Skip to main content
Log in

IQCPSoS: A Model-Based Approach for Modeling and Analyzing Information Quality Requirements for Cyber-Physical System-of-Systems

  • Original Article
  • Published:
Journal on Data Semantics

Abstract

A Cyber-Physical System-of-Systems (CPSoS) can be defined as a System-of-Systems (SoS), where its component systems are Cyber-Physical Systems (CPSs) that have been networked together for achieving a certain higher goal. Therefore, a key viability of any CPSoS is the integration of its CPSs to function as a single integrated system to support a common mission. Although such integration can be achieved relying on the exchange of information among CPSs, only few works have highlighted the importance of considering the quality of such information. Without considering Information Quality (IQ) requirements during the design of CPSoS, CPSs will be vulnerable to faults arising from depending on inaccurate, incomplete, inconsistent, and/or outdated information, which may influence the overall dependability, reliability, and performance of the CPSoS. This paper proposes a model-based approach that offers a novel UML profile, named IQCPSoS (Information Quality for Cyber-Physical System-of-Systems), which contains various stereotypes and tagged values for modeling and analyzing IQ requirements for CPSoS. The profile also proposes a set of constraints expressed in the Object Constraint Language (OCL) to be used for the verification of such models. We evaluate our approach by developing a prototype implementation and test its applicability, usability, and validity for modeling and analyzing IQ requirements for a realistic scenario concerning a Tram system.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Data Availability

Not applicable.

Notes

  1. CPSs that are not part of a CPSoS are also subject to failures due to IQ-related issues, yet such failures will be at the CPS level, i.e., they are not expected to escalate since such CPS(s) are not considered to be a part of a CPSoS.

  2. The tool and a manual on how the profile can be imported and used to create, correct, and verify the models are downloadable at https://cutt.ly/BcD7LZ1.

  3. http://www.progetto-sister.com/.

  4. A similar system for railways has been proposed in [44].

  5. We have adopted the notion of binary requirement satisfaction as it facilitates dealing with complex requirements such as IQ requirements.

  6. In our work, consistency refers only to value consistency [10].

  7. Most of the dependability means have been defined based on the best practices for engineering safety [52].

  8. We have defined similar constraints for all the dependencies our Profile proposes.

  9. http://www.progetto-sister.com/.

  10. The tool is available at https://cutt.ly/BcD7LZ1.

  11. https://www.eclipse.org/papyrus/.

References

  1. Ali S, Iqbal MZ, Arcuri A, Briand L (2011) A search-based OCL constraint solver for model-based test data generation. In: Proceedings—international conference on quality software, https://doi.org/10.1109/QSIC.2011.17

  2. Andrews Z, Fitzgerald J, Payne R, Romanovsky A (2013) Fault modelling for systems of systems. In: 11th International symposium on autonomous decentralized systems, ISADS 2013. IEEE, August, pp 1–8. https://doi.org/10.1109/ISADS.2013.6513445

  3. Avizienis A, Laprie JC, Randell B, Landwehr C (2004) Basic concepts and taxonomy of dependable and secure computing. IEEE Trans Dependable Secure Comput 1(1):11–33. https://doi.org/10.1109/TDSC.2004.2

    Article  Google Scholar 

  4. Ballou D, Wang R, Pazer H, Tayi GK (1998) Modeling information manufacturing systems to determine information product quality. Manage Sci 44(4):462–484. https://doi.org/10.1287/mnsc.44.4.462

    Article  MATH  Google Scholar 

  5. Basili V, Caldiera C, Rombach HD (1994) Goal question metric paradigm. In: Marciniak JJ (ed) Encyclopedia of software engineering, vol 1. Wiley, Hoboken, pp 528–532

    Google Scholar 

  6. Bell D, De Cesare S, Iacovelli N, Lycett M, Merico A (2007) A framework for deriving semantic web services. Inf Syst Front 9(1):69–84, https://doi.org/10.1007/s10796-006-9018-z, arXiv:1011.1669v3

  7. Bernardi S, Merseguer J, Petriu DC (2011) A dependability profile within MARTE. Softw Syst Model 10(3):313–336. https://doi.org/10.1007/s10270-009-0128-1

    Article  Google Scholar 

  8. Bondavalli A, Dal Cin M, Latella D, Majzik I, Pataricza A, Savoia G (2001) Dependability analysis in the early phases of UML-based system design. Comput Syst Sci Eng 16(5):265–275

    Google Scholar 

  9. Bondavalli A, Bouchenak S, Kopetz H (2016) Cyber-physical systems of systems, vol 10099. Springer, Berlin. https://doi.org/10.1007/978-3-319-47590-5

    Book  Google Scholar 

  10. Bovee M, Srivastava R, Mak B (2001) A conceptual framework and belief-function approach to assessing overall information quality. Int J Intell Syst 18(1):51–74

    Article  Google Scholar 

  11. Burmester M, Magkos E, Chrissikopoulos V (2012) Modeling security in cyber-physical systems. Int J Crit Infrastruct Prot 5(3–4):118–126. https://doi.org/10.1016/j.ijcip.2012.08.002

    Article  Google Scholar 

  12. Cappiello C, Milano P, Elettronica D, Leonardo P, Caro A, Rodriguez A, Caballero I (2013) An approach to design business processes addressing data quality issues. In: 21st European conference on information systems (ECIS), pp 1–12

  13. Dal Cin M (2003) Extending UML towards a useful OO-language for modeling dependability features. In: Proceedings—international workshop on object-oriented real-time dependable systems, WORDS, pp 325–330, https://doi.org/10.1109/WORDS.2003.1267547

  14. Efatmaneshnik M, Ryan M (2014) Failure propagation in SoS: Why SoS should be loosely coupled. In: Proceedings of the 9th international conference on system of systems engineering: the socio-technical perspective, SoSE 2014. IEEE, pp 49–54, https://doi.org/10.1109/SYSOSE.2014.6892462

  15. Egyed A (2004) Consistent adaptation and evolution of class diagrams during refinement. In: Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics), vol 2984. Springer, pp 37–53, https://doi.org/10.1007/978-3-540-24721-0_3

  16. Fisher CW, Kingma BR (2001) Criticality of data quality as exemplifed in two disasters. Inf Manage 39(2):109–116. https://doi.org/10.1016/S0378-7206(01)00083-0

    Article  Google Scholar 

  17. de França BBN, Travassos GH (2016) Simulation based studies in software engineering: a matter of validity. CLEI Electron J 18(1):4:1–4:18. https://doi.org/10.19153/cleiej.18.1.4

    Article  Google Scholar 

  18. Gasser U, Eppler M, Helfert M (2004) Information quality: organizational, technological, and legal perspectives. Stud Commun Sci 2(4):1–16

    Google Scholar 

  19. Gharib M, Giorgini P (2013) Analysing information integrity requirements in safety critical systems. CEUR Worksh Proc 978:85–90

    Google Scholar 

  20. Gharib M, Giorgini P (2015) Analyzing trust requirements in socio-technical systems: a belief-based approach. In: Lecture Notes in Business Information Processing, vol 235. Springer, pp 254–270. https://doi.org/10.1007/978-3-319-25897-3_17

  21. Gharib M, Giorgini P (2015) Dealing with information quality requirements. In: Lecture notes in business information processing, vol 214. Springer, pp 379–394. https://doi.org/10.1007/978-3-319-19237-6_24

  22. Gharib M, Giorgini P (2015) Modeling and reasoning about information quality requirements. In: Requirements engineering: foundation for software quality, vol 9013. Springer, pp 49–64. https://doi.org/10.1007/978-3-319-19237-6_15

  23. Gharib M, Giorgini P (2019) Information quality requirements engineering with STS-IQ. Inf Softw Technol 107:83–100. https://doi.org/10.1016/j.infsof.2018.11.002

    Article  Google Scholar 

  24. Gharib M, Lollini P, Bondavalli A (2017) A conceptual model for analyzing information quality in system-of-systems. In: 12th System of systems engineering conference, SoSE17, IEEE, pp 1–6, https://doi.org/10.1109/SYSOSE.2017.7994946

  25. Gharib M, Giorgini P, Mylopoulos J (2018) Analysis of information quality requirements in business processes, revisited. Requir Eng 23(2):227–249. https://doi.org/10.1007/s00766-016-0264-4

    Article  Google Scholar 

  26. Gogolla M, Vallecillo-Moreno A (2019) On softening OCL invariants. J Obj Technol 18(2):1–22

    Article  Google Scholar 

  27. Hevner March, Park Ram (2017) Design science in information systems research. MIS Q 28(1):75. https://doi.org/10.2307/25148625

    Article  Google Scholar 

  28. Humayed A, Lin J, Li F, Journal BLIIOT (2017) U (2017) Cyber-physical systems security-a survey. IEEE Internet Things J 4(6):1802–1831

    Article  Google Scholar 

  29. International Organization for Standardization/International Electrotechnical Commission (1991) Software product evaluation: quality characteristics and guidelines for their use. ISO/IEC 9126, ISO/IEC

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

  31. International Organization for Standardization/International Electrotechnical Commission and others (2011) ISO/IEC 25010 systems and software engineering system and software product quality requirements and evaluation (SQuaRE)

  32. Jamshidi M (2008) System of systems engineering: new challenges for the 21st century. IEEE Aerosp Electron Syst Mag 23(5):4–19. https://doi.org/10.1109/MAES.2008.4523909

    Article  Google Scholar 

  33. Järvinen P (2007) On reviewing of results in design research. In: Proceedings of the 15th European conference on information systems, ECIS 2007, pp 1388–1397

  34. Jedlitschka A, Pfahl D (2005) Reporting guidelines for controlled experiments in software engineering. In: 2005 International symposium on empirical software engineering, ISESE 2005, pp 95–104, https://doi.org/10.1109/ISESE.2005.1541818

  35. Karkouch A, Mousannif H, Al Moatassime H, Noel T (2018) A model-driven framework for data quality management in the Internet of Things. J Ambient Intell Human Comput 9(4):977–998. https://doi.org/10.1007/s12652-017-0498-0

    Article  Google Scholar 

  36. Kirchen I, Schutz D, Folmer J, Vogel-Heuser B (2017) Metrics for the evaluation of data quality of signal data in industrial processes. In: Proceedings—2017 IEEE 15th international conference on industrial informatics, INDIN 2017, pp 819–826. https://doi.org/10.1109/INDIN.2017.8104878

  37. Kitchenham B, Al-Khilidar H, Babar MA, Berry M, Cox K, Keung J, Kurniawati F, Staples M, Zhang H, Zhu L (2008) Evaluating guidelines for reporting empirical software engineering studies. Empir Softw Eng 13(1):97–121. https://doi.org/10.1007/s10664-007-9053-5

    Article  Google Scholar 

  38. Kohn A, Kasmeyer M, Schneider R, Roger A, Stellwag C, Herkersdorf A (2015) Fail-operational in safety-related automotive multi-core systems. In: 2015 10th IEEE international symposium on industrial embedded systems, SIES 2015—Proceedings, pp 144–147, https://doi.org/10.1109/SIES.2015.7185051

  39. Kopetz H (2014) A conceptual model for the information transfer in systems-of-systems. In: Proceedings—IEEE 17th international symposium on object/component/service-oriented real-time distributed computing, ISORC 2014, IEEE, June, pp 17–24, https://doi.org/10.1109/ISORC.2014.19

  40. Kopetz H, Frömel B, Höftberger O (2015) Direct versus stigmergic information flow in systems-of-systems. In: System of systems engineering conference (SoSE), 2015 10th, IEEE, pp 36–41

  41. Kuhlmann M, Gogolla M (2012) From UML and OCL to relational logic and back. In: Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics), vol 7590 LNCS, pp 415–431, https://doi.org/10.1007/978-3-642-33666-9_27

  42. Kuhlmann M, Hamann L, Gogolla M (2011) Extensive validation of OCL models by integrating SAT solving into USE. In: Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics), vol 6705 LNCS, pp 290–306, https://doi.org/10.1007/978-3-642-21952-8_21

  43. Lagarde F, Espinoza H, Terrier F, Gérard S (2007) Improving uml profile design practices by leveraging conceptual domain models. In: Proceedings of the twenty-second IEEE/ACM international conference on automated software engineering. ACM Press, New York, p 445, https://doi.org/10.1145/1321631.1321705

  44. Lehner A, Müller FDP, Strang T, García CR (2009) Reliable vehicle-autarkic collision detection for rail-bound transportation. ITS World Congr 2009:1–8

    Google Scholar 

  45. Liu L, Chi L (2002) Evolutional data quality: a theory-specific view. In: 7th International conference on information quality ICIQ, pp 292–304

  46. Lu T, Zhao J, Zhao L, Li Y, Zhang X (2015) Towards a framework for assuring cyber physical system security. Int J Sec Appl 9(3):25–40. https://doi.org/10.14257/ijsia.2015.9.3.04

    Article  Google Scholar 

  47. Maier MW (1998) Architecting principles for systems-of-systems. Syst Eng 1(4):267–284

    Article  Google Scholar 

  48. Mamatha G, Sharma SC (2013) A highly secured approach against attacks in MANETS. Int J Comput Theory Eng 2(5):815–819. https://doi.org/10.7763/ijcte.2010.v2.246

    Article  Google Scholar 

  49. Melendez WA (1999) The upper layers of the ISO/OSI reference model (Part II). Comput Stand Interfaces 20(4–5):185–199. https://doi.org/10.1016/s0920-5489(98)00057-9

    Article  Google Scholar 

  50. Melendez Wilfred A, Petersen EL (1986) The upper layers of the ISO/OSI reference model (part I). Comput Stand Interfaces 5(1):13–46

    Article  Google Scholar 

  51. Mettler T, Eurich M, Winter R (2014) On the use of experiments in design science research: a proposition of an evaluation framework. Commun Assoc Inf Syst 34(1):223–240. https://doi.org/10.17705/1cais.03410

    Article  Google Scholar 

  52. Möller N, Hansson SO (2008) Principles of engineering safety: risk and uncertainty reduction. Reliab Eng Syst Saf 93(6):798–805. https://doi.org/10.1016/j.ress.2007.03.031

    Article  Google Scholar 

  53. Montecchi L, Lollini P, Bondavalli A (2011) Dependability concerns in model-driven engineering. In: Proceedings—2011 14th IEEE international symposium on object/component/service-oriented real-time distributed computing workshops, ISORCW 2011. IEEE, pp 254–263, https://doi.org/10.1109/ISORCW.2011.32

  54. Montrieux L (2013) Model-based analysis of role-based access control. PhD thesis, The Open University, http://oro.open.ac.uk/38672/

  55. Mori M, Ceccarelli A, Lollini P, Frömel B, Brancati F, Bondavalli A (2018) Systems-of-systems modeling using a comprehensive viewpoint-based SysML profile. J Softw Evol Process. https://doi.org/10.1002/smr.1878

    Article  Google Scholar 

  56. 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

  57. OMG-OCL (2014) Object constraint language. Rep May Tech https://doi.org/10.1167/7.9.852

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

    Article  Google Scholar 

  59. Rajkumar R, Lee I, Sha L, Stankovic J (2010) Cyber-physical systems: the next computing revolution. In: Proceedings—design automation conference, pp 731–736, https://doi.org/10.1145/1837274.1837461

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

  61. Richters M, Gogolla M (2002) OCL: syntax, semantics, and tools. pp 42–68, https://doi.org/10.1007/3-540-45669-4_4

  62. Scannapieco M, Pernici B, Pierce E (2002) IP-UML: Towards a methodology for quality improvement based on the IP-Map framework. In: Proceedings of the 7th international conference on information quality (ICIQ-02), pp 279–291

  63. Selic B (2007) A systematic approach to domain-specific language design using UML. In: 10th IEEE international symposium on object and component-oriented real-time distributed computing, ISORC 2007. IEEE, pp 2–9, https://doi.org/10.1109/ISORC.2007.10

  64. Sha K, Zeadally S (2015) Data quality challenges in cyber-physical systems. J Data Inf Qual. https://doi.org/10.1145/2740965

    Article  Google Scholar 

  65. Song Z, Sun Y, Wan J, Liang P (2017) Data quality management for service-oriented manufacturing cyber-physical systems. Comput Electr Eng 64:1339–1351. https://doi.org/10.1016/j.compeleceng.2016.08.010

    Article  Google Scholar 

  66. Venable J, Pries-Heje J, Baskerville R (2012) A comprehensive framework for evaluation in design science research. In: International conference on design science research in information systems. Springer, pp 423–438

  67. Wang RY (2002) A product perspective on total data quality management. Commun ACM 41(2):58–65. https://doi.org/10.1145/269012.269022

    Article  Google Scholar 

  68. Wang RY, Shankaranarayanan G (2000) IP-MAP : Representing the manufacture of an information product. In: Proceedings of the conference on information quality, pp 1–16

  69. Wieringa R (2009) Design science as nested problem solving. In: Proceedings of the 4th international conference on design science research in information systems and technology, DESRIST ’09 pp 1–12, https://doi.org/10.1145/1555619.1555630

  70. Wohlin C, Runeson P, Höst M, Ohlsson MC, Regnell B, Wesslén A (2012) Experimentation in software engineering. Springer, Berlin

    Book  Google Scholar 

  71. Wu L, Kaiser G, Rudin C, Anderson R (2011) Data quality assurance and performance measurement of data mining for preventive maintenance of power grid. In: Proceedings of the 1st international workshop on data mining for service and maintenance, KDD4 service-held in conjunction with SIGKDD’11, pp 28–32, https://doi.org/10.1145/2018673.2018679

Download references

Funding

This work has received funding from the REGIONE TOSCANA POR FESR 2014–2020 SISTER “SIgnaling and Sensing Technologies in Railway application” and the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie Grant Agreement No. 823788—ADVANCE project.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mohamad Gharib.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Code availability

Not applicable

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gharib, M., Lollini, P. & Bondavalli, A. IQCPSoS: A Model-Based Approach for Modeling and Analyzing Information Quality Requirements for Cyber-Physical System-of-Systems. J Data Semant 10, 267–289 (2021). https://doi.org/10.1007/s13740-021-00129-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13740-021-00129-8

Keywords

Navigation