Toward data mining engineering: A software engineering approach

Information Systems

Volumn 34, Issue 1, 2009, Pages 87-107

  Marbán, Oscar ^a   Segovia, Javier ^a   Menasalvas, Ernestina ^a   Fernández Baizán, Covadonga ^a  

^a UNIVERSIDAD POLITÉCNICA DE MADRID   (Spain)

Author keywords

Data mining; Knowledge engineering; Software engineering

Indexed keywords

COMPUTER SOFTWARE REUSABILITY; DATA MINING; DECISION SUPPORT SYSTEMS; ENGINEERING; INFORMATION MANAGEMENT; KNOWLEDGE BASED SYSTEMS; KNOWLEDGE MANAGEMENT; MINING; MINING ENGINEERING; SIGNAL PROCESSING; SOFTWARE ENGINEERING;

CURRENT STANDARDS; DATA MINING PROCESSES; DEVELOPMENT PROCESSES; ENGINEERING PROJECTS; KNOWLEDGE DISCOVERY IN DATABASES; MINING PROJECTS; SOFTWARE DEVELOPMENT PROCESSES; SOFTWARE ENGINEERING MODELS;

KNOWLEDGE ENGINEERING;

EID: 55349148370     PISSN: 03064379     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.is.2008.04.003     Document Type: Article

References (108)

1. P. Naur, B. Randell, Software engineering: report on a conference sponsored by the NATO science committee, 1969.
2. Pressman R.S. Software Engineering: A Practitioner's Approach. sixth ed. (2005), McGraw-Hill Science, New York
3. Piatetsky-Shaphiro G., and Frawley W. Knowledge Discovery in Databases (1991), AAAI/MIT Press, MA
4. ISL. Clementine User Guide, volume Version 5, ISL, Integral Solutions Limited, July 1995.
5. T. Khabaza, C. Shearer, Data mining with clementine, February 1995.
6. C. Shearer, User driven data mining, 1996, Unicom Data Mining Conference, London.
7. D.S. Tkach, Information mining with the ibm intelligent miner family, February 1998, IBM Software Solutions White Paper.
8. IBM, Application programming interface and utility reference. IBM DB2 Intelligent Miner for Data, IBM, September 1999.
9. Frank E., and Witten I.H. Data Mining: Practical Machine Learning Tools with Java Implementations. second ed. (2005), Morgan Kaufmann, Los Altos, CA
10. The Data Mining Research Group, DBMiner User Manual. Intelligent Database Systems Laboratory, Simnon Fraser University, December 1997.
11. P. Chapman (NCR), J. Clinton (SPSS), R. Kerber (NCR), T. Khabaza (SPSS), T. Reinartz (DaimlerChrysler), C. Shearer (SPSS), R. Wirth (DaimlerChrysler). CRISP-DM 1.0 step-by-step data mining guide, Technical Report, CRISP-DM, 2000.
12. Piatetsky-Shapiro G. Knowledge discovery in databases: 10 years after. SIGKDD Explor. Newsl. 1 2 (2000) 59-61
13. KdNuggets.Com. Data mining activity in 2007 vs 2006 〈http://www.kdnuggets.com/polls/2007/data_mining_2007_vs_2006.htm〉, October 2007.
14. L. DiLauro, What's next in monitoring technology? Data mining finds a calling in call centers, May 2000.
15. M.P. McDonald, T. Jaffarian, L. Mok, S. Stevens, Growing its contribution: then 2006 cio agenda. Gartner group 〈www.gartner.com〉, 2006.
16. B. Chatham, B.D. Temkin, K.M. Gardiner, T. Nakashima, CRM's future: humble growth through 2007, July 2002.
17. B. Eisenfeld, E. Kolsky, T. Topolinski, 42 percent of CRM software goes unused. 〈www.gartner.com〉, February 2003.
18. B. Eisenfeld, E. Kolsky, T. Topolinski, D. Hagemeyer, J. Grigg, Unused CRM software increases TCO and decreases ROI. 〈www.gartner.com〉, Febrero 2003.
19. A. Zornes, The top 5 global 3000 data mining trends for 2003/04, META Group Research-Delta Summary, 2061, March 2003.
20. Edelstein H.A., and Edelstein H.C. Building, Using, and Managing the Data Warehouse. Data Warehousing Institute. first ed. (1997), Prentice-Hall PTR, Englewood Cliffs, NJ
21. M. Strand, The Business Value of Data Warehouses-Opportunities, Pitfalls and Future Directions. Ph.D. Thesis, Department of Computer Science, University of Skövde, December 2000.
22. J.E. Gondar. Metodología Del Data Mining. No. 84-96272-21-4. Data Mining Institute, S.L., 2005.
23. KdNuggets.Com. 〈http://www.kdnuggets.com/polls〉, 2002.
24. KdNuggets.Com. 〈http://www.kdnuggets.com/polls〉, 2004.
25. KdNuggets.Com. 〈http://www.kdnuggets.com/polls/2007/data_mining_methodology.htm〉, 2007.
26. KdNuggets.Com. Is data mining a mature field? 〈http://www.kdnuggets.com/polls/2007/data_mining_mature_field.htm〉, September 2007.
27. Kurgan L.A., and Musilek P. A survey of knowledge discovery and data mining process models. Knowl. Eng. Rev. 21 1 (2006) 1-24
28. P. Richards J. McCall, G. Walters, Factors in software quality, NTIS AD-A049-014, 015(055), November 1977.
29. S. Tyrrell, The many dimensions of the software process. ACM Crossroads, 6.4, 2000.
30. Moore J. Software Engineering Standards: A User's Road Map (1998), IEEE Computer Society, Los Alamitos, CA
31. Fayyad U., Piatetsky-Shapiro G., Smith P., and Uthurusamy R. Advances in Knowledge Discovery and Data Mining (1996), AAAI/MIT Press, MA
32. Two Crows Corporation, Introduction to Data Mining and Knowledge Discovery, second ed., Two Crows Corporation, 1998. ISBN 892095-00-0.
33. Two Crows Corporation, Introduction to Data Mining and Knowledge Discovery, third ed., Two Crows Corporation, 1999, ISBN: 1-892095-02-5.
34. SAS Institute Inc., From Data to Business Advantage: Data Mining, SEMMA Methodology and the SAS System (White Paper). SAS Institute Inc., 1997.
35. SAS Institute, SEMMA data mining methodology 〈http://www.sas.com/technologies/analytics/datamining/miner/semma.html〉, 2005.
36. F.J. Martínez de Pisón Ascacibar. Optimización Mediante Técnicas de Minería de Datos Del Ciclo de Recocido de Una Línea de Galvanizado. Ph.D. Thesis, Universidad de La Rioja, 2003.
37. Pyzdek T. The Six Sigma Handbook. second ed. (2003), McGraw-Hill, New York
38. M. Harry, R. Schroeder, Six Sigma, The Breakthrough Management Strategy Revolutionizing The World's Top Corporations, Currency, 1999.
39. Dyché J. The CRM Handbook: A Business Guide to Customer Relationship Management. first ed. (2001), Addison-Wesley Pub Co., Reading, MA
40. Jose Solarte. A proposed data mining methodoloy and its aplication to industrial engineering, Master's Thesis, University of Tennessee, Knoxville, 2002.
41. Market ConsulTek 〈http://www.marketconsulteks.com〉, 2005.
42. Jacobson I., Booch G., and Rumbaugh J. The Unified Software Development Process (1999), Addison-Wesley Longman Inc.
43. Shearer C. The CRISP-DM model: The new blueprint for data mining. J. Data Warehousing 15 4 (2000) 13-19
44. R. Wirth, J. Hipp, CRISP-DM: Towards a standard process model for data mining, in: Proceedings of the 4th International Conference on the Practical Applications of Knowledge Discovery and Data Mining, Manchester, UK, 2000, pp. 29-39.
45. IEEE, Standard for Developing Software Life Cycle Processes. IEEE Std. 1074-1997. IEEE Computer Society, Nueva York (EE.UU.), 1991.
46. ISO, ISO/IEC Standard 12207:1995, Software Life Cycle Processes, Int. Organization for Standarization, Ginebra, Suiza, 1995.
47. ISO, ISO/IEC Standard 15504:2004, Software Process Improvement and Capability dEtermination. International Organization for Standarization, Ginebra, Suiza, 2004.
48. KdNuggets.Com. Outsourcing data mining 〈http://www.kdnuggets.com/polls/2007/outsourcing_data_mining.htm〉, February 2007.
49. O. Marbán. Modelo Matemático Paramétrico de Estimación Para Proyectos de Data Mining (DMCoMo). Ph.D. Thesis, Facultad de Informática, Universidad Politécnica de Madrid, June 2003.
50. C. Fernandez-Baizan O. Marban, E. Menasalvas. A cost model to estimate the effort of data mining projects (DMCoMo), Inf. Syst., 2007.
51. Boehm B.W., Abts C., Brown A.W., Chulani S., Clark B.K., Horowitz E., Madachy R., Reifer D., and Steece B. Software Cost Estimation with COCOMO II (2000), Prentice-Hall, Englewood Cliffs, NJ
52. Pai W.C. Hierarchical analysis for discovering knowledge in large databases. Inf. Syst. Manage. 21 (2004) 81-88
53. Scott P.D., and Wilkins E. Evaluating data mining procedures: Techniques for generating artificial data sets. Inf. Software Technol. 41 (1999) 579-587
54. K.-E. Biebler, M. Wodny, B. Jager, Data mining and metrics on data sets, in: Proceedings of the International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce Vol-1 (CIMCA-IAWTIC'06), CIMCA '05, IEEE Computer Society, Washington, DC, USA, 2005, pp. 638-641.
55. M. Smith, A. Khotanzad, Quality metrics for object-based data mining applications. in: Proceedings of the International Conference on Information Technology, ITNG '07: IEEE Computer Society, Washington, DC, USA, 2007, pp. 388-392.
56. Reifer D.J. Software Management. seventh ed. (2006), Wiley-IEEE Computer Society Press
57. S. Biffl, D. Winkler, Value-based empirical research plan evaluation. In: First International Symposium on Empirical Software Engineering and Measurement, 2007, IEEE, 2007, pp. 494-494.
58. Software Engineering NASA LaRC and Analysis Lab, Software Engineering Process Guidebook, Software Configuration Management Planning, NASA, December 1995.
59. Joint Financial Management Improvement Program, White paper: Parallel operation of software-is it a desirable software transition technique?, 2001.
60. Hackos J.T. Managing your documentation projects (1994), Wiley, New York, NY, USA
61. Metschke C.J. Secrets of successful documentation projects. Intercom 43 3 (1996)
62. ERIC, Guideline for Software Documentation Management. ERIC, Springfield, VA 22161, 1984.
63. Haneef N.J. Software documentation and readability: a proposed process improvement. SIGSOFT Software Eng. Notes 23 3 (1998) 75-77
64. C. McNamara, Complete guidelines to design your training plan. 〈http://www.managementhelp.org/trng_dev/gen_plan.htm〉, 2007.
65. Kraiger K. Creating, Implementing, & Managing Effective Training & Development: State-of-the-Art Lessons for Practice. first ed. (2001), Pfeiffer
66. Hughes B., and Cotterell M. Software Project Management. fourth ed. (2005), McGraw-Hill Education Europe
67. N. Jenkins, Project Management Primer, Creative Commons (Attribution-NonCommercial- ShareAlike) 2.5 License, 〈http://www.nickjenkins.net/prose/projectPrimer.pdf〉, 2006.
68. J. Westland, The Project Management Life Cycle A Complete Step-by-Step Methodology for Initiating, Planning, Executing and Closing the Project Successfully, Kogan, 2006.
69. Fenton N.E., and Lawrence Pfleeger S. Software Metrics: A Rigorous and Practical Approach. second ed. (1998), Course Technology
70. Kan S.H. Metrics and Models in Software Quality Engineering (1995), Addison-Wesley, Reading, MA
71. Offen R.J., and Jeffery R. Establishing software measurement programs. IEEE Software 14 (1997) 45-53
72. Shepperd M. Foundations of Software Measurement (1996), Prentice-Hall, Englewood Cliffs, NJ
73. G. Pistesky-Shaphiro, An overview of knowledge discovery in databases: recent progress and challenges, Rouhg Sets, Fuzzy Sets and Knowledge Discovery, 1994, pp. 1-11.
74. Eriksson E.H. Business Modeling with UML, Business Patterns at Work (2000), Wiley Computer Publishing
75. J. Gordijn, H. Akkermans, H. van Vliet. Business modelling is not process modelling. in: ER Workshops, 2000, pp. 40-51.
76. J. Gordijn B. Van der Raadt, E. Yu. Exploring web services from a business value perspective, in: 13th IEEE International Conference on Requirements Engineering, IEEE, Paris, 2005, pp. 53-62.
77. Pigneur Y., Osterwalder A., and Tucci Y. Clarifying business models: Origins, present, and future of the concept. Commun. AIS 15 (2005) 1-40
78. G. Kotonya, I. Sommerville, Requirements Engineering. Processes and Techniques, Wiley, USA, 1998.
79. Robertson S., and Robertson J. Mastering the Requirements Process. second ed. (2006), Addison-Wesley Professional
80. Wiegers K.E. Software Requirements. second ed. (2003), Microsoft Press
81. Henderson V., Bersoff E., and Siegel S. Software Configuration Management (1980), Prentice-Hall, Englewood Cliffs, NJ
82. Babich W. Software Configuration Management (1986), Addison-Wesley, Reading, MA
83. Buckley F. Configuration Management: Hardware, Software and Firmware (1992), IEEE Computer Society Press, USA
84. ANSI/IEEE, IEEE Standard for Software Unit Testing, ANSI/IEEE Std 1008, ANSI/IEEE, 1987.
85. Laski J. Testing in the program development cycle. Software Eng. J. 4 2 (1989) 95-106
86. IEEE/ANSI, IEEE Guide to Software Configuration Management. IEEE/ANSI Standard 1042-1987, IEEE/ANSI, 1987.
87. IEEE/ANSI, IEEE Standard for Software Configuration Management Plans. IEEE/ANSI Standard 828, IEEE/ANSI, 1990.
88. Harold Stolovitch, Telling Ain't Training, first ed., ASTD, 2002.
89. Lavrac N., Motoda H., Fawcett T., Holte R., Langley P., and Adriaans P. Lessons learned from data mining applications and collaborative problem solving. Mach. Learn. 57 (2004) 13-34
90. A. Geyer-Schulz, M. Hahsler, Comparing two recommender algorithms with the help of recommendations by peers, in: O.R. Zaiane, J. Srivastava, M. Spiliopoulou, B. Masand, (Eds.), WEBKDD 2002-Mining Web Data for Discovering Usage Patterns and Profiles, 4th International Workshop, Edmonton, Canada, July 2002, Revised Papers, Lecture Notes in Computer Science, Lecture Notes in Artificial Intelligence, vol. 2703, Springer, Berlin, 2003, pp. 137-158.
91. K. Rennolls, An intelligent framework (o-ss-e) for data mining, knowledge discovery and business intelligence, in: First International Workshop on Philosophies and Methodologies for Knowledge Discovery in 16th International Workshop on Database and Expert Systems Applications (DEXA 2005), August 2005, pp. 715-719.
92. Clifton C., and Thuraisingham B. Emerging standards for data mining. Comput. Stand. Interfaces 23 3 (2001) 187-193
93. CRISP-DM Consortium, Crisp-2.0: Updating the methodology 〈http://www.crisp-dm.org/new.htm〉.
94. Yang Q., and Wu X. 10 challenging problems in data mining research. Int. J. Inf. Technol. Decision Making 5 4 (2006) 597-604
95. H. Stoyan O. Hogl, M. Müller, The knowledge discovery assistant: making data mining available for business users, in: ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, 2000, pp. 106-114.
96. Becker K., and Ghedini C. A documentation infrastructure for the management of data mining projects. Inf. Software Technol. 47 2 (2005) 95-111
97. Françoise Fogelman Soulié, Data mining in the real world. what do we need and what do we have ? in: Workshop on Data Mining for Business Applications. Held in conjunction with The 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2006.
98. Bendoly E. Theory and support for process frameworks of knowledge discovery and data mining from erp systems. Inf. Manage. 40 7 (2003) 639-647
99. K. Rennolls, Visualization and bayesian nets to link business aims through kdd to deployment, in: 17th International Conference on Database and Expert Systems Applications (DEXA'06), IEEE Computer Society, Los Alamitos, CA, USA, 2006, pp. 603-607.
100. E. Koutsofios, T. Dasu, J.R. Wright, Zen and the art of data mining, in: Workshop on Data Mining for Business Applications. Held in conjunction with The 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2006.
101. V. Cannella, G. Russo, D. Peri, R. Pirrone, E. Ardizzone, Towards MKDA: a knowledge discovery assistant for researches in medicine, in: AI*IA 2007: Artificial Intelligence and Human-Oriented Computing, Lecture Notes in Computer Science, vol. 4733, Springer, Berlin, 2007, pp. 773-780.
102. Feelders A., Daniels H., and Holsheimer M. Methodological and practical aspects of data mining. Inf. Manage. 37 5 (2000) 271-281
103. M. Hofmann, The development of a generic data mining life cycle (DMLC), Ph.D. Thesis, Dublin Institute of Technology, 2003.
104. M. Castellano, G. Mastronardi, F. Fiorino, G. Bellone de Grecis, F. Arcieri, V. Summo, E-Service Intelligence, in: Orchestrating the Knowledge Discovery Process, Springer, Berlin, 2007, pp. 477-496.
105. C.C. Alves Edilberto Silva, B.A.C. Pereira, T.G. Viott, Metodologia para desenvolvimento de sistemas de suporte a decis ao crisp-dm utilizando a notaç ao uml, in: Uma Abordagem Aplicada à Gerência de Atendimento Hospitalar (outubro/2007). In: SBIC-I Simpósio Brasileiro de Inteligência Computacional, Universidade Federal de Santa Catarina, Florianópolis, October 2007.
106. Zubcoff J., and Trujillo J. A uml 2.0 profile to design association rule mining models in the multidimensional conceptual modeling of data warehouses. Data Knowl. Eng. 63 1 (2007) 44-62
107. J. Pardillo, J. Zubcoff, J. Trujillo, Integrating clustering data mining into the multidimensional modeling of data warehouses with uml profiles, in: Data Warehousing and Knowledge Discovery, Springer, Berlin, 2007, pp. 199-208.
108. J. Zubcoff, J. Trujillo, Conceptual modeling for classification mining in data warehouses. in: Data Warehousing and Knowledge Discovery, Lecture Notes in Computer Science, Vol. 4081, Springer, Berlin, 2006, pp. 566-575.