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A Neuro- Fuzzy Approach for Formulating Survey and Managing recorded Information in Data Warehouses

by Rajdev Tiwari, Manu Pratap Singh
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 26 - Number 5
Year of Publication: 2011
Authors: Rajdev Tiwari, Manu Pratap Singh
10.5120/3103-2199

Rajdev Tiwari, Manu Pratap Singh . A Neuro- Fuzzy Approach for Formulating Survey and Managing recorded Information in Data Warehouses. International Journal of Computer Applications. 26, 5 ( Feb 2011), 1-9. DOI=10.5120/3103-2199

@article{ 10.5120/3103-2199,
author = { Rajdev Tiwari, Manu Pratap Singh },
title = { A Neuro- Fuzzy Approach for Formulating Survey and Managing recorded Information in Data Warehouses },
journal = { International Journal of Computer Applications },
issue_date = { Feb 2011 },
volume = { 26 },
number = { 5 },
month = { Feb },
year = { 2011 },
issn = { 0975-8887 },
pages = { 1-9 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume26/number5/3103-2199/ },
doi = { 10.5120/3103-2199 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:12:37.161783+05:30
%A Rajdev Tiwari
%A Manu Pratap Singh
%T A Neuro- Fuzzy Approach for Formulating Survey and Managing recorded Information in Data Warehouses
%J International Journal of Computer Applications
%@ 0975-8887
%V 26
%N 5
%P 1-9
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The major activity of Business Intelligence (BI) is to dig out the various trends and patterns from variety of authentic sources that helps the managers to take appropriate decision in framing the policies for business plans accordingly. Surveys are considered to be an essential part of BI. Surveys conducted amongst different or same groups by different team may yield conflicting reports. Moreover the recorded answers during the surveys may even contain a lot of vagueness in it. This paper suggests and implements a neuro-fuzzy approach for processing and storing the vague information captured during the surveys. This approach shall help the personnel involve in BI to get the more appropriate analysis based on human like reasoning, out of the Data Warehouse (DW) as compared to the DWs based on the crisp values only.

References
  1. Mamdouh Refaat (2007). Data Preparation for Data Mining Using SAS”, Morgan Kaufmann Publishers , pp.44-47
  2. Das, Shubhabrata(2006). On measuring imprecision in human response due to respondent and attribute and its utility in questionnaire design. In International Journal of Uncertainty, Fuzziness and Knowlege-Based Systems, v 14, n 2, p 155-173.
  3. Chen, Yen-Liang ;Weng, Cheng-Hsiung (2009). Mining fuzzy association rules from questionnaire data. In Knowledge-Based Systems, v 22, n 1, p 46-56.
  4. Marshall, G. (2005). The purpose, design and administration of a questionnaire for data collection, Radiography 11 (2) 131–136.
  5. Groves, Robert M. & Heeringa, Steven G.( 2006). Responsive design for household surveys: tools for actively controlling survey errors and costs. J.R.Statist.Soc.A, 169(3), 1-19
  6. Nikravesh, Masoud (2008). Concept-based search and questionnaire systems. In Soft Computing, v 12, n 3, p 301-314, Special issue on BISCSE 2005 'Forging the Frontiers' Part II.
  7. Chang, S.E., Changchien, S.W., Huang, R.H. (2006). Assessing users’ product-specific knowledge for personalization in electronic commerce, Expert Systems with Applications 30 (4) 682–693
  8. Lafuente, Ruben ; Page, Alvaro; Sanchez-Lacuesta, Javier; Tortosa, Lourdes (1998). Application of fuzzy logic techniques for the qualitative interpretation of preferences in a collective questionnaire for users of wheelchairs. In Journal of Rehabilitation Research and Development, v 35, n 1, p 91-107.
  9. NSSO (2001). Concepts and Definitions used by NSSO, Golden Jubilee Publication .
  10. Mohanan, P. C. & Sourav Chakrabortty (2008). Inter-State Comparisons of Housing conditions – A study based on NSS 58th round. In Journal of National Sample Survey Organization, 94 Issue, Vol. XXVIII No. 3&4.
  11. Zadeh, L.A. (1965) Fuzzy sets. In Information and control, 8, pp. 338-353.
  12. Pedrycz, W., & Gomide, F. (1998). An introduction to fuzzy sets: Analysis and design (A Bradford Book). The MIT Press.
  13. Piegat, A. (2001). Fuzzy modeling and control. In Physica-Verlag (Studies in Fuzziness and Soft Computing).
  14. Buckley, J.J., & Eslami, E.( 2002). An Introduction to fuzzy logic and fuzzy sets (advances in soft computing). Physica-Verlang Heidelberg.
  15. Nguyen, H.T., & Walker, E.A.( 2005). A first course in fuzzy logic (3rd ed.). Chapman & Hall/CRC.
  16. Wang, Y. & Rong, G. (1999). A self-organizing neural-network-based fuzzy system. In Fuzzy Sets and Systems.
  17. Shi, Y. & Mizumoto, M. (2000). A new approach of neuro-fuzzy learning algorithm for tuning fuzzy rules. In Fuzzy Sets and Systems.
  18. Yang, Y., Xu, X. & Zang, W. (2000). Design neural networks based fuzzy logic. In Fuzzy Sets and Systems.
  19. Ian Brace (2004). Questionnaire Design,. In Kogan Page,.
  20. Groves, Robert, M.; Benson, Grant; Mosher, William; Rosenbaum, Jennifer; Granda, Peter; Axinn, William; Lepkowski, James; Chandra, Anjani.( 2005) “Plan and Operation of Cycle 6 of the National Survey of Family Growth”. Hyattsville, MD: National Center for Health Statistics, Vital Health Statistics, 1(42).
  21. Rajdev Tiwari & Manu Pratap Singh (2010). Correlation-based Attribute Selection using Genetic Algorithm. In International Journal of Computer Applications (0975 – 8887) Volume 4– No.8.
Index Terms

Computer Science
Information Sciences

Keywords

Neuro-Fuzzy Fuzzy Information Fuzzy sources Data Warehouse Questionnaire Business Intelligence Surveys

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