PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Towards human consistent data driven decision support systems using verbalization of data mining results via linguistic data summaries

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
We present how the conceptually and numerically simple concept of a fuzzy linguistic database summary can be a very powerful tool for gaining much insight into the essence of data that may be relevant for a business activity. The use of linguistic summaries provides tools for the verbalization of data analysis (mining) results which, in addition to the more commonly used visualization e.g. via a GUI, graphical user interface, can contribute to an increased human consistency and ease of use. The results (knowledge) derived are in a simple, easily comprehensible linguistic form which can be effectively and efficiently employed for supporting decision makers via the data driven decision support system paradigm. Two new relevant aspects of the analysis are also outlined which was first initiated by the authors. First, following Kacprzyk and Zadrożny [1] comments are given on an extremely relevant aspect of scalability of linguistic summarization of data, using their new concept of a conceptual scalability that is crucial for large applications. Second, following Kacprzyk and Zadrożny [2] it is further considered how linguistic data summarization is closely related to some types of solutions used in natural language generation (NLG), which can make it possible to use more and more effective and efficient tools and techniques developed in this another rapidly developing area. An application of a computer retailer is outlined.
Słowa kluczowe
Rocznik
Strony
359--370
Opis fizyczny
Bibliogr. 47 poz., rys., tab.
Twórcy
autor
autor
  • Systems Research Institute, Polish Academy of Sciences, 6 Newelska St., 01-447 Warsaw, Poland
Bibliografia
  • [1] J. Kacprzyk and S. Zadrożny, “Linguistic data summarization: a high scalability through the use of natural language?”, Scalable Fuzzy Algorithms for Data Management and Analysis: Methods and Design 1, 214–237 (2009).
  • [2] J. Kacprzyk and S. Zadrożny, “Computing with words is an implementable paradigm: fuzzy queries, linguistic data summaries and natural language generation”, IEEE Trans. on Fuzzy Systems 18, 461–472 (2010).
  • [3] S.L. Alter, Decision Support Systems: Current Practice and Contributing Challenge, Addison-Wesley, Reading, 1990.
  • [4] C.W. Holsapple and A.B. Whinston, Decision Support Systems: a Knowldege-based Approach, West Publishing, Minneapolis, 1986.
  • [5] R.H. Sprague and H.J. Watson, Decision Support Systems for Management, Prentice-Hall, Englewood Cliffs, 1996.
  • [6] L.A. Zadeh and J. Kacprzyk, Computing with Words in Information/ Intelligent Systems 1. Foundations. 2. Applications, Physica-Verlag, New York, 1999.
  • [7] R.R. Yager, “A new approach to the summarization of data”, Information Sciences 28, 69–86 (1982).
  • [8] J. Kacprzyk and R.R. Yager, “Linguistic summaries of data using fuzzy logic”, Int. J. General Systems 30, 133–154 (2001).
  • [9] J. Kacprzyk, R.R. Yager, and S. Zadrożny, “A fuzzy logic based approach to linguistic summaries of databases”, Int. J. Applied Maths and Computer Science 10, 813–834 (2000).
  • [10] J. Kacprzyk and S. Zadrożny, “Linguistic database summaries and their protoforms: towards natural language based knowledge discovery tools”, Information Sciences 173 (4), 281–304 (2005).
  • [11] J. Kacprzyk and S. Zadrożny, “Protoforms of linguistic database summaries as a human consistent tool for using natural language in data mining”, Int. J. of Software Science and Computational Intelligence 1 (1) 100–111 (2009).
  • [12] E. Reiter and R. Dale, Building Natural Language Generation Systems, Cambridge Univ. Press, Cambridge, 2000.
  • [13] J. Kacprzyk and P. Strykowski, “Linguitic summaries of sales data at a computer retailer: a case study”, Proc. IFSA’99 1, 29–33 (1999).
  • [14] S. Zadrożny and J. Kacprzyk, “On database summarization using a fuzzy querying interface”, Proc. IFSA’99 World Congress 1, 39–43 (1999).
  • [15] S. Zadrożny and J. Kacprzyk, “Summarizing the contents of Web server logs: a fuzzy linguistic approach”, Proc. IEEEIS’ 2007 1, 100–105 (2007).
  • [16] A.P. Wierzbicki, M. Makowski, and J. Wessels, Model-based Decision Support Methodology with Environmental Applications, Kluwer, Dordrecht, 2000.
  • [17] P.B. Checkland, Soft Systems Methodology in Action, Wiley, Chichester, 1990.
  • [18] P.B. Checkland, “Soft systems methodology. A thirty year retrospective”, in Soft Systems Methodology in Action, ed. P.B. Checkland and J. Scholes, Wiley, Chichester, 1999.
  • [19] R.R. Yager, “Database discovery using fuzzy sets”, Int. J. of Intelligent Systems 11, 691–712 (1996).
  • [20] P. Bosc, D. Dubois, O. Pivert, H. Prade, and M. de Calmes, “Fuzzy summarization of data using fuzzy cardinalities”, Proc. IPMU’2002 1, 1553–1559 (2002).
  • [21] D. Dubois and H. Prade, “Fuzzy sets in data summaries-outline of a new approach”, Proc. 8th Int. Conf. IPMU 2, 1035–1040 (2000).
  • [22] G. Raschia and N. Mouaddib, “SAINTETIQ: a fuzzy set-based approach to database summarization”, Fuzzy Sets and Systems 129, 137–162 (2002).
  • [23] D. Rasmussen and R.R. Yager, “SummarySQL-a fuzzy tool for data mining”, Intelligent Data Analysis – An International Journal 1, URL-http//:www-east.elsevier.com/ida/browse/96-6/ida96-6.htm (1997).
  • [24] D. Rasmussen and R.R. Yager, “Finding fuzzy and gradual functional dependencies with summary SQL”, Fuzzy Sets and Systems 106, 131–142 (1999).
  • [25] J. Kacprzyk and S. Zadrożny, “On interactive linguistic summarization of databases via a fuzzy-logic-based querying addon to microsoft access”, Computational Intelligence: Theory and Applications 1, 462–472 (1999).
  • [26] J. Kacprzyk and S. Zadrożny, “On combining intelligent querying and data mining using fuzzy logic concepts”, Recent Research Issues on the Management of Fuzziness in Databases 1, 67–81 (2000).
  • [27] J. Kacprzyk and S. Zadrożny, “Data mining via fuzzy querying over the Internet”, Knowledge Management in Fuzzy Databases 1, 211–233 (2000).
  • [28] J. Kacprzyk and S. Zadrożny, “Data mining via linguistic summaries of databases: an interactive approach”, A New Paradigm of Knowledge Engineering by Soft Computing 1, 325–345 (2001).
  • [29] J. Kacprzyk and S. Zadrożny, “Computing with words in intelligent database querying: standalone and Internet-based applications”, Information Sciences 34, 71–109 (2001).
  • [30] J. Kacprzyk and S. Zadrożny, “Fuzzy querying for microsoft access”, Proc. FUZZ-IEEE’94 1, 167–171 (1994).
  • [31] J. Kacprzyk and S. Zadrożny, “FQUERY for access: fuzzy querying for a Windows-based DBMS”, Fuzziness in Database Management Systems 1, 415–433 (1995).
  • [32] T.H. Davenport and J.G. Harris, “What people want (and how to predict it)”, MIT Sloan Management Review 50 (2), 22–31 (2009).
  • [33] R.A. Cole, J. Mariani, H. Uszkoreit, A. Zaenen, and V. Zue, Survey of the State of the Art in Human Language Technology, http://cslu.cse.ogi.edu/HLTsurvey/HLTsurvey.html, 1996.
  • [34] L.A. Zadeh, “A computational approach to fuzzy quantifiers in natural languages”, Computers and Maths with Appls. 9, 149–184 (1983).
  • [35] R.R. Yager and J. Kacprzyk, The Ordered Weighted Averaging Operators: Theory and Applications, Kluwer, Boston, 1996.
  • [36] R.R. Yager, “On ordered weighted avaraging operators in multicriteria decision making”, IEEE Trans. on Systems, Man and Cybern, SMC-18, 183–190 (1988).
  • [37] S. Zadrożny and J. Kacprzyk, “Issues in the practical use of the OWA operators in fuzzy querying”, J. Intelligent Information Systems 33 (3), 307–325 (2009).
  • [38] R. George and R. Srikanth, “Data summarization using genetic algorithms and fuzzy logic”, in Genetic Algorithms and Soft Computing, pp. 599–611, ed. F. Herrera and J.L. Verdegay, Physica-Verlag, Heidelberg, 1996.
  • [39] T.L. Saaty, The Analytic Hierarchy Process: Planning, Priority Setting, Resource Allocation, McGraw-Hill, New York, 1980.
  • [40] L.A. Zadeh, “From search engines to question answering systems – the problems of world knowledge relevance deduction and precisiation”, in Fuzzy Logic and the Semantic Web, pp. 163–210, ed. E. Sanchez, Elsevier, Amsterdam, 2006.
  • [41] J. Kacprzyk, S. Zadrożny, and A. Wilbik, “Linguistic summarization of some static and dynamic features of consensus Bull. Pol. Ac.: Tech. 58(3) 2010 369 J. Kacprzyk and S. Zadrożny reaching”, in: “Computational Intelligence, Theory and Applications”, pp. 19–28, ed. B. Reusch, Springer, Berlin, 2006.
  • [42] F. Portet, E. Reiter, A. Gatt, J. Hunter, S. Sripada, Y. Freer, and C. Sykes, “Automatic generation of textual summaries from neonatal intensive care data”, Artificial Intelligence 173, 789–816 (2009).
  • [43] S. Sripada, E. Reiter, and I. Davy, “SumTime-Mousam: configurable marine weather forecast generator”, Expert Update 6 (3), 4–10 (2003).
  • [44] K.R. McKeown, Text Generation: Using Discourse Strategies and Focus Constraints to Generate Natural Language Text, Cambridge University Press, Cambridge, 1985.
  • [45] J. Kacprzyk and S. Zadrożny, “Soft computing and Web intelligence for supporting consensus reaching”, Soft Computing, (2010), to be published.
  • [46] J. Kacprzyk and S. Zadrożny, “Towards a synergistic combination of Web-based and data-driven decision-support systems via linguistic data summaries”, LNAI 3528, 211–217 (2005).
  • [47] J. Kacprzyk and S. Zadrożny, “On a concept of a consensus reaching process support system based on the use of soft computing and Web techniques”, Computational Intelligence in Decision and Control 1, 859–864 (2008).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BPG8-0039-0002
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.