Skip to main content
SpringerLink
Log in
Book cover

Fuzzy Data Warehousing for Performance Measurement pp 153–182Cite as

Implementation of Fuzzy Data Warehouse

  • Chapter
  • First Online:

  • 934 Accesses

Part of the book series: Fuzzy Management Methods ((FMM))

Abstract

Chapter 4 discussed the application of a fuzzy data warehouse for a movie rental company, and demonstrated how a fuzzy concept can be integrated in data analysis. The corresponding SQL statements and result set were shown. However, for end users, the application only provides direct access to the database system of the data warehouse. Therefore, the user has to know the structure of the meta tables and the data warehouse.

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Bibliography

  1. S. Agrawal, R. Agrawal, E.A. Gupta, J.F. Naughton, R. Ramakrishnan, S. Sarawagi, On the computation of multidimensional aggregates, in Proceedings of the 22th International Conference on Very Large Data Bases, Hong Kong (Morgan Kaufmann, 1996), pp. 506–521

    Google Scholar 

  2. R. Agrawal, A. Gupta, S. Sarawagi, Modeling multidimensional databases, research report. Technical report, IBM Almaden Research Center, San Jose, 1995

    Google Scholar 

  3. R. Agrawal, A. Gupta, S. Sarawagi, Modeling multidimensional databases, in Proceedings of the Thirteenth International Conference on Data Engineering, Birmingham (IEEE Computer Society, 1997), pp. 232–243

    Google Scholar 

  4. R. Alhajj, M. Kaya, Integrating fuzziness into OLAP for multidimensional fuzzy association rules mining, in Proceedings of the Third IEEE International Conference on Data Mining, Melbourne, 2003

    Google Scholar 

  5. C. Alsina, On a family of connectives for fuzzy sets. Fuzzy Sets Syst. 16(3), 231–235 (1985)

    Article  Google Scholar 

  6. Web Analytics Association, http://www.webanalyticsassociation.org, Nov 2011

  7. D. Burdick, P.M. Deshpande, T.S. Jayram, R. Ramakrishnan, S. Vaithyanathan, OLAP over uncertain and imprecise data, in Proceedings of the 31st International Conference on Very Large Data Bases, Trondheim, 2005, pp. 970–981

    Google Scholar 

  8. E.F. Codd, S.B. Codd, C.T. Smalley, Providing OLAP to User-Analysts: An IT Mandate, Technical report, Codd & Associates, 1993

    Google Scholar 

  9. S. Chaudhuri, U. Dayal, An overview of data warehousing and OLAP technology. ACM SIGMOD Rec. 26(1), 65–74 (1997)

    Article  Google Scholar 

  10. P.P. Chen, The entity-relationship model towards unified view of data. ACM Trans. Database Syst. (TODS) 1, 9–36 (1976)

    Article  Google Scholar 

  11. E.F. Codd, A relational model for large shared data banks. Commun. ACM 13(6), 377–387 (1970)

    Article  Google Scholar 

  12. E.F. Codd, Further normalization of the database relational model, in Database Systems Courant Computer Science Symposium, vol. 6, ed. by R. Rustin (Prentice-Hall, Englewood Cliffs, 1971)

    Google Scholar 

  13. E.F. Codd, More commentary on missing information in relational databases (applicable and inapplicable information). ACM SIGMOD Rec. 16, 42–50 (1987)

    Article  Google Scholar 

  14. S. Chaudhuri, K. Shim, Including group-by in query optimization, in Proceedings of the International Conference on Very Large Data Bases, Santiago (IEEE, 1994), pp. 354–366

    Google Scholar 

  15. S. Chaudhuri, K. Shim, Optimizing queries with aggregate views, in Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology, Avignon (Springer, 1996), pp. 167–182

    Google Scholar 

  16. P. Chamoni, S. Stock, Temporal structures in data warehousing, in Data Warehousing and Knowledge Discovery. Lecture Notes in Computer Science, vol. 1676, ed. by M. Mohania, A.M. Tjoa (Springer, Berlin/Heidelberg, 1999), pp. 353–358

    Google Scholar 

  17. B.A. Devlin, P.T. Murphy, An architecture for a business and information system. IBM Syst. J. 27(1), 60–80 (1988)

    Article  Google Scholar 

  18. M. Delgado, C. Molina, D. Sanchez, A. Vila, L. Rodriguez-Ariza, A fuzzy multidimensional model for supporting imprecision in OLAP, in IEEE International Conference on Fuzzy Systems, Budapest, vol. 3 (IEEE, 2004), pp. 1331–1336

    Google Scholar 

  19. D. Dubois, H.M. Prade, Fuzzy Sets and Systems: Theory and Applications, vol. 144 (Academic, New York, 1980)

    Google Scholar 

  20. D. Dubois, H.M. Prade, A review of fuzzy set aggregation connectives. Inf. Sci. 36(1–2), 85–121 (1985)

    Article  Google Scholar 

  21. W.W. Eckerson, Three tier client/server architectures: achieving scalability, performance, and efficiency in client/server applications. Open Inf. Syst. 10(1), 3–20 (1995)

    Google Scholar 

  22. J. Eder, C. Koncilia, Changes of dimension data in temporal data warehouses, in Data Warehousing and Knowledge Discovery (Springer, Heidelberg, 2001), pp. 284–293

    Book  Google Scholar 

  23. D. Fasel, A fuzzy data warehouse approach for the customer performance measurement for a hearing instrument manufacturing company, in Proceedings of Fuzzy Systems and Knowledge Discovery, Tianjin (IEEE Explore, 2009)

    Google Scholar 

  24. L. Feng, T.S. Dillon, Using fuzzy linguistic representations to provide explanatory semantics for data warehouses. IEEE Trans. Knowl. Data Eng. 15(1), 86–102 (2003)

    Article  Google Scholar 

  25. D. Fasel, K. Shahzad, A data warehouse model for integrating fuzzy concepts in meta table structures, in 17th IEEE International Conference and Workshops on the Engineering of Computer-Based Systems, Oxford (IEEE Computer Society, 2010), pp. 100–109

    Google Scholar 

  26. D. Fasel, K. Shahzad, Fuzzy data warehouse for performance analysis, in Fuzzy Methods for Customer Relationship Management and Marketing: Applications and Classifications, ed. by A. Meier, L. Donzé (IGP Global, Hershey, 2012), pp. 217–251

    Chapter  Google Scholar 

  27. D. Fasel, D. Zumstein, A fuzzy data warehouse approach for web analytics, in Visioning and Engineering the Knowledge Society – A Web Science Perspective, ed. by M.D. Lytras, E. Damiani, J.M. Carroll, R.D. Tennyson, D. Avison, A. Naeve, A. Dale, P. Lefrere, F. Tan, J. Sipior, G. Vossen. Volume 5736 of Lecture Notes in Computer Science (Springer, Berlin, 2009), pp. 276–285

    Google Scholar 

  28. J. Gethland, B. Galbraith, D. Almaer, Pragmatic Ajax – A Web 2.0 Primer (The Pragmatic Bookshelf, Raleigh, 2006)

    Google Scholar 

  29. J. Gray, S. Chaudhuri, A. Bosworth, A. Layman, D. Reichart, M. Venkatrao, F. Pellow, H. Pirahesh, Data cube: a relational aggregation operator generalizing group-by, cross-tab, and sub-totals. Data Min. Knowl. Discov. 1(1), 29–53 (1997)

    Article  Google Scholar 

  30. R. Giles, Lukasiewicz logic and fuzzy set theory+. Int. J. Man-Machine Stud. 8(3), 313–327 (1976)

    Article  Google Scholar 

  31. A. Goncalves, Beginning Java EE 6 Platform with GlassFish 3 (Apress, New York, 2009)

    Book  Google Scholar 

  32. J. Galindo, A. Urrutia, M. Piattini, Representation of fuzzy knowledge in relational databases, in Proceedings of the 15th International Workshop on Database and Expert Systems Applications, Zaragoza (IEEE, 2004), pp. 917–921

    Google Scholar 

  33. H. Hamacher, Über logische Aggregationen nicht-binär explizierter Entscheidungskriterien (Fischer Verlag, Frankfurt, 1978)

    Google Scholar 

  34. M. Hassler, Web Analytics (MITP, Heidelberg, 2008)

    Google Scholar 

  35. D.R. Heffelfinger, Java EE Development Using GlassFish Application Server (Packt Publishing, Birmingham, 2007)

    Google Scholar 

  36. D.R. Heffelfinger, Java EE Development with NetBeans 6 (Packt Publishing, Birmingham, 2011)

    Google Scholar 

  37. H.-G. Hwang, C.-Y. Ku, D.C. Yen, C.-C. Cheng, Critical factors influencing the adoption of data warehouse technology: a study of the banking industry in Taiwan. Decis. Support Syst. 37(1), 1–21 (2004)

    Article  Google Scholar 

  38. E.R. Harold, W.S. Means, XML in a Nutshell, vol. 3 (O’Reilly, Sebastopol, 2005)

    Google Scholar 

  39. A.R. Hevner, S.T. March, J. Park, S. Ram, Design science in information systems research. MIS Q. 28(1), 75–105 (2004)

    Google Scholar 

  40. D. Harel, B. Rumpe, Meaningful modeling: what’s the semantics of “Semantics”? Computer 37(10), 64–72 (2004)

    Article  Google Scholar 

  41. IMDb.com, Inc., The internet movie database. http://www.imdb.com/, Sept 2011

  42. ICEsoft Technologies Inc., ICEFaces. http://www.icefaces.org/main/home, Nov 2011

  43. Oracle America Inc., Java architecture for XML binding. http://jaxb.java.net, Nov 2011

  44. Oracle America Inc., Java server faces. http://www.oracle.com/technetwork/java/javaee/javaserverfaces-139869.html, Nov 2011

  45. VMWare Inc., JDBCTemplate documentation. http://static.springsource.org/spring/docs/2.0.x/reference/jdbc.html, Nov 2011

  46. VMWare Inc., Spring framework. http://www.springsource.org, Nov 2011

  47. W.H. Inmon, Building the Data Warehouse, 4th edn. (Wiley, Indianapolis, 2005)

    Google Scholar 

  48. A. Ionas, Modellierung, Entwicklung und Nutzung eines Data Warehouse für medizinische Communication Centers. PhD thesis, Departement für Informatik – Universität Fribourg, Fribourg, 2008

    Google Scholar 

  49. W.H. Inmon, D. Strauss, G. Neushloss, DW 2.0 – The Architecture for the Next Generation of Data Warehousing (Morgan Kaufmann, Amsterdam, 2008)

    Google Scholar 

  50. ISO, ISO/IEC 9075(1-4,9-11,13,14):2008 – Information Technology – Database Languages – SQL, Geneva, 2008

    Google Scholar 

  51. N.K. Kasabov, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering (MIT, Cambridge, 1996)

    Google Scholar 

  52. A. Kaushik, Web Analytics – An Hour a Day (O’Reilly, New York, 2007)

    Google Scholar 

  53. M. Kaufmann, Inductive fuzzy classification – yet to publish. PhD thesis, University of Fribourg, Fribourg, 2012

    Google Scholar 

  54. R. Kimball, J. Caserta, The Data Warehouse ETL Toolkit (Wiley, Indianapolis, 2004)

    Google Scholar 

  55. B. Kosko, S. Isaka, Fuzzy logic. Sci. Am. 269(1), 76–81 (1993)

    Google Scholar 

  56. R. Kimball, Help for dimensional modeling: helper tables let you design and manage multivalued dimensions successfully. DBMS online (Miller Freeman Inc., 1998), http://www.kimballgroup.com/1998/08/02/help-for-dimensional-modeling/

  57. D.P.V. Kasinadh, P. Radha Krishna, Building fuzzy OLAP using multi-attribute summarization, in Proceedings of the International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007), Sivakasi, vol. 01 (IEEE, 2007), pp. 370–374

    Google Scholar 

  58. K.V.N.N. Pavan Kumar, P. Radha Krishna, S. Kumar De, Fuzzy OLAP cube for qualitative analysis, in Intelligent Sensing and Information Processing, Chennai, 2005, pp. 290–295

    Google Scholar 

  59. B. Kosko, Fuzziness vs. probability. Int. J. Gen. Syst. 17(2), 211–240 (1990)

    Google Scholar 

  60. B. Kosko, Fuzzy systems as universal approximators. IEEE Trans. Comput. 43(11), 1329–1333 (1994)

    Article  Google Scholar 

  61. B. Kosko, The probability monopoly. IEEE Trans. Fuzzy Syst. 2(1), 32–33 (1994)

    Article  Google Scholar 

  62. R. Kimball, M. Ross, The Data Warehouse Toolkit (Wiley, New York, 2002)

    Google Scholar 

  63. R. Kimball, M. Ross, W. Thornthwaite, J. Mundy, B. Becker, The Data Warehouse Lifecycle Toolkit (Wiley, Indianapolis, 2008)

    Google Scholar 

  64. N. Kasabov, B. Woodford, Rule insertion and rule extraction from evolving fuzzy neural networks: algorithms and applications for building adaptive, intelligent expert systems, in IEEE International Fuzzy Systems Conference Proceedings, Seoul, vol. 3 (IEEE, 1999), pp. 1406–1411

    Google Scholar 

  65. W. Lehner, Modeling large scale OLAP scenarios, in Advances in Database Technology – EDBT’98, Valencia, vol. 1377 (Springer, 1998), pp. 153–167

    Google Scholar 

  66. W. Lehner, Datenbanktechnologien für Data-Warehouse-Systeme – Konzepte und Methoden (dpunkt.verlag, Heidelberg, 2003)

    Google Scholar 

  67. M. Levene, G. Loizou, Why is the snowflake schema a good data warehouse design? Inf. Syst. 28(3), 225–240 (2003)

    Article  Google Scholar 

  68. H.-J. Lenz, A. Shoshani, Summarizability in OLAP and statistical data bases, in Statistical and Scientific Database Management, Olympia, 1997

    Google Scholar 

  69. H.J. Lenz, B. Thalheim, OLAP databases and aggregation functions, in Proceedings of the Thirteenth International Conference on Scientific and Statistical Database Management, SSDBM 2001, Fairfax (IEEE Computer Society, 2001), p. 0091

    Google Scholar 

  70. Microsoft Corporation, MDX specifications. http://msdn.microsoft.com/en-us/library/Aa216767, Apr 2010

  71. A. Meier, C. Mezger, N. Werro, G. Schindler, Zur unscharfen Klassifikation von Datenbanken mit fCQL, in Proceedings of the GI-Workshop LLWA Lernen, Lernen, Wissen, Adaptivität, Karlsruhe, 2003

    Google Scholar 

  72. C. Molina, L. Rodríguez-Ariza, D. Sánchez, M.A. Vila, A new fuzzy multidimensional model. IEEE Trans. Fuzzy Syst. 14(6), 897–912 (2006)

    Article  Google Scholar 

  73. S.T. March, G.F. Smith, Design and natural science research on information technology. Decis. Support Syst. 15(4), 251–266 (1995)

    Article  Google Scholar 

  74. A. Motro, P. Smets, Uncertainty Management in Information Systems: From Needs to Solutions (Kluwer Academic, Boston, 1997)

    Book  Google Scholar 

  75. A. Meier, C. Savary, G. Schindler, Y. Veryha, Database schema with fuzzy classification and classification query language, in Proceedings of the International Congress on Computational Intelligence – Methods and Applications, University of Wales, Bangor, 2001

    Google Scholar 

  76. A. Meier, G. Schindler, N. Werro, Extending relational databases with fuzzy classification. Department of Informatics, University of Fribourg, Fribourg, 2007

    Google Scholar 

  77. A. Meier, G. Schindler, N. Werro, Fuzzy classification on relational databases, in Handbook of Research on Fuzzy Information Processing in Databases, vol. 2, ed. by J. Galindo (IGI Global, Hershey, 2008), pp. 586–614

    Chapter  Google Scholar 

  78. M. Mizumoto, K. Tanaka, Fuzzy sets and their operations. Inf. Control 48(1), 30–48 (1981)

    Article  Google Scholar 

  79. E. Medina, J. Trujillo, A standard for representing multidimensional properties: the common warehouse model (CWM), in Advances in Databases and Information Systems, ed. by Y. Manolopoulos, P. Navrat. Volume 2435/2002 of Lecture Notes in Computer Science (Springer, Berlin, 2002), pp. 232–247

    Google Scholar 

  80. A.O. Mendelzon, A.A. Vaisman, Temporal queries in OLAP, in Proceedings of the 26th International Conference on Very Large Data Bases, Cairo (Morgan Kaufmann, 2000), pp. 242–253

    Google Scholar 

  81. A. Meier, N. Werro, M. Albrecht, M. Sarakinos, Using a fuzzy classification query language for customer relationship management, in Proceedings of the 31st VLDB Conference, Trondheim, 2005

    Google Scholar 

  82. E. Malinowski, E. Zimányi, Representing spatiality in a conceptual multidimensional model, in Proceedings of the 12th Annual ACM International Workshop on Geographic Information Systems, Washington, DC (ACM, 2004), pp. 12–22

    Google Scholar 

  83. D.D. Nauck, Data analysis with neuro-fuzzy methods. Technical report, Fakultät für Informatik der Otto-von-Guericke-Universität Magdeburg, 2000

    Google Scholar 

  84. R. Ng, J. Han, Efficient and effective clustering method for spatial data mining, in Proceedings of the 20th International Conference on Very Large Databases, Santiago, 1994, pp. 144–155

    Google Scholar 

  85. S. Nüesch, OLAP cube for a fuzzy data warehouse. Bachelor thesis, University of Fribourg, Fribourg, 2011

    Google Scholar 

  86. OLAP Council, OLAP and OLAP server definitions. http://www.olapcouncil.org/research/resrchly.htm, Aug 2010

  87. Oracle, Oracle essbase hyperion. http://www.oracle.com/technetwork/middleware/essbase/overview/index.html, Jan 2010

  88. Oracle, Oracle essbase hyperion database administration documentation. http://download.oracle.com/docs/cd/E17236_01/epm.1112/esb_dbag/launch.html, Sept 2011

  89. Pentaho Corporation, Mondrian OLAP server. http://mondrian.pentaho.org/, Apr 2010

  90. B.W. Perry, Ajax Hacks (O’Reilly, Sebastopol, 2006)

    Google Scholar 

  91. S. Peterson, Stars: a pattern language for query optimized schema. Sequent Computer Systems, 1994. http://c2.com/ppr/stars.html

  92. T.B. Pedersen, C.S. Jensen, Multidimensional database technology. Computer 34(12), 40–46 (2001)

    Article  Google Scholar 

  93. T.B. Pedersen, C.S. Jensen, C.E. Dyreseon, Supporting imprecision in multidimensional databases using granularities, in Eleventh International Conference on Scientific and Statistical Database Management, Cleveland, 1999

    Google Scholar 

  94. PostgreSQL Global Development Group, PostgreSQL. http://www.postgresql.org/, Jan 2011

  95. PostgreSQL Global Development Group, PostgreSQL documentation. http://www.postgresql.org/docs/current/static, Jan 2011

  96. PostgreSQL Global Development Group, PostgreSQL documentation of server programming. http://www.postgresql.org/docs/current/static/server-programming.html, Sept 2011

  97. PostgreSQL Global Development Group. PostgreSQL documentation of window functions. http://www.postgresql.org/docs/current/static/functions-window.html, June 2011

  98. D. Pérez, M.J. Somodevilla, I.H. Pineda, Fuzzy spatial data warehouse: a multidimensional model, in Eighth Mexican International Conference on Current Trends in Computer Science, Morelia, 2007

    Google Scholar 

  99. T. Purcell, Star join optimization: DB2 UDB for z/OS and OS/390. Int. DB2 User Group (IDUG) Solut. J. 9(1), 17–19 (2002)

    Google Scholar 

  100. C. Rathgeb, Entwicklung eines fuzzy data warehouse. Bachelor thesis, University of Fribourg, Fribourg, 2011

    Google Scholar 

  101. M. Rafanelli, A. Shoshani, STORM: a statistical object representation model, in Statistical and Scientific Data Base Management Conference, Charlotte, 1990, pp. 14–29

    Google Scholar 

  102. G. Schindler, Fuzzy-Datenanalyse durch kontextbasierte Datenbankabfragen. PhD thesis, Technische Hochschule Aachen, 1998

    Google Scholar 

  103. L. Scheuner, Entwurf und Erstellung eines Data Warehouse für die Schweizerische Futtermitteldatenbank. Master’s thesis, Universität Zürich and Universität Fribourg, 2010

    Google Scholar 

  104. A. Shoshani, Statistical databases: characteristics, problems, and some solutions, in Proceedings of the 8th International Conference on Very Large Data Bases, Mexico City (Morgan Kaufmann, 1982), pp. 208–222

    Google Scholar 

  105. H. Schepperle, A. Merkel, A. Haag, Erhalt von imperfektion in einem data warehouse, in Internationales Symposium: Data-Warehouse-Systeme und Knowledge-Discovery, Zaragoza, 2004

    Google Scholar 

  106. L. Sapir, A. Shmilovici, L. Rokach, A methodology for the design of a fuzzy data warehouse, in 4th International IEEE Conference on Intelligent Systems, IS’08, Varna, vol. 1, 2008

    Google Scholar 

  107. Teradata, http://www.teradata.com, June 2011

  108. T. Tilli, Fuzzy-Logik: Grundlagen, Anwendungen, Hard- und Software (Franzis-Verlag GmbH, München, 1991)

    Google Scholar 

  109. TMDb Inc., The open movie database project. http://www.themoviedb.org/, Dec 2010

  110. U. Thole, H.J. Zimmermann, P. Zysno, On the suitability of minimum and product operators for the intersection of fuzzy sets* 1. Fuzzy Sets Syst. 2(2), 167–180 (1979)

    Article  Google Scholar 

  111. P. Vassiliadis, Modeling multidimensional databases, cubes and cube operations, in Proceedings of the 10th International Conference on Scientific and Statistical Database Management, Capri, pp. 53–62 (IEEE, 1998)

    Google Scholar 

  112. Vertica, Vertica database. http://www.vertica.com/, Sept 2011

  113. P. Vassiliadis, T. Sellis, A survey of logical models for OLAP databases. ACM SIGMOD Rec. 28(4), 64–69 (1999)

    Article  Google Scholar 

  114. P. Vassiliadis, S. Skiadopoulos, Modelling and optimisation issues for multidimensional databases, in Advanced Information Systems Engineering (Springer, Berlin, 2000), pp. 482–497

    Google Scholar 

  115. B. Werners, Aggregation models in mathematical programming. Math. Models Decis. Support 48, 295–305 (1988)

    Article  Google Scholar 

  116. N. Werro, Fuzzy classification of online customers. PhD thesis, University of Fribourg, Fribourg, 2008

    Google Scholar 

  117. R.R. Yager, On a general class of fuzzy connectives. Fuzzy Sets Syst. 4(3), 235–242 (1980)

    Article  Google Scholar 

  118. L.A. Zadeh, Fuzzy sets. Inf. Control 8, 338–353 (1965)

    Article  Google Scholar 

  119. L.A. Zadeh, The concept of a linguistic variable and its application to approximate reasoning – Part I. Inf. Sci. 8, 199–249 (1975)

    Article  Google Scholar 

  120. L.A. Zadeh, The concept of a linguistic variable and its application to approximate reasoning – Part II. Inf. Sci. 8, 301–357 (1975)

    Article  Google Scholar 

  121. L.A. Zadeh, The concept of a linguistic variable and its application to approximate reasoning – Part III. Inf. Sci. 9, 43–80 (1975)

    Article  Google Scholar 

  122. L.A. Zadeh, Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets Syst. 1, 3–28 (1978)

    Article  Google Scholar 

  123. L.A. Zadeh, PRUF – a meaning representation language for natural languages. Int. J. Man-Machine Stud. 10, 395–460 (1978)

    Article  Google Scholar 

  124. L.A. Zadeh, A computational approach to fuzzy quantifiers in natural languages* 1. Comput. Math. Appl. 9(1), 149–184 (1983)

    Article  Google Scholar 

  125. L.A. Zadeh, Fuzzy logic, neural networks, and soft computing. Commun. ACM 37(3), 77–84 (1994)

    Article  Google Scholar 

  126. L.A. Zadeh, Test-score semantics for natural languages and meaning representation via PRUF, in Fuzzy Sets, Fuzzy Logic, and Fuzzy Systems: Selected Papers, vol. 6 (World Scientific, Singapore, 1996)

    Book  Google Scholar 

  127. H.-J. Zimmermann, Fuzzy Set Theory and its Applications (Kluwer Academic, Boston, 1991)

    Book  Google Scholar 

  128. L.A. Zadeh, G.J. Klir, B. Yuan (eds.), Fuzzy Sets, Fuzzy Logic, and Fuzzy Systems: Selected Papers, vol. 6 (World Scientific, Singapore, 1996)

    Google Scholar 

  129. H. Zimmermann, P. Zysno, Latent connectives in human decision making. Fuzzy Sets Syst. 4, 37–51 (1980)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Scigility Inc., Marly, Switzerland

    Daniel Fasel

Authors
  1. Daniel Fasel
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Fasel, D. (2014). Implementation of Fuzzy Data Warehouse. In: Fuzzy Data Warehousing for Performance Measurement. Fuzzy Management Methods. Springer, Cham. https://doi.org/10.1007/978-3-319-04226-8_5

Download citation

Publish with us

Policies and ethics

Search

Navigation