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Competitive advantage from Data Mining: some lessons learnt in the Information Systems field

PMKD’05 Copenhagen, Denmark August 22-26, 2005. Competitive advantage from Data Mining: some lessons learnt in the Information Systems field. Mykola Pechenizkiy , Seppo Puuronen Department of Computer Science University of Jyväskylä Finland Alexey Tsymbal

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Competitive advantage from Data Mining: some lessons learnt in the Information Systems field

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  1. PMKD’05 Copenhagen, Denmark August 22-26, 2005 Competitive advantage from Data Mining: some lessons learnt in the Information Systems field Mykola Pechenizkiy, Seppo Puuronen Department of Computer ScienceUniversity of Jyväskylä Finland Alexey Tsymbal Department of Computer ScienceTrinity College DublinIreland

  2. Outline • Introduction and What is our message? • Part I: Existing frameworks for DM • Theory-oriented: Databases; Statistics; Machine learning; etc • Process-oriented: Fayyad’s, CRISP, Reinartz’s • Part II: Where we are? – rigor vs. relevance in DM • Part III: Towards the new framework for DM research • DM System as adaptive Information System (IS) • DM research as IS Development: DM system as artefact • DM success model: success factors • KM Challenges in KDD • One possible reference for new DM research framework • Further plans and Discussion PMKD’05 Copenhagen, Denmark August 22-26, 2005 Competitive advantage from DM: lessons learnt in the IS field by M. Pechenizkiy, S. Puuronen, A. Tsymbal

  3. What is Data Mining Data mining or Knowledge discoveryis the process of finding previously unknown and potentially interesting patterns and relations in large databases (Fayyad, KDD’96) Data mining is the emerging science and industry of applying modern statistical and computational technologies to the problem of finding useful patterns hidden within large databases (John 1997) Intersection of many fields: statistics, AI, machine learning, databases, neural networks, pattern recognition, econometrics, etc. PMKD’05 Copenhagen, Denmark August 22-26, 2005 Competitive advantage from DM: lessons learnt in the IS field by M. Pechenizkiy, S. Puuronen, A. Tsymbal

  4. H.Information Systems • H.0 GENERAL • H.1 MODELS AND PRINCIPLES • H.2 DATABASE MANAGEMENT • H.2.0 General • Security, integrity, and protection • H.2.8 Database Applications • Data mining • Image databases • Scientific databases • Spatial databases and GIS • Statistical databases • H.2.m Miscellaneous http://www.acm.org/class/1998/ valid in 2003 PMKD’05 Copenhagen, Denmark August 22-26, 2005 Competitive advantage from DM: lessons learnt in the IS field by M. Pechenizkiy, S. Puuronen, A. Tsymbal

  5. I. Computing Methodologies • I.2 ARTIFICIAL INTELLIGENCE • I.2.0 General • Cognitive simulation • Philosophical foundations • I.2.1 Applications and Expert Systems • I.2.2 Automatic Programming • I.2.3 Deduction and Theorem Proving • I.2.4 Knowledge Representation Formalisms and Methods • I.2.5 Programming Languages and Software • I.2.6 Learning • Analogies • Concept learning • Connectionism and neural nets • Induction • Knowledge acquisition • Language acquisition • Parameter learning • I.2.7 Natural Language Processing • I.2.m Miscellaneous • I.5 PATTERN RECOGNITION • I.5.0 General • I.5.1 Models • Deterministic • Fuzzy set • Geometric • Neural nets • Statistical • Structural • I.5.2 Design Methodology • Classifier design & evaluation • Feature evaluation & selection • Pattern analysis • I.5.3 Clustering • Algorithms • Similarity measures • I.5.4 Applications • Computer vision • Signal processing • Text processing • Waveform analysis PMKD’05 Copenhagen, Denmark August 22-26, 2005 Competitive advantage from DM: lessons learnt in the IS field by M. Pechenizkiy, S. Puuronen, A. Tsymbal

  6. G. Mathematics of Computing • G.3 PROBABILITY AND STATISTICS • Correlation and regression analysis • Distribution functions • Experimental design • Markov processes • Multivariate statistics • Nonparametric statistics • Probabilistic algorithms (including Monte Carlo) • Statistical computing PMKD’05 Copenhagen, Denmark August 22-26, 2005 Competitive advantage from DM: lessons learnt in the IS field by M. Pechenizkiy, S. Puuronen, A. Tsymbal

  7. Our Message • DM is still a technology having great expectations to enable organizations to take more benefit of their huge databases. • There exist some success stories where organizations have managed to have competitive advantage of DM. • Still the strong focus of most DM-researchers in technology-oriented topics does not support expanding the scope in less rigorous but practically very relevant sub-areas. • Research in the IS discipline has strong traditions to take into account human and organizational aspects of systems beside the technical ones. PMKD’05 Copenhagen, Denmark August 22-26, 2005 Competitive advantage from DM: lessons learnt in the IS field by M. Pechenizkiy, S. Puuronen, A. Tsymbal

  8. Our Message • Currently the maturation of DM-supporting processes which would take into account human and organizational aspects is still living its childhood. • DM community might benefit, at least from the practical point of view, looking at some other older sub-areas of IT having traditions to consider solution-driven concepts with a focus also on human and organizational aspects. • The DM community by becoming more amenable to research results of the IS community might be able to increase its collective understanding of • how DM artifacts are developed – conceived, constructed, and implemented, • how DM artifacts are used, supported and evolved, • how DM artifacts impact and are impacted by the contexts in which they are embedded. PMKD’05 Copenhagen, Denmark August 22-26, 2005 Competitive advantage from DM: lessons learnt in the IS field by M. Pechenizkiy, S. Puuronen, A. Tsymbal

  9. Part I • Existing Frameworks for DM • Theory-oriented • Databases; • Statistics; • Machine learning; • Data compression • Process-oriented • Fayyad’s • CRISP-DM • Reinartz’s PMKD’05 Copenhagen, Denmark August 22-26, 2005 Competitive advantage from DM: lessons learnt in the IS field by M. Pechenizkiy, S. Puuronen, A. Tsymbal

  10. Theory-Oriented Frameworks

  11. Database Perspective • DM as application to DBs • “In the same way business applications are currently supported using SQL-based API, the KKD applications need to be provided with application development support.” • query KDD objects, support for finding NNs, clustering, or discretization and aggregate operations. • Inductive databasesapproach • query concept should be applied also to data mining and knowledge discovery tasks • “there is no such thing as discovery, it is all in the power of the query language” • contain not only the data but the theory of the data as well Imielinski, T., and Mannila, H. 1996, A database perspective on knowledge discovery. Communications of the ACM, 39(11), 58-64. Boulicaut, J., Klemettinen, M., and Mannila, H. 1999, Modeling KDD processes within the inductive database framework. In Proceedings of the First International Conference on Data Warehousing and Knowledge Discovery, Springer-Verlag, London, 293-302 PMKD’05 Copenhagen, Denmark August 22-26, 2005 Competitive advantage from DM: lessons learnt in the IS field by M. Pechenizkiy, S. Puuronen, A. Tsymbal

  12. Reductionism Approach • Two basic Statistical Paradigms • “Statistical Experiment” • Fisher’s version, inductive principle of maximum likelihood • Neyman and Pearson-Wald’s version, inductive behaviour • Bayesian version, maximum posterior probability • “Statistical learning from empirical process” • “Structural Data Analysis” • SVD • Data mining  statistics - the issue of computational feasibility has a much clearer role in data mining than in statistics • data mining area approaches that emphasize on database integration, simplicity of use, and the understandability of results • theoretical framework of statistics does not concern much about data analysis as a process that includes several steps PMKD’05 Copenhagen, Denmark August 22-26, 2005 Competitive advantage from DM: lessons learnt in the IS field by M. Pechenizkiy, S. Puuronen, A. Tsymbal

  13. Machine Learning Approach • “let the data suggest a model” can be seen as a practical alternative to the statistical paradigm “fit a model to the data” • Constructive Induction – a learning process, two intertwined phases: construction of the “best” representation space and generating hypothesis in the found space (Michalski & Wnek, 1993). • Feature transformation (PCA, SVD, Random Projection) • Feature selection • LSI PMKD’05 Copenhagen, Denmark August 22-26, 2005 Competitive advantage from DM: lessons learnt in the IS field by M. Pechenizkiy, S. Puuronen, A. Tsymbal

  14. Data Compression Approach • Compress the data set by finding some structure or knowledge for it, where knowledge is interpreted as a representation that allows coding the data by using fewer amount of bits. • Theories should not be ad hoc that is they should not overfit the examples used to build it. • Occam’s razor principle,14th century. • "when you have two competing models which make exactly the same predictions, the one that is simpler is the better". Mehta, M., Rissanen, J., and Agrawal, R. 1995, MDL-based decision tree pruning. In U.M. Fayyad, R. Uthurusamy (Eds.) Proceedings of the KDD 1995, AAAI Press, Montreal, Canada, 216-221. PMKD’05 Copenhagen, Denmark August 22-26, 2005 Competitive advantage from DM: lessons learnt in the IS field by M. Pechenizkiy, S. Puuronen, A. Tsymbal

  15. Other Theoretical frameworks for DM • Microeconomic view • the key point is that data mining is about finding actionable patterns: the only interest is in patterns that can somehow be used to increase utility; • a decision theoretic formulation of this principle: the goal can be formulated in finding a decision x that tries to maximise utility function f(x). Kleinberg, J., Papadimitriou, C., and Raghavan, P. 1998, A microeconomic view of data mining, Data Mining and Knowledge Discovery2(4), 311-324 • Philosophy of Science • logical empiricism, critical rationalism, systems theory • formism, mechanism, contextualism • dispersive vs. integrative, analytical vs. synthetic theories • subjectivist vs. objectivist, nomothetic vs. ideographic, nominalism vs. realism, voluntarism vs. determinism, epistemological assumptions • Explanation, prediction, understanding PMKD’05 Copenhagen, Denmark August 22-26, 2005 Competitive advantage from DM: lessons learnt in the IS field by M. Pechenizkiy, S. Puuronen, A. Tsymbal

  16. Process-Oriented Frameworks

  17. Knowledge discovery as a process I Fayyad, U., Piatetsky-Shapiro, G., Smyth, P., Uthurusamy, R., Advances in Knowledge Discovery and Data Mining, AAAI/MIT Press, 1997. PMKD’05 Copenhagen, Denmark August 22-26, 2005 Competitive advantage from DM: lessons learnt in the IS field by M. Pechenizkiy, S. Puuronen, A. Tsymbal

  18. CRISP-DM http://www.crisp-dm.org/ PMKD’05 Copenhagen, Denmark August 22-26, 2005 Competitive advantage from DM: lessons learnt in the IS field by M. Pechenizkiy, S. Puuronen, A. Tsymbal

  19. KDD: “Vertical Solutions” Reinartz, T. 1999, Focusing Solutions for Data Mining. LNAI 1623, Berlin Heidelberg. PMKD’05 Copenhagen, Denmark August 22-26, 2005 Competitive advantage from DM: lessons learnt in the IS field by M. Pechenizkiy, S. Puuronen, A. Tsymbal

  20. Conclusion on different frameworks • Reductionist approach of viewing data mining as statistics has advantages of the strong background, and easy-formulated problems. • The data mining tasks concerning processed like clusterisation, regression and classification fit easily into these approaches. • More recent (process-oriented) frameworks address the issues related to a view of data mining as a process, and its iterative and interactive nature PMKD’05 Copenhagen, Denmark August 22-26, 2005 Competitive advantage from DM: lessons learnt in the IS field by M. Pechenizkiy, S. Puuronen, A. Tsymbal

  21. Part II Where we are? Rigor and Relevance in DM Reseach PMKD’05 Copenhagen, Denmark August 22-26, 2005 Competitive advantage from DM: lessons learnt in the IS field by M. Pechenizkiy, S. Puuronen, A. Tsymbal

  22. So, where are we? • Lin in Wu et al. notices that a new successful industry (as DM) can follow consecutive phases: • discovering a new idea, • ensuring its applicability, • producing small-scale systems to test the market, • better understanding of new technology and • producing a fully scaled system. • At the present moment there are several dozens of DM systems, none of which can be compared to the scale of a DBMS system. • This fact indicates that we are still in the 3rd phase in the DM area! PMKD’05 Copenhagen, Denmark August 22-26, 2005 Competitive advantage from DM: lessons learnt in the IS field by M. Pechenizkiy, S. Puuronen, A. Tsymbal

  23. Rigor vs Relevance in DM Research PMKD’05 Copenhagen, Denmark August 22-26, 2005 Competitive advantage from DM: lessons learnt in the IS field by M. Pechenizkiy, S. Puuronen, A. Tsymbal

  24. Where is the focus? • Still! … speeding-up, scaling-up, and increasing the accuracies of DM techniques. • Piatetsky-Shapiro : “we see many papers proposing incremental refinements in association rules algorithms, but very few papers describing how the discovered association rules are used” • Lin claims that the R&D goals of DM are quite different: • since research is knowledge-oriented while development is profit-oriented. • Thus, DM research is concentrated on the development of new algorithms or their enhancements, • but the DM developers in domain areas are aware of cost considerations: investment in research, product development, marketing, and product support. • However, we believe that the study of the DM development and DM use processes is equally important as the technological aspects and therefore such research activities are likely to emerge within the DM field. PMKD’05 Copenhagen, Denmark August 22-26, 2005 Competitive advantage from DM: lessons learnt in the IS field by M. Pechenizkiy, S. Puuronen, A. Tsymbal

  25. Part III Towards the new framework for DM research PMKD’05 Copenhagen, Denmark August 22-26, 2005 Competitive advantage from DM: lessons learnt in the IS field by M. Pechenizkiy, S. Puuronen, A. Tsymbal

  26. Organization DM Task(s) DMS (Artifact) DMS in the Kernel of an Organization Environment • DM is fundamentally application-oriented area motivated by business and scientific needs to make sense of mountains of data. • A DMS is generally used to support or do some task(s) by human beings in an organizational environment both having their desires related to DMS. • Further, the organization has its own environment that has its own interest related to DMS, e.g. that privacy of people is not violated. PMKD’05 Copenhagen, Denmark August 22-26, 2005 Competitive advantage from DM: lessons learnt in the IS field by M. Pechenizkiy, S. Puuronen, A. Tsymbal

  27. The ISs-based paradigm for DM Ives B., Hamilton S., Davis G. (1980). “A Framework for Research in Computer-based MIS” Management Science, 26(9), 910-934. “Information systemsare powerful instruments for organizational problem solving through formal information processing” Lyytinen, K., 1987, “Different perspectives on ISs: problems and solutions.” ACM Computing Surveys, 19(1), 5-46. PMKD’05 Copenhagen, Denmark August 22-26, 2005 Competitive advantage from DM: lessons learnt in the IS field by M. Pechenizkiy, S. Puuronen, A. Tsymbal

  28. Theory Building DM Artifact Development Observation Experimentation DM Artifact Development A multimethodological approach to the construction of an artefact for DM Adapted from: Nunamaker, W., Chen, M., and Purdin, T. 1990-91, Systems development in information systems research, Journal of Management Information Systems, 7(3), 89-106. PMKD’05 Copenhagen, Denmark August 22-26, 2005 Competitive advantage from DM: lessons learnt in the IS field by M. Pechenizkiy, S. Puuronen, A. Tsymbal

  29. Research methods in a paper on DM • Theoretical approach: theory creating • Hypothesis, new algorithm, etc. • Constructive approach • Prototype of a DM tool • Theoretical approach: theory testing and evaluation • Artificial, benchmark, real-world data • Evaluation techniques • Conclusion on theory PMKD’05 Copenhagen, Denmark August 22-26, 2005 Competitive advantage from DM: lessons learnt in the IS field by M. Pechenizkiy, S. Puuronen, A. Tsymbal

  30. Awareness of business problem Contextual Knowledge Action planning Artifact Development Business Knowledge Design Knowledge Artifact Evaluation Action taking Conclusion The Action Research and Design Science Approach to Artifact Creation PMKD’05 Copenhagen, Denmark August 22-26, 2005 Competitive advantage from DM: lessons learnt in the IS field by M. Pechenizkiy, S. Puuronen, A. Tsymbal

  31. System Quality Use Individual Impact Information Quality User Satisfaction Organizational Impact Service Quality DM Artifact Use: Success Model 1 of 3 Adapted from D&M IS Success Models PMKD’05 Copenhagen, Denmark August 22-26, 2005 Competitive advantage from DM: lessons learnt in the IS field by M. Pechenizkiy, S. Puuronen, A. Tsymbal

  32. DM Artifact Use: Success Model 2 of 3 • What are the key factorsof successful use and impact of DMS both at the individual and organizational levels. • how the system is used, and also supported and evolved, and • how the system impacts and is impacted by the contexts in which it is embedded. Coppock: the failure factors of DM-related projects. • have nothing to do with the skill of the modeler or the quality of data. • But those do include: • persons in charge of the project did not formulate actionable insights, • the sponsors of the work did not communicate the insights derived to key constituents, • the resultsdon't agree with institutional truths the leadership, communication skills and understanding of the culture of the organization are not less important than the traditionally emphasized technological job of turning data into insights PMKD’05 Copenhagen, Denmark August 22-26, 2005 Competitive advantage from DM: lessons learnt in the IS field by M. Pechenizkiy, S. Puuronen, A. Tsymbal

  33. DM Artifact Use: Success Model 3 of 3 • Hermiz communicated his beliefs that there are the four critical success factors for DM projects: • (1) having a clearly articulated business problem that needs to be solved and for which DM is a proper tool; • (2) insuring that the problem being pursued is supported by the right type of data of sufficient quality and in sufficient quantity for DM; • (3) recognizing that DM is a process with many components and dependencies – the entire project cannot be "managed" in the traditional sense of the business word; • (4) planning to learn from the DM process regardless of the outcome, and clearly understanding, that there is no guarantee that any given DM project will be successful. PMKD’05 Copenhagen, Denmark August 22-26, 2005 Competitive advantage from DM: lessons learnt in the IS field by M. Pechenizkiy, S. Puuronen, A. Tsymbal

  34. KM Perspective • A knowledge-driven approach to enhance the dynamic integration of DM strategies in knowledge discovery systems. • Focus here is on knowledge management aimed to organise a systematic process of (meta-)knowledge capture and refinement over time. • knowledge extracted from data • the higher-level knowledge required for managing DM techniques’ selection, combination and application • Basic knowledge management processes of • knowledge creation and identification, representation, collection and organization, sharing, adaptation, and application • DEXA’05: TAKMA WS paper&presentation are available PMKD’05 Copenhagen, Denmark August 22-26, 2005 Competitive advantage from DM: lessons learnt in the IS field by M. Pechenizkiy, S. Puuronen, A. Tsymbal

  35. New Research Framework for DM Research PMKD’05 Copenhagen, Denmark August 22-26, 2005 Competitive advantage from DM: lessons learnt in the IS field by M. Pechenizkiy, S. Puuronen, A. Tsymbal

  36. Further Work • Definition of Relevance concept in DM research • The revision of the book chapter • Further work on the new framework for DM research • Organization of Workshop or Special Track or Working conference on • more social directions in DM research likely with one of the focuses on IS as a sister discipline. Few options: • IRIS Scandinavian Conference on IS is one option • Next PMKD • Workshop in Jyväskylä PMKD’05 Copenhagen, Denmark August 22-26, 2005 Competitive advantage from DM: lessons learnt in the IS field by M. Pechenizkiy, S. Puuronen, A. Tsymbal

  37. Thank You! Feedback is very welcome: • Questions • Suggestions • Collaboration Book chapter draft is available on request from Mykola Pechenizkiy Department of Computer Science and Information Systems, University of Jyväskylä, FINLAND E-mail: mpechen@cs.jyu.fi Tel.: +358 14 2602472 Fax: +358 14 260 3011 http://www.cs.jyu.fi/~mpechen PMKD’05 Copenhagen, Denmark August 22-26, 2005 Competitive advantage from DM: lessons learnt in the IS field by M. Pechenizkiy, S. Puuronen, A. Tsymbal

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