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A survey of intelligent assistants for data analysis

Published:03 July 2013Publication History
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Abstract

Research and industry increasingly make use of large amounts of data to guide decision-making. To do this, however, data needs to be analyzed in typically nontrivial refinement processes, which require technical expertise about methods and algorithms, experience with how a precise analysis should proceed, and knowledge about an exploding number of analytic approaches. To alleviate these problems, a plethora of different systems have been proposed that “intelligently” help users to analyze their data.

This article provides a first survey to almost 30 years of research on intelligent discovery assistants (IDAs). It explicates the types of help IDAs can provide to users and the kinds of (background) knowledge they leverage to provide this help. Furthermore, it provides an overview of the systems developed over the past years, identifies their most important features, and sketches an ideal future IDA as well as the challenges on the road ahead.

References

  1. Aha, D. W. 1992. Generalizing from case studies: A case study. In Proceedings of the 9th International Workshop on Machine Learning. 1--10. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Amant, R. and Cohen, P. 1998a. Interaction with a mixed-initiative system for exploratory data analysis. Knowl. Based Syst. 10, 5, 265--273.Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Amant, R. S. and Cohen, P. 1998b. Intelligent support for exploratory data analysis. J. Comput. Graph. Stat. 7, 4, 545--558.Google ScholarGoogle Scholar
  4. Ashburner, M., Ball, C., Blake, J., Botstein, D., Butler, H., Cherry, J., Davis, A., Dolinski, K., Dwight, S., Eppig, J., Harris, M., Hill, D., Issel-Tarver, L., Kasarskis, A., Lewis, S., Matese, J., Richardson, J., Ringwald, M., Rubin, G., and Sherlock, G. 2000. Gene ontology: Tool for the unification of biology. Nature Genetics 25, 25--29.Google ScholarGoogle ScholarCross RefCross Ref
  5. Bensusan, H. and Kalousis, A. 2001. Estimating the predictive accuracy of a classifier. In Machine Learning, Lecture Notes in Computer Science, vol. 2167, Springer, 25--36. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Bernstein, A. and Daenzer, M. 2007. The NExT system: Towards true dynamic adaptations of semantic web service compositions. In The Semantic Web: Research and Applications, Lecture Notes in Computer Science, vol. 4519, Springer, 739--748. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Bernstein, A., Provost, F., and Hill, S. 2005. Toward intelligent assistance for a data mining process: An ontology-based approach for cost-sensitive classification. IEEE Trans. Knowl. Data Eng. 17, 4, 503--518. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Berthold, M. R., Cebron, N., Dill, F., Gabriel, T. R., Kötter, T., Meinl, T., Ohl, P., Thiel, K., and Wiswedel, B. 2009. Knime - the konstanz information miner: version 2.0 and beyond. SIGKDD Explor. Newsl. 11, 26--31. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Blockeel, H. and Vanschoren, J. 2007. Experiment databases: Towards an improved experimental methodology in machine learning. In Knowledge Discovery in Databases, Lecture Notes in Computer Science, vol. 4702, Springer, 6--17. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Blum, A. and Furst, M. 1997. Fast planning through planning graph analysis* 1. Artificial intelligence 90, 1--2, 281--300. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Botia, J., Gomez-Skarmeta, A., Valdes, M., and Padilla, A. 2001. METALA: A meta-learning architecture. In Computational Intelligence. Theory and Apllications, Lecture Notes in Computer Science, vol. 2206, Springer, 688--698. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Boulos, M. N. K. 2009. Semantic wikis: A comprehensible introduction with examples from the health sciences. J. Emerging Technol. Web Intel.Google ScholarGoogle Scholar
  13. Castiello, C., Castellano, G., and Fanelli, A. 2005. Meta-data: Characterization of input features for meta-learning. Model. Decisions Artif. Intel. 3558, 457--468. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Castiello, C. and Fanelli, A. 2005. Meta-learning experiences with the mindful system. In Computational Intelligence and Security, Lecture Notes in Computer Science, vol. 3801, Springer, 321--328. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Cerrito, P. 2007. Introduction to Data Mining Using SAS Enterprise Miner. SAS Publishing, Cary, NC. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Chandrasekaran, B., Johnson, T., and Smith, J. 1992. Task-structure analysis for knowledge modeling. Commun. ACM 35, 9, 124--137. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Chandrasekaran, B. and Josephson, J. 1999. What are ontologies, and why do we need them? IEEE Intell. Sys. 14, 1, 20--26. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Chapman, P., Clinton, J., Khabaza, T., Reinartz, T., and Wirth, R. 1999. The crisp-dm process model. The CRIP--DM Consortium 310.Google ScholarGoogle Scholar
  19. Charest, M., Delisle, S., Cervantes, O., and Shen, Y. 2008. Bridging the gap between data mining and decision support: A case-based reasoning and ontology approach. Intell. Data Anal. 12, 1--26. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Choinski, M. and Chudziak, J. 2009. Ontological learning assistant for knowledge discovery and data mining. In Proceedings of the IEEE International Conference on Computer Science and Information Technology. 147--155.Google ScholarGoogle Scholar
  21. Craw, S., Sleeman, D., Graner, N., and Rissakis, M. 1992. Consultant: Providing advice for the machine learning toolbox. In Proceedings of the Annual Technical Conference on Expert Systems (ES). 5--23.Google ScholarGoogle Scholar
  22. Derriere, S., Preite-Martinez, A., and Richard, A. 2006. UCDs and ontologies. ASP Conf. Series 351, 449.Google ScholarGoogle Scholar
  23. Diamantini, C., Potena, D., and Storti, E. 2009a. KDDONTO: An ontology for discovery and composition of KDD algorithms. In Proceedings of the ECML-PKDD Workshop on Service-Oriented Knowledge Discovery. 13--24.Google ScholarGoogle Scholar
  24. Diamantini, C., Potena, D., and Storti, E. 2009b. Ontology-driven KDD process composition. In Advances in Intelligent Data Analysis VIII, Lecture Notes in Computer Science, vol. 5772, Springer, 285--296. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Engels, R. 1996. Planning tasks for knowledge discovery in databases: Performing task-oriented user-guidance. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data mining (KDD). 170--175.Google ScholarGoogle Scholar
  26. Engels, R., Lindner, G., and Studer, R. 1997. A guided tour through the data mining jungle. In Proceedings of the 3rd International Conference on Knowledge Discovery in Databases. 163--166.Google ScholarGoogle Scholar
  27. Erol, K. 1996. Hierarchical task network planning: Formalization, analysis, and implementation. Ph.D. dissertation, University of Maryland at College Park, College Park, MD. UMI Order No. GAX96-22054. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. Fayyad, U., Piatetsky-Shapiro, G., and Smyth, P. 1996. From data mining to knowledge discovery in databases. AI Mag. 17, 3, 37--54.Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. Fox, M. and Long, D. 2003. PDDL2. 1: An extension to PDDL for expressing temporal planning domains. J. Artif. Intell. Res. 20, 1, 61--124. Google ScholarGoogle ScholarCross RefCross Ref
  30. Gale, W. 1986. Rex review. In Artificial Intelligence and Statistics. Addison-Wesley Longman Publishing Co., Inc., Boston, MA. 173--227. Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. Giraud-Carrier, C. 2005. The data mining advisor: Meta-learning at the service of practitioners. In Proceedings of the International Conference on Machine Learning and Applications (ICMLA). 113--119. Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. Goble, C., Bhagat, J., Aleksejevs, S., Cruickshank, D., Michaelides, D., Newman, D., Borkum, M., Bechhofer, S., Roos, M., Li, P., and De Roure, D. 2010. myExperiment: A repository and social network for the sharing of bioinformatics workflows. Nucl. Acids Res..Google ScholarGoogle Scholar
  33. Goebel, M. and Gruenwald, L. 1999. A survey of data mining and knowledge discovery software tools. SIGKDD Explor. Newsl. 1, 20--33. Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. Grabczewski, K. and Jankowski, N. 2007. Versatile and efficient meta-learning architecture: Knowledge representation and management in computational intelligence. In Proceedings of the IEEE Symposium on Computational Intelligence and Data Mining. 51--58.Google ScholarGoogle Scholar
  35. Graner, N., Sharma, S., Sleeman, D., Rissakis, M., CRAW, S., and Moore, C. 1993. The machine learning toolbox consultant. Int. J. AI Tools 2, 3, 307--328.Google ScholarGoogle ScholarCross RefCross Ref
  36. Grimmer, U. 1996. Clementine: Data mining software. In Classification and Multivariate Graphics: Models, Software and Applications. 25--31.Google ScholarGoogle Scholar
  37. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., and Witten, I. 2009. The weka data mining software: An update. ACM SIGKDD Explor. News. 11, 1, 10--18. Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. Hand, D. 1985. Statistical expert systems: Necessary attributes. J. Appl. Stat. 12, 1, 19--27.Google ScholarGoogle ScholarCross RefCross Ref
  39. Hand, D. 1987. A statistical knowledge enhancement system. J. Royal Stat. Soc. Series A (General) 150, 4, 334--345.Google ScholarGoogle ScholarCross RefCross Ref
  40. Hand, D. 1990. Practical experience in developing statistical knowledge enhancement systems. Ann. Math. Artif. Intell. 2, 1, 197--208.Google ScholarGoogle ScholarCross RefCross Ref
  41. Hand, D. 1997. Intelligent data analysis: Issues and opportunities. In Proceedings of the 2nd International Symposium on Advances in Intelligent Data Analysis. Reasoning about Data (IDA'97). 1--14. Google ScholarGoogle ScholarDigital LibraryDigital Library
  42. Hernansaez, J., Bota, J., and Skarmeta, A. 2004. METALA: A J2EE technology based framework for web mining. Revista Colombiana de Computación 5, 1.Google ScholarGoogle Scholar
  43. Hilario, M. and Kalousis, A. 2001. Fusion of meta-knowledge and meta-data for case-based model selection. In Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery (PKDD'01). 180--191. Google ScholarGoogle ScholarDigital LibraryDigital Library
  44. Hilario, M., Kalousis, A., Nguyen, P., and Woznica, A. 2009. A data mining ontology for algorithm selection and meta-mining. In Proceedings of the ECML-PKDD Workshop on Service-Oriented Knowledge Discovery. 76--87.Google ScholarGoogle Scholar
  45. Hoffmann, J. and Nebel, B. 2001. The FF planning system: Fast plan generation through heuristic search. J. Artif. Intell. Res. 14, 253--302. Google ScholarGoogle ScholarCross RefCross Ref
  46. Horrocks, I., Patel-Schneider, P., and Boley, H. 2004. SWRL: A semantic web rule language combining OWL and RuleML. http://www. w3.org/submission/SWRL/.Google ScholarGoogle Scholar
  47. Ihaka, R. and Gentleman, R. 1996. R: A language for data analysis and graphics. J. Computation. Graph. Stat. 5, 3, 299--314.Google ScholarGoogle Scholar
  48. Kalousis, A. 2002. Algorithm selection via meta-learning. Ph.D. dissertation, University of Geveve.Google ScholarGoogle Scholar
  49. Kalousis, A., Bernstein, A., and Hilario, M. 2008. Meta-learning with kernels and similarity functions for planning of data mining workflows. In Proceedings of the ICML/UAI/COLT Workshop on Planning to Learn. 23--28.Google ScholarGoogle Scholar
  50. Kalousis, A. and Hilario, M. 2001. Model selection via meta-learning: A comparative study. Int. J. Artif. Intell. Tools 10, 4, 525--554.Google ScholarGoogle ScholarCross RefCross Ref
  51. Kalousis, A. and Theoharis, T. 1999. Noemon: Design, implementation and performance results of an intelligent assistant for classifier selection. Intell. Data Anal. 3, 4, 319--337.Google ScholarGoogle ScholarDigital LibraryDigital Library
  52. Kietz, J., Serban, F., and Bernstein, A. 2010. eProPlan: A tool to model automatic generation of data mining workflows. In Proceedings of the 3rd Planning to Learn Workshop (WS9) At the European Conference on Artificial Intelligence (ECAI'10). 15.Google ScholarGoogle Scholar
  53. Kietz, J., Serban, F., Bernstein, A., and Fischer, S. 2009. Towards cooperative planning of data mining workflows. In Proceedings of the ECML-PKDD Workshop on Service-Oriented Knowledge Discovery. 1--12.Google ScholarGoogle Scholar
  54. Kietz, J., Vaduva, A., and Zücker, R. 2000. Mining mart: Combining case-based-reasoning and multi-strategy learning into a framework to reuse kdd-application. In Proceedings of the 5th International Workshop on Multistrategy Learning (MSL'00). Vol. 311.Google ScholarGoogle Scholar
  55. Klusch, M., Gerber, A., and Schmidt, M. 2005. Semantic Web service composition planning with OWLS-Xplan. In Proceedings of the AAAI Fall Symposium on Agents and the Semantic Web. 55--62.Google ScholarGoogle Scholar
  56. Kodratoff, Y., Sleeman, D., Uszynski, M., Causse, K., and Craw, S. 1992. Building a machine learning toolbox. In Enhancing the Knowledge Engineering Process: Contributions from ESPRIT, L. Steels and B. Lepape, Eds., Elsevier, 81--108.Google ScholarGoogle Scholar
  57. Kohavi, R., Brodley, C. E., Frasca, B., Mason, L., and Zheng, Z. 2000. Kdd-cup 2000 organizers' report: Peeling the onion. SIGKDD Explor. Newsl. 2, 86--93. Google ScholarGoogle ScholarDigital LibraryDigital Library
  58. Leite, R. and Brazdil, P. 2007. An iterative process for building learning curves and predicting relative performance of classifiers. In Progress in Artificial Intelligence, Lecture Notes in Computer Science, vol. 4874, Springer, 87--98. Google ScholarGoogle ScholarDigital LibraryDigital Library
  59. Levesque, R. 2005. SPSS Programming and Data Management: A Guide for SPSS and SAS Users. SPSS, Chicago, IL. Google ScholarGoogle ScholarDigital LibraryDigital Library
  60. Lindner, G. and Studer, R. 1999. AST: Support for algorithm selection with a CBR approach. In Principles of Data Mining and Knowledge Discovery, Lecture Notes in Computer Science, vol. 1704, Springer, 418--423. Google ScholarGoogle ScholarDigital LibraryDigital Library
  61. Liu, Z., Ranganathan, A., and Riabov, A. 2007. A planning approach for message-oriented semantic web service composition. In Proceedings of the AAAI National Conference On Artificial Intelligence 5, 2, 1389--1394. Google ScholarGoogle ScholarDigital LibraryDigital Library
  62. MathWorks. 2004. Matlab. The MathWorks, Natick, MA.Google ScholarGoogle Scholar
  63. McDermott, D., Ghallab, M., Howe, A., Knoblock, C., Ram, A., Veloso, M., Weld, D., and Wilkins, D. 1998. PDDL-the planning domain definition language. http://academic.research.microsoft.com/Paper/2024980.Google ScholarGoogle Scholar
  64. Michie, D., Spiegelhalter, D., and Taylor, C. 1994. Machine Learning, Neural and Statistical Classification. Ellis Horwood, Upper Saddle River, NJ. Google ScholarGoogle ScholarDigital LibraryDigital Library
  65. Mierswa, I., Wurst, M., Klinkenberg, R., Scholz, M., and Euler, T. 2006. Yale: Rapid prototyping for complex data mining tasks. In Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'06). 935--940. Google ScholarGoogle ScholarDigital LibraryDigital Library
  66. Mikut, R. and Reischl, M. 2011. Data mining tools. Wiley Interdisciplinary Rev. Data Mining Knowl. Discov..Google ScholarGoogle Scholar
  67. Morik, K. and Scholz, M. 2004. The MiningMart approach to knowledge discovery in databases. In Intelligent Technologies for Information Analysis, N. Zhong, and J. Liu, Eds., Springer, 47--65.Google ScholarGoogle Scholar
  68. Nonaka, I. and Takeuchi, H. 1995. The Knowledge-Creating Company: How Japanese Companies Create the Dynamics of Innovation. Oxford University Press, New York, NY.Google ScholarGoogle Scholar
  69. Oinn, T., Addis, M., Ferris, J., Marvin, D., Greenwood, M., Carver, T., Pocock, M., Wipat, A., and Li, P. 2004. Taverna: A tool for the composition and enactment of bioinformatics workflows. Bioinformatics 20, 17, 3045--3054. Google ScholarGoogle ScholarDigital LibraryDigital Library
  70. Panov, P., Soldatova, L., and Džeroski, S. 2009. Towards an ontology of data mining investigations. In Discovery Science, Lecture Notes in Computer Science, vol. 5808, Springer, 257--271. Google ScholarGoogle ScholarDigital LibraryDigital Library
  71. Patel-Schneider, P., Hayes, P., and Horrocks, I. 2004. OWL web ontology language semantics and abstract syntax. http://www.w3.org/TR/owl-semantics/.Google ScholarGoogle Scholar
  72. Peng, Y., Flach, P., Brazdil, P., and Soares, C. 2002a. Decision tree-based data characterization for meta-learning. In Proceedings of the ECML-PKDD Workshop on Integration and Collaboration Aspects of Data Mining, Decision Support and Meta-Learning. 111--122.Google ScholarGoogle Scholar
  73. Peng, Y., Flach, P., Soares, C., and Brazdil, P. 2002b. Improved dataset characterisation for meta-learning. In Discovery Science, Lecture Notes in Computer Science, vol. 2534, Springer, 141--152. Google ScholarGoogle ScholarDigital LibraryDigital Library
  74. Pfahringer, B., Bensusan, H., and Giraud-Carrier, C. 2000. Meta-learning by landmarking various learning algorithms. In Proceedings of the International Conference on Machine Learning (ICML) 951, 743--750. Google ScholarGoogle ScholarDigital LibraryDigital Library
  75. Podpečan, V., Zemenova, M., and Lavrač, N. 2011. Orange4ws environment for service-oriented data mining. Comput. J.Google ScholarGoogle Scholar
  76. Raes, J. 1992. Inside two commercially available statistical expert systems. Stat. Comput. 2, 2, 55--62.Google ScholarGoogle ScholarCross RefCross Ref
  77. Rendell, L., Seshu, R., and Tcheng, D. 1987. Layered concept learning and dynamically-variable bias management. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI). 308--314. Google ScholarGoogle ScholarDigital LibraryDigital Library
  78. Rice, J. 1976. The algorithm selection problem. Adv. Comput. 15, 65--118.Google ScholarGoogle ScholarCross RefCross Ref
  79. Roure, D. D., Goble, C., and Stevens, R. 2009. The design and realisation of the myExperiment virtual research environment for social sharing of workflows. Future Gen. Comput. Syst. 25, 561--567. Google ScholarGoogle ScholarDigital LibraryDigital Library
  80. Russell, D. M., Stefik, M. J., Pirolli, P., and Card, S. K. 1993. The cost structure of sensemaking. In Proceedings of the INTERACT and CHI Conference on Human Factors in Computing Systems (CHI'93). 269--276. Google ScholarGoogle ScholarDigital LibraryDigital Library
  81. Sacerdoti, E. 1974. Planning in a hierarchy of abstraction spaces. Artif. Intell. 5, 2, 115--135.Google ScholarGoogle ScholarCross RefCross Ref
  82. Schaffer, C. 1994. A conservation law for generalization performance. In Proceedings of the International Conference on Machine Learning. 259--265.Google ScholarGoogle ScholarCross RefCross Ref
  83. Sirin, E. and Parsia, B. 2007. SPARQL-DL: SPARQL query for OWL-DL. In Proceedings of the International Workshop on OWL Experiences and Directions (OWLED).Google ScholarGoogle Scholar
  84. Sirin, E., Parsia, B., Grau, B., Kalyanpur, A., and Katz, Y. 2007. Pellet: A practical owl-dl reasoner. Web Semantics: Sci. Services Agents World Wide Web 5, 2, 51--53. Google ScholarGoogle ScholarDigital LibraryDigital Library
  85. Sirin, E., Parsia, B., Wu, D., Hendler, J., and Nau, D. 2004. HTN planning for web service composition using SHOP2. J. Web Semantics 1, 4, 377--396. Google ScholarGoogle ScholarDigital LibraryDigital Library
  86. Sleeman, D., Rissakis, M., Craw, S., Graner, N., and Sharma, S. 1995. Consultant-2: Pre-and post-processing of machine learning applications. Int. J. Human Comput. Studies 43, 1, 43--63. Google ScholarGoogle ScholarDigital LibraryDigital Library
  87. Smith-Miles, K. 2008. Cross-disciplinary perspectives on meta-learning for algorithm selection. ACM Comput. Surv. 41, 1, Article 6. Google ScholarGoogle ScholarDigital LibraryDigital Library
  88. Soares, C., Brazdil, P., and Kuba, P. 2004. A meta-learning method to select the kernel width in support vector regression. Machine Learn. 54, 195--209. Google ScholarGoogle ScholarDigital LibraryDigital Library
  89. Stoeckert, C., Causton, H., and Ball, C. 2002. Microarray databases: Standards and ontologies. Nature Genetics 32, 469--473.Google ScholarGoogle ScholarCross RefCross Ref
  90. Szalay, A. and Gray, J. 2001. The world-wide telescope. Science 293, 2037--2040.Google ScholarGoogle ScholarCross RefCross Ref
  91. Taylor, I., Shields, M., Wang, I., and Harrison, A. 2007. The Triana workflow environment: Architecture and applications. In Workflows for e-Science I. Taylor, E. Deelman, D. Gannon, and M. Shields, Eds., Springer, London, U.K. 320--339.Google ScholarGoogle Scholar
  92. Todorovski, L., Blockeel, H., and Džeroski, S. 2002. Ranking with predictive clustering trees. In Proceedings of the 13th European Conference on Machine Learning, Lecture Notes in Computer Science, vol. 2430, Springer, 444--455. Google ScholarGoogle ScholarDigital LibraryDigital Library
  93. Vanschoren, J. 2010. Understanding machine learning performance with experiment databases. Ph.D. dissertation, Katholieke Universiteit Leuven, Fianders, Belgium.Google ScholarGoogle Scholar
  94. Vanschoren, J., Blockeel, H., Pfahringer, B., and Holmes, G. 2012. Experiment databases: A new way to share, organize and learn from experiments. Machine Learn. DOI 10.1007/s10994-011-5277-0. To appear. Google ScholarGoogle ScholarDigital LibraryDigital Library
  95. Vilalta, R. and Drissi, Y. 2002a. A characterization of difficult problems in classification. In Proceedings of the International Conference on Machine Learning and Applications (ICMLA). 133--138.Google ScholarGoogle Scholar
  96. Vilalta, R. and Drissi, Y. 2002b. A perspective view and survey of meta-learning. Artif. Intell. Rev. 18, 77--95. Google ScholarGoogle ScholarDigital LibraryDigital Library
  97. Wirth, R., Shearer, C., Grimmer, U., Reinartz, T., Schlosser, J., Breitner, C., Engels, R., and Lindner, G. 1997. Towards process-oriented tool support for knowledge discovery in databases. In Proceedings of the 1st European Symposium on Principles of Data Mining and Knowledge Discovery, Lecture Notes in Computer Science, vol. 1263, Springer, 243--253. Google ScholarGoogle ScholarDigital LibraryDigital Library
  98. Wolpert, D. 2001. The supervised learning no-free-lunch theorems. In Proceedings of the Online World Conference on Soft Computing in Industrial Applications. 25--42.Google ScholarGoogle Scholar
  99. Yang, G., Kifer, M., Zhao, C., and Chowdhary, V. 2002. Flora-2: Users Manual. Department of Computer Science, Stony Brook University, Stony Brook, NY.Google ScholarGoogle Scholar
  100. Žáková, M., Křemen, P., Železný, F., and Lavrač, N. 2010. Automating knowledge discovery workflow composition through ontology-based planning. IEEE Tran. Autom. Sci. Eng. 8, 2, 253--264.Google ScholarGoogle ScholarCross RefCross Ref

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            cover image ACM Computing Surveys
            ACM Computing Surveys  Volume 45, Issue 3
            June 2013
            575 pages
            ISSN:0360-0300
            EISSN:1557-7341
            DOI:10.1145/2480741
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            Publication History

            • Published: 3 July 2013
            • Accepted: 1 February 2012
            • Revised: 1 January 2012
            • Received: 1 August 2011
            Published in csur Volume 45, Issue 3

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