Advertisement

Toward Problem Solving Support Based on Big Data and Domain Knowledge: Interactive Granular Computing and Adaptive Judgement

  • Andrzej SkowronEmail author
  • Andrzej Jankowski
  • Soma Dutta
Chapter
Part of the Studies in Big Data book series (SBD, volume 16)

Abstract

Nowadays efficient methods for dealing with Big Data are urgently needed for many real-life applications. Big Data is often distributed over networks of agents involved in complex interactions. Decision support for users, to solve problems using Big Data, requires to develop relevant computation models for the agents as well as methods for incorporating changes in the reasoning of the computation models themselves; these requirements would enable agents to control computations for achieving the target goals. It is to be noted that users are also agents. Agents are performing computations on complex objects of very different natures (e.g., (behavioral) patterns, classifiers, clusters, structural objects, sets of rules, aggregation operations, reasoning schemes etc.). One of the challenges for systems based on Big Data is to provide the systems with high-level primitives of users for composing and building complex analytical pipelines over Big Data. Such primitives are very often expressed in natural language, and they should be approximated using low-level primitives, accessible from raw data. In Granular Computing (GrC), all such constructed and/or induced objects are called granules. To model interactive computations, performed by the agent in complex systems based on Big Data, we extend the existing approach to GrC by introducing complex granules (c-granules or granules, for short). Many advanced tasks, concerning complex systems based on Big Data may be classified as control tasks performed by agents aiming at achieving the high quality trajectories (defined by computations) relative to the considered target tasks and quality measures. Here, new challenges are to develop strategies to control, predict, and bound the behavior of the system based on Big Data at scale. We propose to investigate these challenges using the GrC framework. The reasoning, which aims at controlling the computational schemes from time-to-time, in order to achieve the required target, is called an adaptive judgement. This reasoning deals with granules and computations over them. Adaptive judgement is more than a mixture of reasoning based on deduction, induction and abduction. Due to the uncertainty the agents generally cannot predict exactly the results of actions (or plans). Moreover, the approximations of the complex vague concepts initiating actions (or plans) are drifting with time. Hence, adaptive strategies for evolving approximation of concepts with respect to time are needed. In particular, the adaptive judgement is very much needed in the efficiency management of granular computations, carried out by agents, for risk assessment, risk treatment, cost/benefit analysis. The approach, discussed in this paper, is a step towards realization of the Wisdom Technology (WisTech) program [2, 3], and is developed over years of experiences, based on the work on different real-life projects.

Keywords

Rough set (Interactive) granular computing Interactive computation Adaptive judgement Efficiency management Risk management Cost/benefit analysis Big data technology Cyber-physical system Wisdom web of things Ultra-large system 

References

  1. 1.
    Berman, J.J.: Principles of Big Data. Sharing, and Analyzing Complex Information. Elsevier, Amsterdam, Preparing (2013)Google Scholar
  2. 2.
    Jankowski, A.: Complex Systems Engineering: Conclusions from Practical Experience. Springer, Heidelberg (2015). (in preparation)Google Scholar
  3. 3.
    Jankowski, A., Skowron, A.: A WisTech paradigm for intelligent systems. Trans. Rough Sets VI: J. Subline 94–132Google Scholar
  4. 4.
    Arthur, L.: Big Data Marketing. Wiley, Hoboken (2013)Google Scholar
  5. 5.
    Chu, W.W. (ed.): Data Mining and Knowledge Discovery for Big Data Methodologies. Challenges and Opportunities. Springer, Berlin (2014)Google Scholar
  6. 6.
    Kudyba, S. (ed.): Big Data, Mining, and Analytics: Components of Strategic Decision Making. CRC Press Taylor & Francis, Boca Raton (2014)Google Scholar
  7. 7.
    Mayer-Schönberger, V., Cukier, K.: Big Data: A Revolution That Will Transform How We Live, Work, and Think. John Murray Pub, London (2013)Google Scholar
  8. 8.
    O’Reilly Media, I.T.: Big Data Now: 2012 Edition. O’Reilly Media, Inc., Sebastopol (2012)Google Scholar
  9. 9.
    Pollak, B. (ed.): Ultra-Large-Scale Systems. Carnegie Mellon University, Pittsburgh, PA, The Software Challenge of the Future. Software Engineering Institute (2006)Google Scholar
  10. 10.
    Schmarzo, B.: Big Data: Understanding How Data Powers Big Business. Wiley, Indianapolis (2013)Google Scholar
  11. 11.
    Zikopoulos, P.C., Eaton, C., deRoos, D., Deutsch, T., Lapis, G.: Understanding Big Data. Analytics from Enterprise Class Hadoop and Streaming Data. McGraw-Hill, New York (2012)Google Scholar
  12. 12.
    Lamnabhi-Lagarrigue, F., Di Benedetto, M.D., Schoitsch, E.: Introduction to the special theme cyber-physical systems. Ercim News 94, 6–7 (2014)Google Scholar
  13. 13.
    Zhong, N., Ma, J.H., Huang, R., Liu, J., Yao, Y., Zhang, Y.X., Chen, J.: Research challenges and perspectives on wisdom web of things (W2T). J. Supercomput. 64, 862–882 (2013)CrossRefGoogle Scholar
  14. 14.
    Cyber-physical and ultra-large scale systems (2013), http://resources.sei.cmu.edu/library/asset-view.cfm?assetid=85282
  15. 15.
    Zadeh, L.A.: Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets Syst. 90, 111–127 (1997)MathSciNetCrossRefzbMATHGoogle Scholar
  16. 16.
    Bargiela, A., Pedrycz, W. (eds.): Granular Computing: An Introduction. Kluwer Academic Publishers (2003)Google Scholar
  17. 17.
    Pedrycz, W., Skowron, S., Kreinovich, V. (eds.): Handbook of Granular Computing. Wiley, Hoboken (2008)Google Scholar
  18. 18.
    Pedrycz, W.: Granular Computing Analysis and Design of Intelligent Systems. CRC Press, Taylor & Francis, Boca Raton (2013)CrossRefGoogle Scholar
  19. 19.
    Skowron, A., Pal, S.K., Nguyen, H.S. (eds.): Special issue on rough sets and fuzzy sets in natural computing. Theor. Comput. Sci. 412(42), (2011)Google Scholar
  20. 20.
    Jagadish, H., Gehrke, J., Labrinidis, A., Papakonstantinou, Y., Patel, J.M., Ramakrishnan, R., Shahabi, C.: Big data and its technical challenges. Commun. ACM 57, 86–94 (2014)CrossRefGoogle Scholar
  21. 21.
    Pfeifer, R., Lungarella, M., Iida, F.: Self-organization, embodiment, and biologically inspired robotic. Science 318, 1088–1093 (2007). NovemberCrossRefGoogle Scholar
  22. 22.
    Amershi, S., Cakmak, M., Knox, W.B., Kulesza, T.: Power to the people: the role of humans in interactive machine learning. AI Mag. 35, 105–120 (Winter 2014)Google Scholar
  23. 23.
    Bazan, J.: Hierarchical classifiers for complex spatio-temporal concepts. Trans. Rough Sets IX: J. Subline LNCS 5390, 474–750 (2008)CrossRefGoogle Scholar
  24. 24.
    Nguyen, S.H., Bazan, J., Skowron, A., Nguyen, H.S.: Layered learning for concept synthesis. Trans. Rough Sets I: J. Subline LNCS 3100, 187–208 (2004)CrossRefzbMATHGoogle Scholar
  25. 25.
    Deutsch, D., Ekert, A., Lupacchini, R.: Machines, logic and quantum physics. Bull. Symbolic Logic 6, 265–283 (2000)MathSciNetCrossRefzbMATHGoogle Scholar
  26. 26.
    Goldin, D., Smolka, S., Wegner, P. (eds.): Interactive Computation: The New Paradigm. Springer (2006)Google Scholar
  27. 27.
    Mendel, J.M., Zadeh, L.A., Trillas, E., Yager, R., Lawry, J., Hagras, H., Guadarrama, S.: What computing with words means to me. IEEE Comput. Intell. Mag. 20–26 (February 2010)Google Scholar
  28. 28.
    Zadeh, A.: Computing with Words: Principal Concepts and Ideas, Studies in Fuzziness and Soft Computing, vol. 277. Springer, Heidelberg (2012)CrossRefzbMATHGoogle Scholar
  29. 29.
    Zadeh, L.A.: Fuzzy logic = computing with words. IEEE Trans. Fuzzy Syst. 4, 103–111 (1996)CrossRefGoogle Scholar
  30. 30.
    Zadeh, L.A.: From computing with numbers to computing with words—from manipulation of measurements to manipulation of perceptions. IEEE Trans. Circuits Syst. 45, 105–119 (1999)MathSciNetCrossRefzbMATHGoogle Scholar
  31. 31.
    Zadeh, L.A.: Foreword. In: Pal et al. [48], pp. IX–XIGoogle Scholar
  32. 32.
    Zadeh, L.A.: A new direction in AI: toward a computational theory of perceptions. AI Mag. 22(1), 73–84 (2001)zbMATHGoogle Scholar
  33. 33.
    Zadeh, L.A.: Fuzzy sets and information granularity. In: Advances in Fuzzy Set Theory and Applications, pp. 3–18. North-Holland, Amsterdam (1979)Google Scholar
  34. 34.
    Skowron, A., Stepaniuk, J.: Information granules and rough-neural computing. In: Pal et al. [48], pp. 43–84Google Scholar
  35. 35.
    Jankowski, A., Skowron, A., Swiniarski, R.W.: Interactive computations: toward risk management in interactive intelligent systems. In: Maji, P., Ghosh, A., Murty, M.N., Ghosh, K., Pal, S.K. (eds.) Pattern Recognition and Machine Intelligence—5th International Conference, PReMI 2013, Kolkata, India, December 10–14, 2013. Proceedings. Lecture Notes in Computer Science, vol. 8251, pp. 1–12. Springer (2013)Google Scholar
  36. 36.
    Jankowski, A., Skowron, A., Swiniarski, R.W.: Interactive complex granules. Fundamenta Informaticae 133, 181–196 (2014)Google Scholar
  37. 37.
    Jankowski, A., Skowron, A., Swiniarski, R.W.: Perspectives on uncertainty and risk in rough sets and interactive rough-granular computing. Fundamenta Informaticae 129, 69–84 (2014)MathSciNetzbMATHGoogle Scholar
  38. 38.
    Skowron, A., Jankowski, A., Wasilewski, P.: Risk management and interactive computational systems. J. Adv. Math. Appl. 1, 61–73 (2012)Google Scholar
  39. 39.
    ISO 31000 standard, http://webstore.ansi.org/
  40. 40.
    Pearl, J.: Causal inference in statistics: an overview. Stat. Surv. 3, 96–146 (2009)MathSciNetCrossRefzbMATHGoogle Scholar
  41. 41.
    Skowron, A., Wasilewski, P.: An introduction to perception based computing. In: Kim, T.H., Lee, Y.H., Kang, B.H., Ślȩzak, D. (eds.) Proceedings of FGIT 2010. Lectures Notes in Computer Science, vol. 6485, pp. 12–25. Springer, Heidelberg (2010)Google Scholar
  42. 42.
    Skowron, A., Wasilewski, P.: Interactive information systems: toward perception based computing. Theor. Comput. Sci. 454, 240–260 (2012)MathSciNetCrossRefzbMATHGoogle Scholar
  43. 43.
    Zadeh, L.A.: Computing with words and perceptions a paradigm shift. In: Proceedings of the IEEE International Conference on Information Reuse and Integration (IRI 2009), Las Vegas, Nevada, USA. pp. viii–x. IEEE Systems, Man, and Cybernetics Society (2009)Google Scholar
  44. 44.
    Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. The MIT Press (1998)Google Scholar
  45. 45.
    Bower, J.M., Bolouri, H. (eds.): Computational Modeling of Genetic and Biochemical Networks. MIT Press (2001)Google Scholar
  46. 46.
    Press, Harvard Business School: SWOT Analysis I: Looking Outside for Threats and Opportunities. Harvard Business School Publishing Corporation, Boston (2006)Google Scholar
  47. 47.
    Press, Harvard Business School: SWOT Analysis II: Looking Inside for Strengths and Weaknesses. Harvard Business School Publishing Corporation, Boston (2006)Google Scholar
  48. 48.
    Osterwalder, A., Pigneur, Y.: Business Model Generation: A Handbook for Visionaries, Game Changers, and Challengers. Wiley, Hoboken (2010)Google Scholar
  49. 49.
    Pahl, N., Richter, A.: Swot Analysis. Methodology and a Practical Approach. GRIN Verlag GmbH, Münich, Idea (2009)Google Scholar
  50. 50.
    Imai, M., Kaizen, G.: A Commonsense Approach to a Continuous Improvement Strategy, 2nd edn. McGraw-Hill Professional, New York (2012)Google Scholar
  51. 51.
    Sobek II, D.K., Smalley, A.: Understanding A3 Thinking: A Critical Component of Toyota’s PDCA Management System. Productivity Press, Boca Raton (2008)Google Scholar
  52. 52.
    Rozenberg, G., Bäck, T., Kok, J. (eds.): Handbook of Natural Computing. Springer (2012)Google Scholar
  53. 53.
    Jankowski, A., Skowron, A.: Wisdom technology: a rough-granular approach. In: Marciniak, M., Mykowiecka, A. (eds.) Bolc Festschrift. Lectures Notes in Computer Science, vol. 5070, pp. 3–41. Springer, Heidelberg (2009)Google Scholar
  54. 54.
    Skowron, A., Stepaniuk, J., Swiniarski, R.: Modeling rough granular computing based on approximation spaces. Inf. Sci. 184, 20–43 (2012)CrossRefzbMATHGoogle Scholar
  55. 55.
    Skowron, A., Wasilewski, P.: Information systems in modeling interactive computations on granules. Theor. Comput. Sci. 412(42), 5939–5959 (2011)MathSciNetCrossRefzbMATHGoogle Scholar
  56. 56.
    Heller, M.: The Ontology of Physical Objects. Cambridge University Press, Four Dimensional Hunks of Matter. Cambridge Studies in Philosophy (1990)Google Scholar
  57. 57.
    Vapnik, V.: Statistical Learning Theory. Wiley, New York (1998)zbMATHGoogle Scholar
  58. 58.
    Omicini, A., Ricci, A., Viroli, M.: The multidisciplinary patterns of interaction from sciences to computer science. In: Goldin et al. [18], pp. 395–414Google Scholar
  59. 59.
    Einstein, A.: Geometrie und Erfahrung (Geometry and Experience). Julius Springer, Berlin (1921)CrossRefzbMATHGoogle Scholar
  60. 60.
    Pawlak, Z., Skowron, A.: Rudiments of rough sets. Inf. Sci. 177(1), 3–27 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  61. 61.
    Pawlak, Z.: Rough sets. Int. J. Comput. Inf. Sci. 11, 341–356 (1982)CrossRefzbMATHGoogle Scholar
  62. 62.
    Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning about Data, System Theory, Knowledge Engineering and Problem Solving, vol. 9. Kluwer Academic Publishers, Dordrecht (1991)CrossRefGoogle Scholar
  63. 63.
    Stepaniuk, J.: Rough-Granular Computing in Knowledge Discovery and Data Mining. Springer, Heidelberg (2008)zbMATHGoogle Scholar
  64. 64.
    Skowron, A., Stepaniuk, J., Jankowski, A., Bazan, J.G., Swiniarski, R.: Rough set based reasoning about changes. Fundamenta Informaticae 119(3–4), 421–437 (2012)MathSciNetGoogle Scholar
  65. 65.
    Abbott, D.: Applied Predictive Analytics: Principles and Techniques for the Professional Data Analyst. Wiley, Indianapolis (2014)Google Scholar
  66. 66.
    Bartlett, R.: A Practitioner’s Guide To Business Analytics: Using Data Analysis Tools to Improve Your Organization’s Decision Making and Strategy. McGraw-Hill, New York (2013)Google Scholar
  67. 67.
    Provost, F., Fawcett, T.: Data Science for Business: What You Need to Know About Data Mining and Data-analytic Thinking. O’Reilly Media, Sebastopol (2013)Google Scholar
  68. 68.
    Marr, B.: Big Data: Using SMART Big Data. Analytics and Metrics to Make Better Decisions and Improve Performance. Wiley, Hoboken (2015)Google Scholar
  69. 69.
    Siegel, E.: Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die. Wiley, Hoboken (2013)Google Scholar
  70. 70.
    Staab, S., Studer, R. (eds.): Handbook on Ontologies. International Handbooks on Information Systems. Springer, Heidelberg (2004)Google Scholar
  71. 71.
    Polkowski, L., Skowron, A.: Rough mereology: a new paradigm for approximate reasoning. Int. J. Approximate Reasoning 15(4), 333–365 (1996)MathSciNetCrossRefzbMATHGoogle Scholar
  72. 72.
    Polkowski, L., Skowron, A.: Towards adaptive calculus of granules. In: Zadeh, L.A., Kacprzyk, J. (eds.) Computing with Words in Information/Intelligent Systems, pp. 201–227. Physica-Verlag, Heidelberg (1999)CrossRefGoogle Scholar
  73. 73.
    Polkowski, L., Skowron, A.: Rough mereological calculi of granules: a rough set approach to computation. Comput. Intell. Int. J. 17(3), 472–492 (2001)MathSciNetGoogle Scholar
  74. 74.
    Noë, A.: Action in Perception. MIT Press (2004)Google Scholar
  75. 75.
    Skowron, A., Stepaniuk, J., Peters, J., Swiniarski, R.: Calculi of approximation spaces. Fundamenta Informaticae 72, 363–378 (2006)MathSciNetzbMATHGoogle Scholar
  76. 76.
    Skowron, A., Stepaniuk, J.: Hierarchical modelling in searching for complex patterns: constrained sums of information systems. J. Exp. Theor. Artif. Intell. 17, 83–102 (2005)CrossRefzbMATHGoogle Scholar
  77. 77.
    Desai, A.: Adaptive complex enterprises. Commun. ACM 45, 32–35 (2005)CrossRefGoogle Scholar
  78. 78.
    Liu, J.: Autonomous Agents and Multi-Agent Systems: Explorations in Learning, Self-organization and Adaptive Computation. World Scientific Publishing (2001)Google Scholar
  79. 79.
    Hilbert, D.: Mathematische probleme. Nachr. Akad. Wiss. Göttingen, pp. 253–297 (1900), (Gesammelte Abhandlungen,. Bd. 3, Springer, Berlin, 1935, pp. 290–329)Google Scholar
  80. 80.
    Vitushkin, A.G.: On Hilbert’s thirteenth problem. Dokl. Acad. Nauk. SSSR 156, 1003–1006 (1954)Google Scholar
  81. 81.
    Estep, M.: Self-organizing Natural Intelligence: Issues of Knowing, Meaning, and Complexity. Springer, Heidelberg (2014)zbMATHGoogle Scholar
  82. 82.
    Holland, J.: Signals and Boundaries Building Blocks for Complex Adaptive Systems. MIT Press, Cambridge (2014)Google Scholar
  83. 83.
    Jarrah, K., Guan, L., Kyan, M., Muneesawang, P.: Unsupervised Learning: A Dynamic Approach. IEEE Press Series on Computational Intelligence, Wiley-IEEE Press, Hoboken (2014)Google Scholar
  84. 84.
    Nolfi, S., Fioreano, D.: Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-organizing Machines. MIT Press, Cambridge (2000)Google Scholar
  85. 85.
    Martin-Löf, P.: Intuitionistic Type Theory (Notes by Giovanni Sambin of a Series of Lectures Given in Padua, June 1980). Bibliopolis, Napoli (1984)zbMATHGoogle Scholar
  86. 86.
    Barwise, J., Seligman, J.: Information Flow: The Logic of Distributed Systems. Cambridge University Press (1997)Google Scholar
  87. 87.
    Rahwan, I., Simari, G.R.: Argumentation in Artificial Intelligence. Springer, Berlin (2009)Google Scholar
  88. 88.
    Polkowski, L., Skowron, A.: Rough mereological approach to knowledge-based distributed AI. In: Lee, J.K., Liebowitz, J., Chae, J.M. (eds.) Critical Technology, Proc. Third World Congress on Expert Systems, February 5–9, Soeul, Korea, pp. 774–781. Cognizant Communication Corporation, New York (1996)Google Scholar
  89. 89.
    Slovik, P., Cournède: Macroeconomic Impact of Basel III, Working Papers, vol. 844. OECD Economics Publishing, OECD Economics Department (2011), http://www.oecd.org/eco/Workingpapers
  90. 90.
    Shevchenko, P. (ed.): Modelling Operational Risk Using Bayesian Inference. Springer (2011)Google Scholar
  91. 91.
    Kahneman, D.: Maps of bounded rationality: psychology for behavioral economics. Am. Econ. Rev. 93, 1449–1475 (2002)CrossRefGoogle Scholar
  92. 92.
    Thiele, L.P.: The Heart of Judgment: Practical Wisdom, Neuroscience, and Narrative. Cambridge University Press, Cambridge (2010)Google Scholar
  93. 93.
    Pal, S.K., Polkowski, L., Skowron, A. (eds.): Rough-Neural Computing: Techniques for Computing with Words. Cognitive Technologies. Springer, Heidelberg (2004)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Andrzej Skowron
    • 1
    • 2
    Email author
  • Andrzej Jankowski
    • 3
  • Soma Dutta
    • 1
  1. 1.Institute of MathematicsWarsaw UniversityWarsawPoland
  2. 2.Systems Research InstitutePolish Academy of SciencesWarsawPoland
  3. 3.Knowledge Technology FoundationWarsawPoland

Personalised recommendations