American Journal of Operations Management and Information Systems

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Detecting FNE in Sound Free-choice Petri Net with Data

Received: 24 April 2019    Accepted: 29 May 2019    Published: 12 June 2019
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Abstract

Nowadays, the development of a third-party service (Express industry) and a third-party payment (Alipay) are very fast in online shopping. Despite there are many technologies to detect control flow errors in business process, the soundness verification in data flow is very hard. To support the design of a workflow, we usually consider the correct control flow structure. However, information about data flow should also be ensured correct. The operation of the system may suffer some external attacks, which makes the task change the read and write operations, which result in changing of control flow structure which would lead to the emergence of unusual system. As a result, our approach provides a new technology to analysis the correctness of sound free-choice Petri net with data (SCDN). With the strong concealment of this attack, the system may suffer false-negative data flow errors (FNE), which would bring some loses to the participants. On the basis of behavioral profiles (BP), redundant data flow errors (RDE) and missing data flow errors (MDE), we provide the theory of FNE to demonstrate the stability, effectiveness and adaptation of our detection methods. Finally, a real E-commerce business system is used to illustrate the practicability of the method provided in this paper.

DOI 10.11648/j.ajomis.20190402.11
Published in American Journal of Operations Management and Information Systems (Volume 4, Issue 2, June 2019)
Page(s) 48-56
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

SCDN, FNE, BP, RDE, MDE

References
[1] Ying Huang, Wei Li, Zhengping Liang, Yu Xue, Xiuni Wang. Efficient business process consolidation: combining topic features with structure matching [J]. Soft Computing, 2018, 22 (2): 645-657.
[2] Natalia Sidorova, Christian Stahl and Nikola Trcka. Soundness verification for conceptual workflow nets with data: Early detection of errors with the most precision possible [J]. Information Systems, 2011, 36: 1026-1043.
[3] Eike Best and Harro Wimmel. Structure Theory of Petri Nets [J]. Transactions on Petri Nets and Other Models of Concurrency VII, 2013, 7480: 162-224.
[4] XuYa Cong, YuFeng Chen, ZhiWu Li, NaiQi Wu, Emad Abouel Nasr, and Abdulaziz Mohammed El-tamimi. Optimal Petri Net Supervisors of Discrete Event Systems via Weighted and Data Inhibitor Arcs [J]. IEEE Access, 2017, 6: 8245-8257.
[5] Matthias Weidlich, Artem Polyvyany, Jan Mendling and Mathias Weske. Causal Behavioral Profiles-Efficient Computation, Applications, and Evaluation [J]. Fundamental Informaticae, 2011, 113 (3-4): 399-435.
[6] Julio Clempner. Verifying soundness of business processes: A decision process Petri Nets approach [J]. Expert Systems with Applications, 2014, 41: 5030-5040.
[7] Matthias Weidlich, Tomer Sagi, Henrik Leopold, Avigdor Gal, and Jan Mendling. Predicting the Quality of Process Model Matching [C]. BPM, 2013, LNCS 8094: 203-210.
[8] Andreas Meyer, Luise Pufahl, Dirk Fahland, and Mathias Weske. Modeling and Enacting Complex Data Dependencies in Business Processes [C]. BPM, 2013, LNCS 8094: 171-186.
[9] Abdulelah Aldahami, Yuefeng Li, and Taizan Chan. Discovery of Dependency Relations in Sequential Data Flow [J]. Web Intelligence, 2017, 15 (1): 35-53.
[10] OMG. Business Process Modeling Notation Specification BPMN 1. 0, 2006.
[11] Nour Assy, Nguyen Ngoc Chan, and Walid Gaaloul. An Automated Approach for Assisting the Design of Configurable Process Models [J]. IEEE transactions on services computing, 2015, 8 (6): 874-888.
[12] Xinwei Zhu, Guobin Zhu, Seppe vanden Broucke, and Jan Recker. On Merging Business Process Management and Geographic Information Systems: Modeling and Execution of Ecological Concerns in Processes [C]. GRMSE, 2014, CCIS 482: 486-496.
[13] Sherry X. Sun, J. Leon Zhao, Jay F. Nunamaker and Olivia R. Liu Sheng. Formulating the Data-Flow Perspective for Business Process Management [J]. Information Systems Research, 2006, 17 (4): 374-391.
[14] Cristina Claudia Dolean, Razvan Petrusel. Data Flow Modeling: A Survey of Issues and Approaches [J]. Informatica Economica, 2012, 16 (4): 117-130.
[15] Booch G, Rumbaugh J, Jacobson I. The UML User Guide Addison Wesley, 1999.
[16] Wil M. P. van der Aalst and Hee K van. Workflow Management: Models, Methods and Systems, The MIT Press, 2002.
[17] Wil M. P. van der Aalst. Markings in Perpetual Free-Choice Nets are Fully Characterized by their Enabled Transitions [J]. Computer Science, Logic in Computer Science, 2018: 1-21.
[18] Matthias Weidlich, Jan Mendling and Mathias Weske. Efficient consistency Measurement Based on Behavioral Profiles of Process Models [J]. In 2011 IEEE Transactions on Software Engineer, 2011, 37 (3): 410-429.
[19] R. J. van Glabbeek and U. Goltz. Refinement of actions and equivalence notions for concurrent systems [J]. Acta Informatica, 2001, 37 (4/5): 229-327.
[20] J. Hidders, M. Dumas, W. M. P. van der Aalst, A. H. M. ter Hofstede, and J. Verelst. When are two Workflows the Same? [J] Australian Computer Society, 2005, 41: 3-11.
[21] Hema S. Meda, Anup Kumar Sen and Amitava Bagchi. On Detecting Data Flow Errors in Workflows [J]. ACM Journal of data and Information Quality, 2010, 2 (1): 1-31.
[22] Wang J and Kumar A. A framework for document-driven workflow systems [C]. BPM, 2005, LNCS 3649: 285-301.
[23] Basu A and Blanning R W. A formal approach to workflow analysis [J]. ISR, 2000, 11 (1): 17-36.
[24] M Hema Sundari, Anup K Sen, Amitava Bagchi. Detecting Data Flow Errors in Workflows: A Systematic Graph Traversal Approach [J]. In: Workshop on Information Technology and Systems, 2007.
[25] Sherry X. Sun and J. Leon Zhao. Developing a Workflow Design Framework Based on Dataflow Analysis [C]. Proceedings of the 41st Hawaii International Conference on System Sciences, 2008: 1-10.
[26] Nikola Trcka, Wil M. P. van der Aalst, and Natalia Sidorova. Analyzing Control Flow and Data Flow in Workflow Processes in a Unified Way [J]. Computer Science Reports, 2008, 0831: 1-23.
[27] Nikola Trcka, Wil M. P. van der Aalst, and Natalia Sidorova. Data-Flow Anti-patterns: Discovering Data-Flow Errors in Workflows [C]. In: International Conference on Advanced Information Systems Engineering, 2009, LNCS 5565: 425-439.
[28] Divya Sharma, Srujana Pinjala and Anup K Sen. Correction of Data-flow Errors in Workflows [C]. 25th Australasian Conference on Information Systems, Auckland, New Zealand, 8th -10th Dec 2014: 1-10.
[29] Silvia von Stackelberg, Susanne Putze, Jutta Mvlle, Klemens Bohm. Detecting Data Flow Errors in BPMN 2. 0 [J]. Open Journal of Information Systems, 2014, 1 (2): 1-19.
[30] Shazia Sadiq, Maria Orlowska, Wasim Sadiq and Cameron Foulger. Data Flow and Validation in Workflow Modeling [C]. Conferences in Research and Practice in Information Technology, 2003, 27: 207-214.
[31] Clarke, E. M., Grumberg, O., Peled, D. A. Model Checking [J]. The MIT Press, Cambridge, 1999.
[32] Win M. P. van der Aalst. Verification of workflow nets [J]. Lecture Notes in Computer Science, 1997, 1248: 407-426.
[33] Mariusz Dramski. Missing Data Problem in the Event Logs of Transport Processes [C]. TST 2017, 2017, CCIS 715: 110-120.
Author Information
  • Department of Computer Science, Tongji University, Shanghai, China

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  • APA Style

    Fang Zhao. (2019). Detecting FNE in Sound Free-choice Petri Net with Data. American Journal of Operations Management and Information Systems, 4(2), 48-56. https://doi.org/10.11648/j.ajomis.20190402.11

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    ACS Style

    Fang Zhao. Detecting FNE in Sound Free-choice Petri Net with Data. Am. J. Oper. Manag. Inf. Syst. 2019, 4(2), 48-56. doi: 10.11648/j.ajomis.20190402.11

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    AMA Style

    Fang Zhao. Detecting FNE in Sound Free-choice Petri Net with Data. Am J Oper Manag Inf Syst. 2019;4(2):48-56. doi: 10.11648/j.ajomis.20190402.11

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  • @article{10.11648/j.ajomis.20190402.11,
      author = {Fang Zhao},
      title = {Detecting FNE in Sound Free-choice Petri Net with Data},
      journal = {American Journal of Operations Management and Information Systems},
      volume = {4},
      number = {2},
      pages = {48-56},
      doi = {10.11648/j.ajomis.20190402.11},
      url = {https://doi.org/10.11648/j.ajomis.20190402.11},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.ajomis.20190402.11},
      abstract = {Nowadays, the development of a third-party service (Express industry) and a third-party payment (Alipay) are very fast in online shopping. Despite there are many technologies to detect control flow errors in business process, the soundness verification in data flow is very hard. To support the design of a workflow, we usually consider the correct control flow structure. However, information about data flow should also be ensured correct. The operation of the system may suffer some external attacks, which makes the task change the read and write operations, which result in changing of control flow structure which would lead to the emergence of unusual system. As a result, our approach provides a new technology to analysis the correctness of sound free-choice Petri net with data (SCDN). With the strong concealment of this attack, the system may suffer false-negative data flow errors (FNE), which would bring some loses to the participants. On the basis of behavioral profiles (BP), redundant data flow errors (RDE) and missing data flow errors (MDE), we provide the theory of FNE to demonstrate the stability, effectiveness and adaptation of our detection methods. Finally, a real E-commerce business system is used to illustrate the practicability of the method provided in this paper.},
     year = {2019}
    }
    

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  • TY  - JOUR
    T1  - Detecting FNE in Sound Free-choice Petri Net with Data
    AU  - Fang Zhao
    Y1  - 2019/06/12
    PY  - 2019
    N1  - https://doi.org/10.11648/j.ajomis.20190402.11
    DO  - 10.11648/j.ajomis.20190402.11
    T2  - American Journal of Operations Management and Information Systems
    JF  - American Journal of Operations Management and Information Systems
    JO  - American Journal of Operations Management and Information Systems
    SP  - 48
    EP  - 56
    PB  - Science Publishing Group
    SN  - 2578-8310
    UR  - https://doi.org/10.11648/j.ajomis.20190402.11
    AB  - Nowadays, the development of a third-party service (Express industry) and a third-party payment (Alipay) are very fast in online shopping. Despite there are many technologies to detect control flow errors in business process, the soundness verification in data flow is very hard. To support the design of a workflow, we usually consider the correct control flow structure. However, information about data flow should also be ensured correct. The operation of the system may suffer some external attacks, which makes the task change the read and write operations, which result in changing of control flow structure which would lead to the emergence of unusual system. As a result, our approach provides a new technology to analysis the correctness of sound free-choice Petri net with data (SCDN). With the strong concealment of this attack, the system may suffer false-negative data flow errors (FNE), which would bring some loses to the participants. On the basis of behavioral profiles (BP), redundant data flow errors (RDE) and missing data flow errors (MDE), we provide the theory of FNE to demonstrate the stability, effectiveness and adaptation of our detection methods. Finally, a real E-commerce business system is used to illustrate the practicability of the method provided in this paper.
    VL  - 4
    IS  - 2
    ER  - 

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