Knowledge discovery standards

Artificial Intelligence Review

Volumn 27, Issue 1, 2007, Pages 21-56

  Anand, Sarabjot Singh ^a   Grobelnik, Marko ^b   Herrmann, Frank ^c   Hornick, Mark ^d   Lingenfelder, Christoph ^e   Rooney, Niall ^f   Wettschereck, Dietrich ^c  

^a UNIVERSITY OF WARWICK   (United Kingdom)

^b JO EF STEFAN INSTITUTE   (Slovenia)

^c ROBERT GORDON UNIVERSITY   (United Kingdom)

^d ORACLE CORPORATION   (United States)

^e University of Ulster at Jordanstown ^*  (United States)

^f UNIVERSITY OF ULSTER   (United Kingdom)

Author keywords

CRISP DM; Data Mining; JDM; Knowledge discovery; OLE DB; PMML; Standards

Indexed keywords

DECISION SUPPORT SYSTEMS; INFORMATION MANAGEMENT; KNOWLEDGE BASED SYSTEMS; KNOWLEDGE REPRESENTATION; MATHEMATICAL MODELS; SIGNAL PROCESSING; STANDARDS; TECHNOLOGY;

CRISP-DM; DATA MINING; JDM; KNOWLEDGE DISCOVERY; OLE-DB; PMML;

STANDARDIZATION;

EID: 54049125639     PISSN: 02692821     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10462-008-9067-4     Document Type: Article

References (33)

1. Anand S, Büchner A (1998) Data mining for decision support. Financial Times Management
2. I Bhandari E Colet J Parker Z Pines R Pratap K Ramanujam 1997 Advanced scout: data mining and knowledge discovery in NBA data Data Mining Knowl Discov 1 121 125
3. Blake C, Merz C (1998) UCI repository of machine learning databases. http://www.ics.uci.edu/~mlearn/MLRepository.html
4. Brachman R, Anand T (1994) The process of knowledge discovery in databases: a first sketch. In: Fayyad U, Uthurusamy R (eds) Knowledge discovery in databases: papers from the 1994 AAAI Workshop. Seattle, Washington, AAAI Press, pp 1-12
5. Chapman P, Clinton J, Kerber R, Khabaza T, Reinartz T, Shearer C, Wirth R (2000) Crisp-dm 1.0: Step-by-step data mining guide. http://www.crisp-dm.org
6. CWM (2004) Common warehouse metamodel (CWM). http://www.omg.org/ technology/documents/formal/cwm.htm
7. DB2-IM (2004) DB2 intelligent miner for data. http://www-306.ibm.com/ software/data/iminer/fordata/
8. Eisenberg A, Melton J (2001) SQL multimedia and application packages (SQL/MM). SIGMOD RECORD 30(4)
9. Farrand J, Flach P (2003) ROCOn: a tool for visualising ROC graphs. http://www.cs.bris.ac.uk/
10. Fayyad U, Piatetsky-Shapiro G, Smyth P (1996a) From data mining to knowledge discovery: an overview. In: Fayyad U, Piatetsky-Shapiro G, Smyth P, Uthurusamy R(eds) Advances in knowledge discovery and data mining. AAAI Press/The MIT Press, Menlo Park, CA, pp 1-34
11. U Fayyad G Piatetsky-Shapiro P Smyth 1996 The KDD process for extracting useful knowledge from volumes of data Commun ACM 39 11 27 34
12. Grossman R, Kamath C, Kegelmeyer P, V Kumar RN (2001) Data mining for scientific and engineering applications. Kluwer Academic Publishers
13. Grossman R, Hornick M, Meyer G (2002) Data mining standards initiatives. Commun ACM 45(8), http://www.dmg.org
14. ISO/IEC 9075:2003 (2003) ISO/IEC 9075:2003 Database Language SQL
15. ISO/IEC CD 13249-6 (2004) Information technology-database languages-SQL multimedia and application packages-Part 6: Data mining
16. JCA (2003) Java connection architecture. http://www.xmla.org/faq.asp
17. JCP (1995) The Java community process. http://jcp.org/en/home/index
18. JDM (2004) Java specification request 73. http://www.jcp.org/en/jsr/ detail?id=73
19. Jorge A, Poças J, Azevedo P (2002) Post-processing operators for browsing large sets of association rules. In: Lange S, Satoh K, Smith C (eds) Proceedings of discovery science 02, Springer-Verlag, Lübeck, Germany, vol LNCS 2534
20. Klösgen W (1996) EXPLORA: a multipattern and multistrategy discovery assistant. In: Fayyad U, Piatetsky- Shapiro G, Smyth P, Uthurusamy R (eds) Advances in knowledge discovery and data mining. AAAI Press, Menlo Park, CA, pp 249-271
21. Klösgen W, Zytkow J (eds) (2002) Knowledge discovery in databases: the purpose, necessity, and challenges, Handbook of data mining and knowledge discovery. Oxford University Press, Inc.
22. Li J (2003) PMML output and visualization for WEKA. PhD thesis, Department of Computer Science, University of Bristol. http://www.cs.bris.ac.uk/ home/jl2092/project/thesis.pdf
23. OLE-DB (2000) OLE-DB for data mining specification 1.0. Microsoft. http://msdn2.microsoft.com/en-us/library/ms146608.aspx
24. Oracle (2004) Oracle data mining. http://www.oracle.com/technology/ products/bi/odm/index.html
25. F Provost T Fawcett 2001 Robust classification for imprecise environments Mach Learn 42 3 203 231
26. Pyle D (2004) Nine simple rules you won't want to follow. DB2 magazine 9(1)
27. Raedt LD (2002) A perspective on inductive databases. ACM SIGKDD Explor Newslett 4(2)
28. SOAP (2004) Simple object access protocol (SOAP). http://www.w3.org/TR/ SOAP/
29. Tang Z, Kim P (2004) Building data mining solutions with SQL Server 2000. http://www.dmreview.com/whitepaper/wid292.pdf
30. Wettschereck D, Jorge A, Moyle S (2003) Data mining and decision support integration through the predictive model markup language standard and visualization. In: Data mining and decision support: integration and collaboration. Kluwer
31. Witten I, Frank E (1999) Data mining: practical machine learning tools with Java implementations. Morgan Kaufmann, San Francisco
32. XMLA (2004) XML for analysis (XMLA). http://www.xmla.org/
33. XMLA-spec (2004) XML for analysis (XMLA) specification version 1.1. http://www.xmla.org/docs_pub.asp