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Analysis of information quality requirements in business processes, revisited

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Abstract

Many business processes (BPs) involving critical decision-making activities require good-quality information for their successful enactment. Despite this fact, existing BP approaches focus on control flow and ignore the complementary information perspective, or simply treat it as a technical issue, rather than a social and organizational one. To tackle this problem, we propose a comprehensive framework for modeling and analyzing information quality requirements for business processes using the WFA-net BP modeling language. In addition, we describe a prototype implementation and present two realistic examples concerning the stock market domain, intended to illustrate our approach .

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Notes

  1. The term security refers to any tradable financial asset.

  2. A quote is an order that has not been performed [36].

  3. Details about capturing IQ requirements at a high-level of abstraction as soft goals and gradually refined and then approximated into IQ constraints (IQC) can be found in [28].

  4. The responsible actor has the capability, and we trust it for achieving such activity.

  5. A marking of a Petri net is a distribution of tokens over its places.

  6. http://www.dlvsystem.com/.

  7. The formalization of the concepts and axioms is omitted due to space limitation, yet they can be found at https://mohamadgharib.wordpress.com/bpsts-iq-tool/.

  8. The prototype tool is available at https://mohamadgharib.wordpress.com/bpsts-iq-tool/.

  9. Developed by Sirius https://projects.eclipse.org/projects/modeling.sirius.

  10. https://projects.eclipse.org/projects/modeling.m2t.acceleo.

  11. http://www.dlvsystem.com/dlv/.

  12. For more information about the case study refer to [26].

  13. We mainly focus on the ability of the automated analysis in detecting any violation to the properties of the design.

  14. All possible configurations concerning this process are shown in Fig. 5.

  15. T2 is the same as activity T10 in Scenario 1, and there is no need to discuss it again.

  16. Orders that last very short time, which make them unavailable for most of the traders [47].

  17. Orders with prices far away from the current market prices [41].

  18. T4 is the same as activity T2 in Scenario 1.

References

  1. Aguilar-Saven RS (2004) Business process modelling: review and framework. Int J Prod Econ 90(2):129–149

    Article  Google Scholar 

  2. Aldridge I (2013) High-frequency trading: a practical guide to algorithmic strategies and trading systems. Wiley, New York

    Google Scholar 

  3. Ballou D, Pazer H (1985) Modeling data and process quality in multi-input, multi-output information systems. Manag Sci 31(2):150–162

    Article  Google Scholar 

  4. Ballou D, Wang R, Pazer H, Tayi GK (1998) Modeling information manufacturing systems to determine information product quality. Manag Sci 44(4):462–484

    Article  MATH  Google Scholar 

  5. Batini C, Scannapieco M (2006) Data quality: concepts, methodologies and techniques. Springer, Berlin

    MATH  Google Scholar 

  6. Bhattacharya K, Hull R, Su J et al (2009) A data-centric design methodology for business processes. Handb Res Bus Process Model 23:503–531

    Article  Google Scholar 

  7. Biersack EW (1992) Performance evaluation of forward error correction in ATM networks. In: ACM SIGCOMM Computer Communication Review, vol 22, ACM, pp 248–257

  8. Bihlmeyer R, Faber W, Ielpa G, Lio V, Pfeifer G (2009) DLV-user manual. The DLV Project

  9. Bovee M, Srivastava RP, Mak B (2003) A conceptual framework and belief-function approach to assessing overall information quality. Int J Intell Syst 18(1):51–74

    Article  MATH  Google Scholar 

  10. Bresciani P, Perini A, Giorgini P, Giunchiglia F, Mylopoulos J (2004) Tropos: an agent-oriented software development methodology. Auton Agent Multi-Agent Syst 8(3):203–236

    Article  MATH  Google Scholar 

  11. Buneman P, Khanna S, Wang-Chiew T (2001) Why and where: a characterization of data provenance. In: International conference on database theory, Springer, Berlin, pp. 316–330

  12. Calvanese D, De Giacomo G, Hull R, Su J (2009) Artifact-centric workflow dominance. In: Service-Oriented Computing. Springer, Berlin, pp. 130–143

  13. Cappiello C, Caro A, Rodriguez A, Caballero I (2013) An approach to design business processes addressing data quality issues. In: ECIS, p 216

  14. Cliff D (2011) The Flash Crash of May 6th 2010: WTF. Technical Report Mimeo. University of Bristol

  15. Cohen F (1987) A cryptographic checksum for integrity protection. Comput Secur 6(6):505–510

    Article  Google Scholar 

  16. Cohn D, Hull R (2009) Business artifacts: a data-centric approach to modeling business operations and processes. IEEE Data Eng Bull 32(3):3–9

    Google Scholar 

  17. Combi C, Gambini M (2009) Flaws in the flow: the weakness of unstructured business process modeling languages dealing with data. In: On the move to meaningful internet systems: OTM 2009. Springer, Berlin, pp 42–59

  18. Cysneiros LM, Yu E (2004) Addressing agent autonomy in business process management-with case studies on the patient discharge process. In: Proceedings of 2004 IRMA Conference

  19. Dai C, Lin D, Bertino E, Kantarcioglu M (2008) An approach to evaluate data trustworthiness based on data provenance. In: Workshop on secure data management, Springer, Berlin, pp 82–98

  20. Deutsch A, Hull R, Patrizi F, Vianu V (2009) Automatic verification of data-centric business processes. In: Proceedings of the 12th international conference on database theory, ACM, pp 252–267

  21. Emery F, Trist E (1960) Socio-technical systems. Management sciences, models and techniques. churchman cw et al

  22. Eric S, Mylopoulos J (1994) From ER to AR—modelling strategic actor relationships for business process reengineering. In International Conference on Conceptual Modeling, Springer, Berlin, pp 548–565

  23. Eriksson H-E, Penker M (2000) Business modeling with UML. Business Patterns at Work. Wiley, New York

    Google Scholar 

  24. Fisher CW, Kingma BR (2001) Criticality of data quality as exemplified in two disasters. Inf Manag 39(2):109–116

    Article  Google Scholar 

  25. Gharib M, Giorgini P (2013) Modeling and analyzing information integrity in safety critical systems. In: Advanced information systems engineering workshops, Springer, Berlin, pp 524–529

  26. Gharib M, Giorgini P (2014) Detecting conflicts in information quality requirements: the May 6, 2010 Flash Crash. Technical Report, Universitá degli studi di Trento

  27. Gharib M, Giorgini P (2015) Analyzing trust requirements in socio-technical systems: a belief-based approach. In: IFIP working conference on the practice of enterprise modeling, Springer, pp 254–270

  28. Gharib M, Giorgini P (2015) Dealing with information quality requirements. In: International conference on enterprise, business-process and information systems modeling, Springer, Berlin, pp 379–394

  29. Gharib M, Giorgini P (2015) A goal-based approach for automated specification of information quality policies. In: 9th International conference on research challenges in information science (RCIS), IEEE, pp 139–150

  30. Gharib M, Giorgini P (2015) Modeling and reasoning about information quality requirements. In: International working conference on requirements engineering: foundation for software quality, Springer, Berlin, pp 49–64

  31. Gharib M, Giorgini P (2015) Modeling and reasoning about information quality requirements in business processes. In: Enterprise, business-process and information systems modeling. Springer, Berlin, pp 231–245

  32. Gomber P, Haferkorn M, Lutat M, Zimmermann K (2012) The effect of single-stock circuit breakers on the quality of fragmented markets. In: FinanceCom, pp 71–87

  33. Guerra-García C, Caballero I, Piattini M (2013) Capturing data quality requirements for web applications by means of dq_webre. Inf Syst Front 15(3):433–445

    Article  Google Scholar 

  34. Hamming R (1950) Error detecting and error correcting codes. Bell Syst Tech J 29(2):147–160

    Article  MathSciNet  Google Scholar 

  35. Hevner AR, March ST, Park J, Ram S (2004) Design science in information systems research. MIS Q 28(1):75–105

    Article  Google Scholar 

  36. Holden CW, Jacobsen S (2011) The breakdown of standard microstructure techniques: and what to do about it. Available at SSRN 1911491

  37. International Organization for Standardization/International Electrotechnical Commission (1991) ISO/IEC 9126: Information Technology-Software Product Evaluation-Quality Characteristics and Guidelines for Their Use. ISO/IEC

  38. International Organization for Standardization/International Electrotechnical Commission (2009) ISO/IEC 25012: Software engineering-software product quality requirements and evaluation (square)-data quality model. ISO/IEC

  39. International Organization for Standardization/International Electrotechnical Commission (2011) ISO/IEC 25010:2011: Systems and software engineering—Systems and software product Quality Requirements and Evaluation (SQuaRE)

  40. Juran J, Gryna F, Bingham R (1979) Quality control handbook, vol 9. McGraw-Hill Book Company, New York

    Google Scholar 

  41. Kirilenko AA, Kyle AS, Samadi M, Tuzun T (2015) The Flash Crash: the impact of high frequency trading on an electronic market. Available at SSRN 1686004

  42. Koliadis G, Vranesevic A, Bhuiyan M, Krishna A, Ghose A (2006) Combined approach for supporting the business process model lifecycle. In: PACIS, Citeseer, p 76

  43. Kumaran S, Liu R, Wu FY (2008) On the duality of information-centric and activity-centric models of business processes. In: International conference on advanced information systems engineering, Springer, Berlin, pp 32–47

  44. Lapouchnian A, Yu Y, Mylopoulos J (2007) Requirements-driven design and configuration management of business processes. In: Business Process Management. Springer, Berlin, pp 246–261

  45. Lewis M (2014) Flash boys: a wall street revolt. W. W. Norton & Company, New York

    Google Scholar 

  46. Liu L, Chi L (2002) Evolutional data quality: a theory-specific view. In IQ, pp 292–304

  47. McInish T, Upson J (2012) Strategic liquidity supply in a market with fast and slow traders. Available at SSRN 1924991

  48. Mishkin FS (2003) Policy remedies for conflicts of interest in the financial system. In: Macroeconomics, monetary policy and financial stability conference, Citeseer

  49. Moore DA, Tetlock PE, Tanlu L, Bazerman MH (2006) Conflicts of interest and the case of auditor independence: Moral seduction and strategic issue cycling. Acad Manag Rev 31(1):10–29

    Article  Google Scholar 

  50. Murata T (1989) Petri nets: properties, analysis and applications. Proc IEEE 77(4):541–580

    Article  Google Scholar 

  51. Nanex LLC (2010) Nanex Flash Crash summary report. http://www.nanex.net/FlashCrashFinal/FlashCrashSummary.html. Accessed 30 May 2014

  52. Natale D, Scannapieco M, Angeletti P, Catarci T, Raiss G (2001) Qualità dei dati e standard ISO/IEC 9126: Analisi critica ed esperienze nella pubblica amministrazione italiana. In Proceedings of the National Workshop on Sistemi in Rete nella Pubblica Amministrazione (in Italian), Roma, Italy

  53. Petrick JA, Scherer RF (2003) The Enron scandal and the neglect of management integrity capacity. Am J Bus 18(1):37–50

    Article  Google Scholar 

  54. Pipino LL, Lee YW, Wang RY (2002) Data quality assessment. Commun ACM 45(4):211–218

    Article  Google Scholar 

  55. Prabhakaran V, Arpaci-Dusseau A, Arpaci-Dusseau R (2005) Analysis and evolution of journaling file systems. In: Proceedings of the Annual USENIX Technical Conference, pp 105–120

  56. Quinlan S, Dorward S (2002) Venti: a new approach to archival storage. In: Proceedings of the FAST 2002 conference on file and storage technologies, vol 4

  57. Redman TC (1996) Improve data quality for competitive advantage. Qual Control Appl Stat 41:49–52

    Google Scholar 

  58. Redman TC, Blanton A (1997) Data quality for the information age. Artech House Inc., Norwood

    Google Scholar 

  59. Reichert M, Rinderle-Ma S, Dadam P (2009) Flexibility in process-aware information systems. Trans Petri Nets Other Models Concurr II:115–135

    Article  MATH  Google Scholar 

  60. Rose C (2011) The Flash Crash of May 2010: accident or market manipulation? J Bus Econ Res 9(1):85

    Google Scholar 

  61. Runeson P, Höst M (2009) Guidelines for conducting and reporting case study research in software engineering. Empir Softw Eng 14(2):131–164

    Article  Google Scholar 

  62. Russell N, ter Hofstede A, Edmond D, van der Aalst W (2005) Workflow data patterns: identification, representation and tool support. In: Conceptual Modeling, pp 353–368

  63. Russell N, van der Aalst W, ter Hofstede A (2006) Exception handling patterns in process-aware information systems. BPM Center Report BPM-06-04, BPMcenter.org, 06–04

  64. Sadiq S, Orlowska M, Sadiq W, Foulger C (2004) Data flow and validation in workflow modelling. In: Proceedings of the 15th Australasian database conference-Volume 27, Australian Computer Society, Inc., pp 207–214

  65. Scannapieco M, Pernici B, Pierce E (2002) IP-UML: towards a methodology for quality improvement based on the IP-MAP framework. In: IQ, pp 279–291

  66. Securities and Exchange Commission and others (2010) Findings regarding the market events of May 6, 2010. Report of the Staffs of the CFTC and SEC to the Joint Advisory Committee on Emerging Regulatory Issues

  67. Shankaranarayanan G, Wang RY, Ziad M (2000) IP-MAP: Representing the manufacture of an information product. In: IQ, pp 1–16

  68. Sidorova N, Stahl C, Trčka N (2010) Workflow soundness revisited: checking correctness in the presence of data while staying conceptual. In: Advanced Information Systems Engineering, Springer, Berlin, pp 530–544

  69. Simmhan YL, Plale B, Gannon D (2005) A survey of data provenance in e-science. ACM Sigmod Rec 34(3):31–36

    Article  Google Scholar 

  70. Sobolewski JS (2003) Cyclic redundancy check. Encyclopedia of Computer Science. Wiley, Chichester

    Google Scholar 

  71. Soffer P (2010) Mirror, mirror on the wall, can I count on you at all? exploring data inaccuracy in business processes. Enterprise, Business-Process and Information Systems Modeling, pp 14–25

  72. Sommerville I, Cliff D, Calinescu R, Keen J, Kelly T, Kwiatkowska M, Mcdermid J, Paige R (2012) Large-scale complex IT systems. Commun ACM 55(7):71–77

    Article  Google Scholar 

  73. Subrahmanyam A (2013) Algorithmic trading, the Flash Crash, and coordinated circuit breakers. Borsa Istanb Rev 13(3):4–9

    Article  Google Scholar 

  74. Sun Y, Su J, Wu B, Yang J (2014) Modeling data for business processes. In: 2014 IEEE 30th international conference on data engineering, IEEE, pp 1048–1059

  75. Sun Y, Su J, Yang J (2014) Separating execution and data management: A key to business-process-as-a-service (BPaaS). In: International conference on business process management, Springer, pp 374–382

  76. Trčka N, van der Aalst W, Sidorova N (2008) Analyzing control-flow and data-flow in workflow processes in a unified way. Technical Report 08-31, Eindhoven University of Technology

  77. Trčka N, van der Aalst W, Sidorova N (2009) Data-flow anti-patterns: Discovering data-flow errors in workflows. In: Advanced information systems engineering, Springer, Berlin, pp 425–439

  78. Trochim W, Donnelly J (2006) The research methods knowledge base. Cengage Learning

  79. U.S. Commodity Futures Trading Commission (2010) Preliminary findings regarding the market events of May 6, 2010. Report of the Staffs of the CFTC and SEC to the Joint Advisory Committee on Emerging Regulatory Issues

  80. Van der Aalst W, Weske M, Grunbauer D (2005) Case handling: a new paradigm for business process support. Data Knowl Eng 53(2):129–162

    Article  Google Scholar 

  81. van der Aalst WM (1998) The application of petri nets to workflow management. J Circuits Syst Comput 8(01):21–66

    Article  Google Scholar 

  82. Wand Y, Wang R (1996) Anchoring data quality dimensions in ontological foundations. Commun ACM 39(11):86–95

    Article  Google Scholar 

  83. Wang R (1998) A product perspective on total data quality management. Commun ACM 41(2):58–65

    Article  Google Scholar 

  84. Wang RY, Strong DM (1996) Beyond accuracy: What data quality means to data consumers. J Manag Inf Syst 12(4):5–34

    Article  Google Scholar 

  85. Yu ES-K (1995) Modelling strategic relationships for process reengineering. Ph.D. thesis, University of Toronto

  86. Zannone N (2006) A requirements engineering methodology for trust, security, and privacy. Ph.D. thesis, University of Trento

  87. Zhao X, Su J, Yang H, Qiu Z (2009) Enforcing constraints on life cycles of business artifacts. In: Third IEEE International symposium on theoretical aspects of software engineering. TASE 2009, IEEE, pp 111–118

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Acknowledgements

This research has received funding from the ERC advanced Grant No. 267856, “Lucretius: Foundations for Software Evolution”, http://www.lucretius.eu/, and the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 653642 - VisiOn.

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  1. Information Engineering and Computer Science Department (DISI), University of Trento, Via Sommarive 14, 38123, Povo, Trento, Italy

    Mohamad Gharib, Paolo Giorgini & John Mylopoulos

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  1. Mohamad Gharib
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Correspondence to Mohamad Gharib.

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Gharib, M., Giorgini, P. & Mylopoulos, J. Analysis of information quality requirements in business processes, revisited. Requirements Eng 23, 227–249 (2018). https://doi.org/10.1007/s00766-016-0264-4

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