-Learning Technologies Ministry of Education Changchun 130024 PRC 

出 版 物：《International Journal of Automation and computing》 (国际自动化与计算杂志（英文版）)

年 卷 期：2010年第7卷第4期

页      面：584-595页

核心收录：

学科分类：0401[教育学-教育学] 04[教育学] 08[工学] 0802[工学-机械工程] 0835[工学-软件工程] 0701[理学-数学] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 081202[工学-计算机软件与理论] 040110[教育学-教育技术学（可授教育学、理学学位）] 

基　　金：supported by E-learning Platform  National Torch Project (No. z20040010) 

主　　题：Service composition e-learning sequence mining algorithm service-oriented architecture (SOA) legacy system 

摘      要：With the fast development of business logic and information technology, today＇s best solutions are tomorrow＇s legacy systems. In China, the situation in the education domain follows the same path. Currently, there exists a number of e-learning legacy assets with accumulated practical business experience, such as program resource, usage behaviour data resource, and so on. In order to use these legacy assets adequately and efficiently, we should not only utilize the explicit assets but also discover the hidden assets. The usage behaviour data resource is the set of practical operation sequences requested by all users. The hidden patterns in this data resource will provide users＇ practical experiences, which can benefit the service composition in service-oriented architecture （SOA） migration. Namely, these discovered patterns will be the candidate composite services （coarse-grained） in SOA systems. Although data mining techniques have been used for software engineering tasks, little is known about how they can be used for service composition of migrating an e-learning legacy system （MELS） to SOA. In this paper, we propose a service composition approach based on sequence mining techniques for MELS. Composite services found by this approach will be the complementation of business logic analysis results of MELS. The core of this approach is to develop an appropriate sequence mining algorithm for mining related data collected from an e-learning legacy system. According to the features of execution trace data on usage behaviour from this e-learning legacy system and needs of further pattern analysis, we propose a sequential mining algorithm to mine this kind of data of tile legacy system. For validation, this approach has been applied to the corresponding real data, which was collected from the e-learning legacy system; meanwhile, some investigation questionnaires were set up to collect satisfaction data. The investigation result is 90% the same with the result obtained thr