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

机译：一种基于序列挖掘的服务学习方法，用于将在线学习遗留系统迁移到SOA

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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 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.
机译：随着业务逻辑和信息技术的快速发展，当今最好的解决方案就是明天的遗留系统。在中国，教育领域的情况遵循相同的道路。当前，存在许多具有积累的实际业务经验的电子学习遗留资产，例如程序资源，使用行为数据资源等。为了充分有效地利用这些遗留资产，我们不仅应利用显性资产，而且还应发现隐藏资产。使用行为数据资源是所有用户请求的一组实际操作序列。此数据资源中的隐藏模式将为用户提供实践经验，这可以有益于面向服务的体系结构（SOA）迁移中的服务组合。即，这些发现的模式将成为SOA系统中的候选组合服务（粗粒度）。尽管数据挖掘技术已用于软件工程任务，但对于如何将其用于将电子学习遗留系统（MELS）迁移到SOA的服务组合知之甚少。在本文中，我们提出了一种基于序列挖掘技术的服务组合方法。通过这种方法发现的组合服务将作为MELS的业务逻辑分析结果的补充。该方法的核心是开发一种适当的序列挖掘算法，用于挖掘从电子学习遗留系统收集的相关数据。根据此电子学习遗留系统中有关使用行为的执行跟踪数据的特征以及进一步的模式分析的需求，我们提出了一种顺序挖掘算法来挖掘遗留系统中的此类数据。为了验证，此方法已应用于从电子学习遗留系统收集的相应真实数据；同时，建立了一些调查问卷以收集满意度数据。调查结果与通过我们的方法获得的结果相同，为90％。

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  • 来源
    《International Journal of Automation & Computing》 |2010年第4期|p.584-595|共12页
  • 作者

    Zhuo Zhang; Dong-Dai Zhou; Hong-Ji Yang; Shao-Chun Zhong;

  • 作者单位

    Software School in Northeast Normal University, Changchun 130024, PRC,Software Technology Research Laboratory, De Montfort University, Leicester LEI 9BH, UK;

    Software School in Northeast Normal University, Changchun 130024, PRC,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;

    Software Technology Research Laboratory, De Montfort University, Leicester LEI 9BH, UK;

    Software School in Northeast Normal University, Changchun 130024, PRC,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;

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  • 正文语种 eng
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  • 关键词

    service composition; e-learning; sequence mining algorithm; service-oriented architecture (soa); legacy system;

    机译：服务组成;电子学习;序列挖掘算法面向服务的架构（soa）;遗留系统;

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