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A service composition approach based on sequence mining for migrating e-learning legacy system to SOA

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Abstract

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 the 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 through our approach.

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Authors and Affiliations

  1. Software School in Northeast Normal University, Changchun, 130024, PRC

    Zhuo Zhang, Dong-Dai Zhou & Shao-Chun Zhong

  2. Software Technology Research Laboratory, De Montfort University, Leicester, LE1 9BH, UK

    Zhuo Zhang & Hong-Ji Yang

  3. Institute of Ideal Information and Technology in Northeast Normal University, Changchun, 130024, PRC

    Dong-Dai Zhou & Shao-Chun Zhong

  4. Engineering Research Center of E-Learning Technologies, Ministry of Education, Changchun, 130024, PRC

    Dong-Dai Zhou & Shao-Chun Zhong

Authors
  1. Zhuo Zhang
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  2. Dong-Dai Zhou
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  3. Hong-Ji Yang
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  4. Shao-Chun Zhong
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Corresponding author

Correspondence to Shao-Chun Zhong.

Additional information

The work was supported by E-learning Platform, National Torch Project (No. z20040010)

Zhuo Zhang graduated from Jilin University, PRC in 1986. She received the M. Sc. degree from Jilin University, PRC in 1999, and from Montreal University, Canada in 2003. She is currently a professor at Software School in Northeast Normal University, PRC and a Ph.D. candidate of De Montfort University, UK.

Her research interests include data mining, software architecture, and intelligent tutoring, especially the service-oriented architecture (SOA) migration from the legacy systems such as migrating the educational software to SOA.

Dong-Dai Zhou graduated from Changchun University of Science and Technology (CUST), PRC in 1992. He received the M. Sc. degree from CUST in 1997 and the Ph. D. degree from the Jilin University, PRC in 2001. He is currently a professor at School of Software, Northeast Normal University, PRC.

His research interests include software architecture and software code autogeneration, especially the architecture design and codeless development of e-learning software.

Hong-Ji Yang received the B. Sc. and M.Phil. degrees from Jilin University in 1982 and 1985, respectively, and the Ph.D. degree from Durham University, UK in 1994. Currently, he is a professor at the Software Technology Research Laboratory, Faculty of Technology, De Montfort University, UK and leads the Software Evolution and Reengineering Group. He served as a program co-chair at IEEE International Conference on Software Maintenance in 1999 and the program chair at IEEE Computer Software and Application Conference in 2002.

His research interests include software engineering and pervasive computing.

Shao-Chun Zhong graduated from Northeast Dianli University, PRC in 1982. He received the M. Sc. degree from Jilin University, PRC in 1989 and the Ph.D. degree from Jilin University, PRC in 1994.

He is a professor at the Northeast Normal University, PRC and he is the director of Engineering Research Center of e-learning Technologies, Ministry of Education, PRC. His research interests include artificial intelligence, intelligent tutoring, and education technology.

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Zhang, Z., Zhou, DD., Yang, HJ. et al. A service composition approach based on sequence mining for migrating e-learning legacy system to SOA. Int. J. Autom. Comput. 7, 584–595 (2010). https://doi.org/10.1007/s11633-010-0544-2

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  • DOI: https://doi.org/10.1007/s11633-010-0544-2

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