English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  Items with full text/Total items : 113648/144635 (79%)
Visitors : 51688417      Online Users : 641
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
    •  Adv. Search
    Home ‧ Login ‧ Upload ‧ Help ‧ About ‧ Administer Goto mobile version
    政大機構典藏 > 資訊學院 > 資訊科學系 > 學位論文 >  Item 140.119/37121


    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/37121


    Title: 由執行記錄中探勘具備活動期間之工作流程模型
    Discovery of Workflow Models from Execution Logs with Activity Lifespans
    Authors: 黃文範
    Huang,Wen-Fan
    Contributors: 沈錳坤
    Shan,Man-Kwan
    黃文範
    Huang,Wen-Fan
    Keywords: 資料探勘
    工作流程探勘
    Data Mining
    Workflow Mining
    Date: 2006
    Issue Date: 2009-09-19 12:11:54 (UTC+8)
    Abstract: 工作流程（workflow）是商業流程自動化的一部份。一個工作流程是由完成一件工作所有可能執行的活動（activity）以及活動間在執行時的前後關係所構成。而工作流程的設計或改進舊有的工作流程是商業上很重要的工作，因為工作流程的好與壞會影響企業的競爭力。工作流程探勘（workflow mining）是利用資料探勘的技術，分析工作流程執行時所留下的流程執行記錄，還原出一個能夠產生這些記錄的工作流程模型（workflow model），而這個工作流程模型可做為設計新模型或改進既有模型的參考。
    本研究針對我們所定義的工作流程模型，以一個未知的工作流程模型所產生的流程執行記錄（workflow log）當做輸入資料（input data），提出方法利用輸入資料還原一個能夠產生輸入資料中所有資料工作流程模型，且希望這個工作流程模型能與產生流程執行記錄之未知模型越相似越好。我們提出兩個還原工作流程模型的演算法，並利用precision和recall來評估還原的模型與未知模型間的相似程度，驗證我們所提出方法的效果。實驗結果顯示，我們的方法所還原的工作流程模型precision和recall值都能達到80%以上。
    The workflow plays an important role in business process automation. A workflow is composed of activities and causal relations between activities to complete a task. Workflow design and refinement are important tasks in business process reengineering. As a workflow is executed, the orders of the executed activities are recorded in workflow logs. Workflow mining utilizes the technology of data mining to analyze these workflow logs, and reconstruct a workflow model.
    In this thesis, we investigate the workflow mining problem to reconstrcuct the workflow model. Two algorithms are proposed to reconstruct a workflow model. We evaluate our proposed algorithms by precision and recall to measure the similarity between the constructed and the groundtruth models. The result of the experiment shows that our proposed methods can achieve 80% precision and 80% recall for the reconstruction of workflow models.
    Reference: [1] A. Inokuchi, T. Washio, and H. Motoda. An Apriori-based Algorithm for MiningFrequent Substructures from Graph Data. Proceedings of the European Conference on Principles of Data Mining and Knowledge Discovery, 2000.
    [2] M. Kuramochi, and G. Karypis. Frequent Subgraph Discovery. Proceedings of IEEE International Conference on Data Mining, 2001.
    [3] X. Yan, and J. Han. gSapn: Graph-based Substructure Pattern Mining. Proceedings of IEEE International Conference on Data Mining, 2002.
    [4] X. Yan, and J. Han. CloseGraph: Mining Closed Frequent Graph Patterns. Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2003.
    [5] R. Agrawal, and R. Srikant. “Mining Sequential Patterns,” Proceedings of International Conference on Data Engineering, 1995.
    [6] R. Srikant, and R. Agrawal. Mining Sequential Patterns: Generalizations and Performance Improvements. Proceedings of International Conference on Extending Database Technology, 1996.
    [7] J. Han, J. Pei, B. Mortazavi-Asl, Q. Chen, U. Dayal, and M. C. Hsu. FreeSpan: Frequent Pattern-Projected Sequential Pattern Mining. Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2000.
    [8] J. Pei, J. Han, B. Mortazavi-Asl, and H. Pinto. PrefixSpan: Mining Sequential Patterns Efficiently by Prefix-Projected Pattern Growth. Proceedings of International Conference on Data Engineering, 2001.
    [9] G. Greco, A. Guzzo, G. Manco, and D. Saccà. Mining Frequent Instances on Workflows. Proceedings of Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2003.
    [10] G. Greco, A. Guzzo, G. Manco, and D. Saccà. Mining and Reasoning on Workflows. IEEE Transactions on Knowledge and Data Engineering, Vol. 17, No.4, pp. 519-534, 2005.
    [11] R. Agrawal, and R. Srikant. Fast Algorithms for Mining Association Rules. Proceedings of International Conference on Very Large Data Bases, 1994.
    [12] A. K. A. de Medeiros, W. M. P. van der Aalst, and A. J. M. M. Weijters. Workflow Mining: Current Status and Future Directions. Proceedings of International Conference on Cooperative Information Systems, 2003.
    [13] G. Greco, A. Guzzo, G. Manco, L. Pontieri, and D. Saccà. Mining Constrained Graphs: The Case of Workflow Systems. Constraint-Based Mining and Inductive Databases, pp. 155-171, 2004.
    [14] R. Agrawal, D. Gunopulos, and F. Leymann. Mining Process Models from Workflow Logs. Proceedings of International Conference on Extending Database Technology, 1998.
    [15] G. Greco, A. Guzzo, L. Pontieri, and D. Saccà. On the Mining of Complex Workflow Schemas. Proceedings of Italian Conference on Advanced Database Systems, 2004.
    [16] G. Greco, A. Guzzo, L. Pontieri, and D. Saccà. Mining Expressive Process Models by Clustering Workflow Traces. Proceedings of Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2004.
    [17] W. M. P. van der Aalst, B. F. van Dongena, J. Herbst, L. Marustera, G. Schimm, and A. J. M. M. Weijters. Workflow Mining: A Survey of Issues and Approach. Data & Knowledge Engineering, Volume 47, Issue 2, pp. 237-267, 2003.
    [18] S. Y. Hwang, C. P. Wei, and W. S. Yang. Discovery of Temporal Patterns from Process Instances. Computers in Industry, Volume 53, Issue 3, pp. 345-364, 2004.
    [19] W. M. P. van der Aalst, and A. J. M. M. Weijters. Process Mining: A Research Agenda. Computers in Industry, Volume 53, Issue 3, pp. 231-244, 2004.
    [20] G. Schimm. Mining Exact Models of Concurrent Workflows. Computers in Industry, Volume 53 , Issue 3, pp. 265-281, 2004.
    [21] L. Maruster, W. M. P. van der Aalst, T. Weijters, A. van der Bosch, and W. Daelemans. Automated Discovery of Workflow Models from Hospital Data. Proceeding of Belgium-Netherlands Conference on Artificial Intelligence, 2001.
    [22] S. S. Pinter, and M. Golani. Discovering Workflow Models from Activities’ Lifespans. Computers in Industry, Volume 53, Issue 3, pp. 283-296, 2004.
    [23] R. J. van Glabbeek, and W. P. Weijland. Branching Time and Abstraction in Bisimulation Semantics. Journal of ACM, Vol. 43, No. 3, pp. 555-600, 1996.
    [24] H. Mannila, and D. Rusakov. Decomposition of Event Sequences into Independent Components. Proceeding of SIAM International Conference on Data Mining, 2001.
    [25] J. E. Cook, and A. L. Wolf. Discovering Models of Software Processes from Event-based Data. ACM Transactions on Software Engineering and Methodology, Vol. 7, No. 3, pp. 215-249, 1998.
    [26] W. M. P. van der Aalst, and B. F. van Dongen. Discovering Workflow Performance Models from Timed Logs. Proceedings of International Conference on Engineering and Deployment of Cooperative Information Systems, 2002.
    [27] D. Grigori, F. Casati, U. Dayal, and M. C. Shan. Improving Business Process Quality through Exception Understanding, Prediction, and Prevention. Proceeding of International Conference on Vary Large Data Bases, 2001.
    [28] G. Schimm. Process Miner-A Tool for Mining Process Schemes from Event-based Data. Proceeding of European Conference on Artificial Intelligence, 2002.
    [29] L. Maruster, A. J. M. M. Weijters, W. M. P. van der Aalst, and A. van den Bosch. Process Mining：Discovering Direct Successors in Process Logs. Proceedings of International Conference on Discovery Science, 2002.
    [30] W. M. P. van der Aalst, A. J. M. M. Weijters, and L. Maruster. Workflow Mining：Which Process can be Rediscovered？. BETA Working Paper Series, WP 74, Eindhoven University of Technology, 2002.
    [31] J. E. Cook, and A. L. Wolf. Event-Based Detection of Concurrency. ACM SIGSOFT Software Engineering Notes, Vol. 23, Issue 6, pp. 35-45, 1998.
    [32] J. E. Cook, and A. L. Wolf. Software Process Validation：Quantitatively Measuring the Correspobdence of a Process to a Model. ACM Transactions on Software Engineering and Methodology, Vol. 8, No. 2, pp. 147-176, 1999.
    [33] G.. Greco, A. Guzzo, G.. Manco, and D. Saccà. Mining Unconnected Patterns in Workflows. Proceeding of SIAM International Conference on Data Mining, 2005.
    [34] B. F. van Dongen, and W. M. P. van der Aalst. Multi-phase Process Mining：Building Instance Graphs. Proceeding of International Conference on Conceptual Modeling, 2004.
    [35] E. Liu, A. Kumar, and W. M. P. van der Aalst. A Formal Modeling Approach for Supply Chain Event Management. Workshop on Issues in the Theory of Security, 2004.
    [36] S. Dustdar, T. Hoffmann, and W. M. P. van der Aalst. Mining of Ad-hoc Business Processes with TeamLog. Technical Report TUV-1841-2004-07, Vienna University of Technology, 2004.
    [37] J. Herbst, and D. Karagiannis. Workflow Mining with InWoLvE. Computers in Industry, Volume 53 , Issue 3, pp. 245-264, 2004.
    [38] L. Dehaspe, and H. Toivonen. Discovery of Frequent DATALOG Patterns. Data Mining and Knowledge Discovery, Volumn 3, Issue 1, pp. 7-36, 1999.
    [39] C. Faloutsos, K. S. McCurley, and A. Tomkins. Fast Discovery of Connection Subgraphs. Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2004.
    [40] J. Huan, W. Wang, and J. Prins. Efficient Mining of Frequent Subgraph in the Presence of Isomorphism. Proceedings of IEEE International Conference on Data Mining, 2003.
    [41] J. E. Cook, and A. L. Wolf. Automating Process Discovery through Event-data Analysis. Proceedings of International Conference on Software Engineering, 1995.
    [42] W. S. Yang. Mining Workflow Instances to Support Workflow Schema Design. Master Thesis, National Sun Yat-sen University, 1998.
    [43] S. Y. Hwang, and W. S. Yang. On the Discovery of Workflow Models from Their Instances. Decision Support System, Vol. 34, Issue 1, pp. 41-57, 2002.
    [44] W. M. P. van der Aalst, A. P. Barros, A.H.M ter Hofstede, and B. Kiepuszewski. Advance Workflow Patterns. Proceedings of International Conference on Cooperative Information System, 2000.
    Description: 碩士
    國立政治大學
    資訊科學學系
    92753029
    95
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0927530291
    Data Type: thesis
    Appears in Collections:[資訊科學系] 學位論文

    Files in This Item:

    File Description SizeFormat
    53029101.pdf49KbAdobe PDF2819View/Open
    53029102.pdf64KbAdobe PDF2749View/Open
    53029103.pdf60KbAdobe PDF2764View/Open
    53029104.pdf17KbAdobe PDF2761View/Open
    53029105.pdf56KbAdobe PDF2752View/Open
    53029106.pdf72KbAdobe PDF2769View/Open
    53029107.pdf41KbAdobe PDF2786View/Open
    53029108.pdf99KbAdobe PDF21028View/Open
    53029109.pdf118KbAdobe PDF2884View/Open
    53029110.pdf188KbAdobe PDF21064View/Open
    53029111.pdf155KbAdobe PDF2907View/Open
    53029112.pdf78KbAdobe PDF2872View/Open
    53029113.pdf48KbAdobe PDF2955View/Open


    All items in 政大典藏 are protected by copyright, with all rights reserved.


    社群 sharing

    著作權政策宣告 Copyright Announcement
    1.本網站之數位內容為國立政治大學所收錄之機構典藏，無償提供學術研究與公眾教育等公益性使用，惟仍請適度，合理使用本網站之內容，以尊重著作權人之權益。商業上之利用，則請先取得著作權人之授權。
    The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

    2.本網站之製作，已盡力防止侵害著作權人之權益，如仍發現本網站之數位內容有侵害著作權人權益情事者，請權利人通知本網站維護人員(nccur@nccu.edu.tw)，維護人員將立即採取移除該數位著作等補救措施。
    NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback