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Business Intelligence: Achieving Fineness through Data, Text and Web Mining

by Jitendra Singh Tomar
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 128 - Number 12
Year of Publication: 2015
Authors: Jitendra Singh Tomar
10.5120/ijca2015906691

Jitendra Singh Tomar . Business Intelligence: Achieving Fineness through Data, Text and Web Mining. International Journal of Computer Applications. 128, 12 ( October 2015), 46-52. DOI=10.5120/ijca2015906691

@article{ 10.5120/ijca2015906691,
author = { Jitendra Singh Tomar },
title = { Business Intelligence: Achieving Fineness through Data, Text and Web Mining },
journal = { International Journal of Computer Applications },
issue_date = { October 2015 },
volume = { 128 },
number = { 12 },
month = { October },
year = { 2015 },
issn = { 0975-8887 },
pages = { 46-52 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume128/number12/22929-2015906691/ },
doi = { 10.5120/ijca2015906691 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:21:29.331557+05:30
%A Jitendra Singh Tomar
%T Business Intelligence: Achieving Fineness through Data, Text and Web Mining
%J International Journal of Computer Applications
%@ 0975-8887
%V 128
%N 12
%P 46-52
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

There is no more a dearth of information with business organization incorporating ICT at core of its business structure. The amount of data available has been enormously increasing with business growth, hence determining patterns and trends out of substantial data is a challenge. The mining technologies are used by the organization to quest limitless data for crucial insight and knowledge. Web, Data, and Text mining are the important tools applied by the organization to automate finding hidden patterns to formulate policies and achieve competitive advantages in all functional business areas. Through mining techniques applied on various data repositories, the business intelligence systems along with analytical tools could present valuable and competitive information to the planners so as to develop new avenues for business growth. The usage of text, data, and web mining is discussed in this paper with a witness that how it can address business leadership and risk management, and enhance business intelligence.

References
  1. Bill Palace, (1996) “Technology Note prepared for Management 274A” Anderson Graduate School of Management at UCLA.
  2. Trevor Hastie, Robert Tibshirani, and Jerome Friedman, (2008) “The Elements of Statistical Learning: Data Mining, Inference and Prediction,” New York, Springer-Verlag, ISBN- 0 387 95284-5
  3. Marti Hearst, (2003) “What Is Text Mining?” SIMS, UC Berkeley.
  4. Prof. Anita Wasilewska, (2011) “Web Mining Presentation 1” CSE 590 Data Mining, Stony Brook.
  5. Usama Fayyad, Gregory Piatetsky-Shapiro, and Padhraic Smyth, (1996) “From Data Mining to Knowledge Discovery in Databases,” AI Magazine, American Association for Artificial Intelligence AAAI, Vol. 17 No. 3.
  6. Meryem Duygun Fethi, Fotios Pasiouras (2010) “Assessing Bank Efficiency and Performance with Operational Research and Artificial Intelligence Techniques: A survey” European Journal of Operational Research, pp. 189–198, Elsevier.
  7. J.R. Quinlan, (1986) “Induction of Decision Trees”, Machine Learning, Kluwer Academic Publishers, Boston.
  8. Dave Smith (2010) “Using Data and Text Mining to Drive Innovation” PhUSE 2010, UK.
  9. Michael Goebel, Le Gruenwald, (1999) “A Survey Of Data Mining and Knowledge Discovery Software Tools,” SIGKDD Explorations, Vol. 1, Issue1. Pg 20, ACM SIGKDD.
  10. Judy Redfearn and the JISC Communications team, (2006) “What Text Mining can do” Briefing paper, ‘Joint Information Systems Committee’ JISC.
  11. Neto, J., Santos, A., Kaestner, C., Freitas, A. 2000) “Document Clustering and Text Summarization” In the Proceeding of the 4th International Conference Practical Applications of Knowledge Discovery and Data Mining PADD-2000, London, UK.
  12. Rafael Berlanga, Oscar Romero, Alkis Simitsis,Victoria Nebot, Torben Bach Pedersen, Alberto Abelló, María José Aramburu (2012) “Semantic Web Technologies for Business Intelligence” IGI.
  13. Sankar K. Pal, Varun Talwar, Pabitra Mitra, (2002) “Web Mining in Soft Computing Framework: Relevance, State of the Art and Future Directions” IEEE Transactions on Neural Networks, Vol. 13, No. 5.
  14. Federico Michele Facca, Pier Luca Lanzi, (2005) “Mining interesting knowledge from weblogs: a survey” Data & Knowledge Engineering, 53, Elsevier.
  15. Ralf Mikut, and Markus Reischl, (2011) “Data mining tools” Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, Vol. 1, Issue 5.
  16. IBM, SurfAid Analytics (2003).
  17. IBM Software Group Case Study (2010) “Great Canadian Gaming Corporation Leverages IBM Cognos 8: Solutions for Financial Consolidation and Reporting Standardization”.
  18. Atos, (2011) “Business Intelligence solutions: Decisions that are Better-Informed Leading to Long Term Competitive Advantage”.
  19. Mark Hall, Eibe Frank, Geoffrey Holmes, Bernhard Pfahringer, Peter Reutemann, and Ian H. Witten (2009) “The WEKA data mining software: an update” SIGKDD Explorer News.
  20. Christian Thomsen, Torben Bach Pedersen (2009) “A Survey of Open Source Tools for Business Intelligence” International Journal of Data Warehousing and Mining, Vol. 5, Issue 3, IGI Global.
  21. Oksana Grabova, Jerome Darmont, Jean-Hugues Chauchat, Iryna Zolotaryova (2010) “Business Intelligence for Small and Middle-Sized Enterprises” SIGMOD Rec. 39.
  22. Sang Jun Lee, Keng Siau, (2001) “A Review of Data Mining Techniques” Industrial Management and Data Systems, 101/1, MCB University Press.
  23. Shantanu Godbole, Shourya Roy, (2008) “Text Classification, Business Intelligence, and Interactivity: Automating C-Sat Analysis for Services Industry” KDD’08, ACM Las Vegas, USA.
  24. Thiagarajan Ramakrishnan, Mary C. Jones, Anna Sidorova, (2011) “Factors Influencing Business Intelligence and Data Collection Strategies: An empirical investigation”, Decision Support Systems.
  25. Aura-Mihaela Mocanu, Daniela Litan, Stefan Olaru, A. Munteanu (2010) “Information Systems in the Knowledge Based Economy” WSEAS Transactions on Business and Economics, Issue 1, Vol. 7.
  26. K. Laundon and J. Laundon (2012) “Enhancing Decision Making” Managing Information Systems: Managing the Digital Firm, Pearson Education, Pearson Hall.
  27. Byung-Kwon Park and Il-Yeol Song (2011) “Toward total business intelligence incorporating structured and unstructured data” In Proceedings of the 2nd International Workshop on Business intelligence and the WEB (BEWEB '11), ACM, NY, USA.
  28. Clifton Phua, Vincent Lee, Kate Smith, Ross Gayler, (2010) “A Comprehensive Survey of Data Mining-based Fraud Detection Research” Cornell University library, CoRR.
  29. Fitzsimons, M., Khabaza, T., and Shearer, C. (1993) “The Application of Rule Induction and Neural Networks for Television Audience Prediction” In Proceedings of ESOMAR/EMAC/AFM Symposium on Information Based Decision Making in Marketing, Paris, pp 69-82.
  30. Amir F. Atiya, (2001) “Bankruptcy Prediction for Credit Risk Using Neural Networks: A Survey and New Results” IEEE Transactions on Neural Networks, vol. 12, no. 4.
  31. M. Crouhy, D. Galai, and R. Mark, (2000) “A comparative analysis of current credit risk models,” J. Banking & Finance, vol. 24, pp. 59–117.
  32. MAIA Intelligence (2009) “Business Intelligence in Manufacturing”.
  33. Injazz J. Chen,K. Popovich, (2003) "Understanding Customer Relationship Management (CRM): People, process and technology", Business Process Management Journal, Vol. 9, pp.672 –688.
  34. Dien D. Phan, Douglas R. Vogel, (2010) “A Model of Customer Relationship Management and Business Intelligence Systems for Catalogue and Online Retailers”, Information & Management, Vol. 47, Issue 2, Pages 69-77.
  35. Mu-Chen Chen, Cheng-Lung Huang, Kai-Ying Chen,Hsiao-Pin Wu, (2005) “Aggregation of Orders in Distribution Centers using Data Mining” Expert Systems with Applications, Volume 28, Issue 3, Pages 453-460, Elsevier.
  36. Van den Berg, J. P. (1999) “A literature survey on planning and control of warehousing systems” IIE Transactions, 31, PP.751–762.
  37. Y. Li, M.R. Kramer, A.J.M. Beulens, J.G.A.J. Van Der Vorst (2010) “A Framework for Early Warning and Proactive Control Systems in Food Supply Chain Networks” Computers in Industry, Vol. 61, Issue 9, pp. 852-862.
  38. Srinivasa Rao P, Saurabh Swarup (2001) “Business Intelligence and Logistics” Wipro Technologies.
  39. Gregory Piatetsky-Shapiro, Ron Brachman, Tom Khabaza (1996) “An Overview of Issues in Developing Industrial Data Mining and Knowledge Discovery Applications” KDD-96 Proceedings.
  40. K.A. Taipale (2003) "Data Mining and Domestic Security: Connecting the Dots to Make Sense of Data" Columbia Science and Technology Law Review 5.
  41. Carlos Rodríguez, Florian Daniel, F. Casati, Cinzia Cappiello (2010) “Toward Uncertain Business Intelligence: The Case of Key Indicators” Internet Computing, IEEE, vol.14, no.4, pp.32-40.
  42. K. Laundon and J. Laundon (2011) “Foundations of Business Intelligence: Databases and Information Management” Managing Information Systems: Managing the Digital Firm, Pearson Education Inc.
Index Terms

Computer Science
Information Sciences

Keywords

Data Mining Text Mining Web Mining Business Intelligence Information Systems Knowledge Management.

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