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Technology of Complex Activity

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Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 86))

Abstract

In this chapter, using the results of Belov and Novikov (Methodology of complex activity. Lenand, Moscow, 320 pp., 2018, [1]), the technology control problem for the complex activity (CA) of organizational and technical systems (OTSs) is formalized.

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Notes

  1. 1.

    An activity is a purposeful behavior of a human. A complex activity is an activity with a nontrivial internal structure, with multiple and/or changing actors, technologies and roles of the subject matter in its relevant context [1].

    An organizational and technical system is a complex system that consists of humans, technical and natural elements.

  2. 2.

    We will consider an information model as a model of an object represented in the form of information that describes the significant parameters and variables of the object, the relations between them and also the inputs and outputs of the object. An information model can be used to simulate all possible states of an object by supplying information about its input variations.

  3. 3.

    The BPMN format uses the following notations: rounded rectangles as operations or actions; arrows as control flows––the sequences of transitions between actions; circles as different events (thin boundary––initial event; thick boundary––terminal event; double boundary––event of uncertainty occurring during action implementation); diamonds as control points––branching and merging of control flows, including parallel execution and conditions checking.

  4. 4.

    The particular cases of the lower CA elements are managerial activities––resources pools management, network planning and scheduling, interests coordination for different actors, regulation and evaluation (reflexion).

  5. 5.

    The staff and relations of nodes in structure (2) are somewhat conditional.

References

  1. Belov M, Novikov D (2018) Methodology of complex activity. Lenand, Moscow, 320 pp (in Russian)

    Google Scholar 

  2. Novikov D (2013) Theory of control in organizations. Nova Science Publishers, New York, 341 pp

    Google Scholar 

  3. Rebovich G, White B (2011) Enterprise systems engineering: advances in the theory and practice. CRC Press, Boca Raton, 459 pp

    Google Scholar 

  4. Novikov A, Novikov D (2007) Methodology. Sinteg, Moscow, 668 pp (in Russian)

    Google Scholar 

  5. Schwab K (2016) The fourth industrial revolution. World Economic Forum, Geneva, 172 pp

    Google Scholar 

  6. De Smet A, Lund S, Schaininger W (2016) Organizing for the future, McKinsey Q, Jan 2016. http://www.mckinsey.com/insights/organization/organizing-for-the-future

  7. ISO/IEC/IEEE 15288:2015 systems and software engineering—system life cycle processes

    Google Scholar 

  8. Business Process Model and Notation (BPMN), v2.0.2. http://www.omg.org/spec/BPMN/2.0

  9. Henriques D, Martins JG, Zuliani P, Platzer A, Clarke EM (2012) Statistical model checking for Markov decision processes. In: International Conference on Quantitative Evaluation of Systems (QEST), London, pp 84–93

    Google Scholar 

  10. Aichernig B, Schumi R (2017) Statistical model checking meets property-based testing. In: 2017 IEEE International Conference on Software Testing, Verification and Validation (ICST), Tokyo, pp 390–400

    Google Scholar 

  11. Alagoz H, German R (2017) A selection method for black box regression testing with a statistically defined quality level. In: 2017 IEEE International Conference on Software Testing, Verification and Validation (ICST), Tokyo, pp 114–125

    Google Scholar 

  12. Ali S, Yue T (2015) U-test: evolving, modelling and testing realistic uncertain behaviours of cyber-physical systems. In: 2015 IEEE 8th International Conference on Software Testing, Verification and Validation (ICST), Graz, pp 1–2

    Google Scholar 

  13. Bourque P, Fairley RE (eds) (2014) Guide to the software engineering body of knowledge, Version 3.0. IEEE Computer Society, 2014. www.swebok.org

  14. Legay A, Delahaye B, Bensalem S (2010) Statistical model checking: an overview. In: Barringer H et al (eds) Runtime Verification. RV 2010. Lecture notes in computer science, vol 6418. Springer, Berlin, pp 122–135

    Chapter  Google Scholar 

  15. Patrick M, Donnelly R, Gilligan C (2017) A toolkit for testing stochastic simulations against statistical oracles. In: 2017 IEEE International Conference on Software Testing, Verification and Validation (ICST), Tokyo, pp 448–453

    Google Scholar 

  16. Yizhen C et al (2017) Effective online software anomaly detection. In: Proceedings of the 26th ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA 2017), Santa Barbara, pp 136–146

    Google Scholar 

  17. Orseau L, Lattimore T, Hutter M (2013) Universal knowledge-seeking agents for stochastic environments. In: Jain S, Munos R, Stephan F, Zeugmann T (eds) Algorithmic Learning Theory. ALT 2013. Lecture notes in computer science, vol 8139, Springer, Berlin, pp 158–172

    Google Scholar 

  18. Stocia G, Stack B (2017) Acquired knowledge as a stochastic process. Surv Math Appl 12:65–70

    MathSciNet  Google Scholar 

  19. Wong A, Wang Y (2003) Pattern discovery: a data driven approach to decision support. IEEE Trans Syst Man Cybern 33(1):114–124

    Article  Google Scholar 

  20. Crawford J (1944) Learning curve, ship curve, ratios, related data. Lockheed Aircraft Corporation, pp 122–128

    Google Scholar 

  21. Henderson B (1984) The application and misapplication of the learning curve. J Bus Strategy 4:3–9

    Article  Google Scholar 

  22. Wright T (1936) Factors affecting the cost of airplanes. J Aeronaut Sci 3(4):122–128

    Article  Google Scholar 

  23. Chui D, Wong A (1986) Synthesizing knowledge: a cluster analysis approach using event covering. IEEE Trans Syst Man Cybern 16(2):251–259

    Article  Google Scholar 

  24. Leibowitz N, Baum B, Enden G, Karniel A (2010) The exponential learning equation as a function of successful trials results in sigmoid performance. J Math Psychol 54:338–340

    Article  MathSciNet  Google Scholar 

  25. Thurstone L (1919) The learning curve equation. Psychol Monogr 26(3):1–51

    Article  Google Scholar 

  26. Thurstone L (1930) The learning function. J Gen Psychol 3:469–493

    Article  Google Scholar 

  27. Sutton R, Barto A (2016) Reinforcement learning: an introduction. MIT Press, Massachusetts, 455 pp

    Google Scholar 

  28. van der Linden WJ, Hambleton RH (1996) Handbook of modern item response theory, Springer, New York, 512 pp

    Google Scholar 

  29. Shiryaev A (1973) Statistical sequential analysis: optimal stopping rules. American Mathematical Society, New York, 174 pp

    Google Scholar 

  30. Mikami S, Kakazu Y (1993) Extended stochastic reinforcement learning for the acquisition of cooperative motion plans for dynamically constrained agents. In: Proceedings of IEEE Systems Man and Cybernetics Conference (SMC), Le Touquet, vol 4, pp 257–262

    Google Scholar 

  31. Novikov D (1998) Laws of iterative learning. Trapeznikov Institute of Control Sciences RAS, Moscow, 98 pp (in Russian)

    Google Scholar 

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Correspondence to Mikhail V. Belov .

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Belov, M.V., Novikov, D.A. (2020). Technology of Complex Activity. In: Models of Technologies. Lecture Notes in Networks and Systems, vol 86. Springer, Cham. https://doi.org/10.1007/978-3-030-31084-4_1

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  • DOI: https://doi.org/10.1007/978-3-030-31084-4_1

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