ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2017, Vol. 54 ›› Issue (9): 1880-1891.doi: 10.7544/issn1000-1239.2017.20160755

• 软件技术 • 上一篇    下一篇

基于行为和结构特征的相似语义工作流检索

孙晋永1,2,古天龙2,闻立杰3,钱俊彦2,孟瑜2   

  1. 1(西安电子科技大学计算机学院 西安 710071);2(广西可信软件重点实验室(桂林电子科技大学) 广西桂林 541004);3(清华大学软件学院 北京 100084) (sunjy@guet.edu.cn)
  • 出版日期: 2017-09-01
  • 基金资助: 
    国家自然科学基金项目(61562015,61572146,U1501252);广西自然科学基金项目(2015GXNSFDA139038,2016GXNSFDA380006);广西可信软件重点实验室项目(KX201627);广西高等学校高水平创新团队及卓越学者计划项目;桂林电子科技大学创新团队项目;广西精密导航技术与应用重点实验室项目(DH201508)

Retrieval of Similar Semantic Workflows Based on Behavioral and Structural Characteristics

Sun Jinyong1,2, Gu Tianlong2, Wen Lijie3, Qian Junyan2, Meng Yu2   

  1. 1(School of Computer Science and Technology, Xidian University, Xi’an 710071);2(Guangxi Key Laboratory of Trusted Software (Guilin University of Electronic Technology), Guilin,Guangxi 541004);3(School of Software, Tsinghua University, Beijing 100084)
  • Online: 2017-09-01

摘要: 相似语义工作流检索是语义工作流重用的首要任务.现有的相似语义工作流检索方法仅关注结构特征,忽略了行为特征,影响了检索到的相似语义工作流的整体质量,提高了语义工作流重用的代价.为此,提出一种结合行为和结构特征的2阶段相似语义工作流检索算法.使用任务紧邻关系集表达语义工作流的执行行为,结合领域知识构造语义工作流库的任务紧邻关系树索引和数据索引.针对查询语义工作流,先基于任务紧邻关系树索引和数据索引进行过滤得到候选语义工作流集;然后使用图匹配相似性算法对候选语义工作流集进行验证,得到排序的候选语义工作流集.实验结果表明,较主流的语义工作流检索算法,该方法的检索性能有较大提升,可以为工作流重用提供更高质量的语义工作流.

关键词: 工作流重用, 语义工作流, 相似性检索, 结构特征, 行为特征, 任务紧邻关系树索引

Abstract: Workflow reuse is an important method for modern enterprises and organizations to improve the efficiency of business process management (BPM). Semantic workflows are domain knowledge-based workflows. The retrieval of similar semantic workflows is the first step for semantic workflow reuse. Existing retrieval algorithms of similar semantic workflows only focus on semantic workflows’ structural characteristics while ignoring their behavioral characteristics, which affects the overall quality of retrieved similar semantic workflows and increases the cost of semantic workflow reuse. To address this issue, a two-phase retrieval algorithm of similar semantic workflows is put forward based on behavioral and structural characteristics. A task adjacency relations (TARs) set is used to express a semantic workflow’s behavior. A TARs trees index named TARTreeIndex and a data index named DataIndex are constructed combined with domain knowledge for the semantic workflows case base. For a given query semantic workflow, firstly, candidate semantic workflows are obtained by filtering the semantic workflows case base with the TARTreeIndex and DataIndex, then candidate semantic workflows are verified and ranked with the graph matching similarity algorithm. Experiments show that the proposed algorithm improves the retrieval performance of similar semantic workflows compared with the existing popular retrieval algorithms for similar semantic workflows, so it can provide high-quality semantic workflows for semantic workflow reuse.

Key words: workflow reuse, semantic workflow, similarity-based retrieval, structural characteristics, behavioral characteristics, task adjacency relations trees index (TARTreeIndex)

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