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Multidimensional Data Analysis Facilities and Challenges: A Survey for Data Analysis Tools

by Prarthana A. Deshkar, Parag S. Deshpande, A. Thomas
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 179 - Number 13
Year of Publication: 2018
Authors: Prarthana A. Deshkar, Parag S. Deshpande, A. Thomas
10.5120/ijca2018916178

Prarthana A. Deshkar, Parag S. Deshpande, A. Thomas . Multidimensional Data Analysis Facilities and Challenges: A Survey for Data Analysis Tools. International Journal of Computer Applications. 179, 13 ( Jan 2018), 28-33. DOI=10.5120/ijca2018916178

@article{ 10.5120/ijca2018916178,
author = { Prarthana A. Deshkar, Parag S. Deshpande, A. Thomas },
title = { Multidimensional Data Analysis Facilities and Challenges: A Survey for Data Analysis Tools },
journal = { International Journal of Computer Applications },
issue_date = { Jan 2018 },
volume = { 179 },
number = { 13 },
month = { Jan },
year = { 2018 },
issn = { 0975-8887 },
pages = { 28-33 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume179/number13/28862-2018916178/ },
doi = { 10.5120/ijca2018916178 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:55:17.301711+05:30
%A Prarthana A. Deshkar
%A Parag S. Deshpande
%A A. Thomas
%T Multidimensional Data Analysis Facilities and Challenges: A Survey for Data Analysis Tools
%J International Journal of Computer Applications
%@ 0975-8887
%V 179
%N 13
%P 28-33
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Data is one of the most important aspects of any commercial or research organization. Different technical processes are applied to extract information from data. This information gets transformed into knowledge for further decision making. Today many data analysis tools are available in the market which helps to convert data into useful information. The primary focus of these tools is to provide beneficial and helpful results to its users as per their requirements. The key to analyze any data is lies in its multidimensional structure. The new wave of technology has changed the form and volume of data. There is also a significant change in the type of users, it has been observed that non- technical data miners as target users of various analytical tools. Hence, in today’s world, it is essential for these analysis tools to adapt themselves to the changing needs of both the users and the technology and be updated with new statistical techniques, data mining algorithms and machine learning methodologies. In this paper, focus is on the advantages and challenges faced by some of the data analysis tools due to the change in form and volume of data.

References
  1. C.L. Philip Chen, Chun-Yang Zhang “Data- intensive applications, challenges, techniques and technologies: A survey on Big Data”, Information Sciences.
  2. 2014BusinessObjects User’s Guide,Copyright © 2004 Business Objects. All rights reserved.
  3. SAP BO Architecture from http://bigclasses.com/blog/sap-bo-architecture
  4. SAP BusinessObjects Business Intelligence Suit, 2015 SAP SE or an SAP affiliate company.
  5. Architecture for Enterprise Business Intelligence, an overview of the microstrategy platform architecture for big data, cloud bi, and mobile applications.
  6. MDX Cube Reporting Guide, Copyright © 2017 by MicroStrategy Incorporated.
  7. IBM Cognos Dynamic Cubes,Copyright International Busines`s Machines Corporation 2012.
  8. IBM Cognos Analytics – Reporting Version 11.0, Copyright IBM Corporation 2005, 2015.
  9. Better planning and forecasting with IBM Predictive Analytics Using IBM Cognos TM1 with IBM SPSS Predictive Analytics to build better plans and forecasts,Copyright IBM Corporation 2014
  10. IBM Cognos StatisticsWizard-driven statistical analysis incorporated into your business reporting powered by IBM SPSS Statistics Engine, Copyright IBM Corporation 2010
  11. IBM SPSS Statistics 22, part 1 Descriptive Statistics summer 2014
  12. Tableau,from https://nihlibrary.nih.gov/resources/tools/tableau
  13. Tableau Server Study Guide
  14. Tableau for the Enterprise: An Overview for IT,Authors: Marc Rueter, Senior Director, Strategic Solutions Ellie Fields, Senior Director, Product Marketing May 2012
  15. SAP Business Objects Business Intelligence platform Document Version: 4.2 SP2 – 2016
  16. Tableau Software Review, 2017 fromhttps://www.betterbuys.com/bi/reviews/tableau-business-intelligence/
  17. Product Overview from https://www.betterbuys.com/bi/reviews/ibm-cognos-business-intelligence/
  18. IBM Cognos Architecture from https://www.ibm.com/support/knowledgecenter/en/SSEP7J_10.2.2/com.ibm.swg.ba.cognos.dg_sdk.10.2.2.doc/i_d15e231405.html
  19. Modeling components from https://www.ibm.com/support/knowledgecenter/en/SSEP7J_11.0.0/com.ibm.swg.ba.cognos.inst_cr_winux.doc/c_inst_modelingcomponents.html
  20. Hints in BO Universe to Improve Report Performance, 2013 from https://dwbicastle.com/2013/12/13/hints-in-bo-universe-to-improve-report-performance/
  21. Using Index Awareness in Business Objects Universe for Performance Optimization, from http://www.bidw.org/business-objects/universe-design/using-index-awareness-in-business-objects-universe-for-performance-optimization/
  22. Tableau for the Enterprise: An Overview for IT, Marc Rueter, Senior Director, Strategic solutions Ellie Fields, Senior Director, Product Marketing , May 2012
  23. Advanced Analytics with Tableau, Ian Coe, Product Manager
  24. Performance analysis from http://www2.microstrategy.com/producthelp/10.4/OperationsManager/Content/Operations_Manager_Guide/Performance_analysis.htm
  25. Managing users and roles from, https://www.ibm.com/support/knowledgecenter/en/SSMR4U_10.1.0/com.ibm.swg.ba.cognos.ig_exprss.10.1.0.doc/t_managingusersandroles.html
Index Terms

Computer Science
Information Sciences

Keywords

Multidimensional Data Analysis Data Analysis Tools Data Mining Statistical Methods Machine Learning.

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