Strategic Utilization of Data Mining

2005 ◽  
pp. 2638-2642
Author(s):  
Chandra S. Amaravadi
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Decision Support ◽  
Information Systems ◽  
Relational Databases ◽  
Online Analytical Processing ◽  
Artificial Intelligence Techniques ◽  
The Past ◽  
Analytical Processing

In the past decade, a new and exciting technology has unfolded on the shores of the information systems area. Based on a combination of statistical and artificial intelligence techniques, data mining has emerged from relational databases and Online Analytical Processing as a powerful tool for organizational decision support (Shim et al., 2002).

Strategic Utilization of Data Mining

2008 ◽  
pp. 1689-1695
Author(s):  
Chandra S. Amaravadi
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Decision Support ◽  
Information Systems ◽  
Relational Databases ◽  
Online Analytical Processing ◽  
Artificial Intelligence Techniques ◽  
The Past ◽  
Analytical Processing

In the past decade, a new and exciting technology has unfolded on the shores of the information systems area. Based on a combination of statistical and artificial intelligence techniques, data mining has emerged from relational databases and Online Analytical Processing as a powerful tool for organizational decision support (Shim et al., 2002).

An Intelligent Support System Integrating Data Mining and Online Analytical Processing

2011 ◽  
pp. 141-156
Author(s):  
Rahul Singh ◽  
Richard T. Redmond ◽  
Victoria Yoon
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Decision Support ◽  
Decision Makers ◽  
Online Analytical Processing ◽  
Intelligent Decision Making ◽  
Course Of Action ◽  
Intelligent Decision ◽  
Analytical Processing ◽  
Intelligent Decision Support

Intelligent decision support requires flexible, knowledge-driven analysis of data to solve complex decision problems faced by contemporary decision makers. Recently, online analytical processing (OLAP) and data mining have received much attention from researchers and practitioner alike, as components of an intelligent decision support environment. Little that has been done in developing models to integrate the capabilities of data mining and online analytical processing to provide a systematic model for intelligent decision making that allows users to examine multiple views of the data that are generated using knowledge about the environment and the decision problem domain. This paper presents an integrated model in which data mining and online analytical processing complement each other to support intelligent decision making for data rich environments. The integrated approach models system behaviors that are of interest to decision makers; predicts the occurrence of such behaviors; provides support to explain the occurrence of such behaviors and supports decision making to identify a course of action to manage these behaviors.

An Intelligent Support System Integrating Data Mining and Online Analytical Processing

2008 ◽  
pp. 2964-2977
Author(s):  
Rahul Singh ◽  
Richard T. Redmond ◽  
Victoria Yoon
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Decision Support ◽  
Decision Makers ◽  
Online Analytical Processing ◽  
Intelligent Decision Making ◽  
Course Of Action ◽  
Intelligent Decision ◽  
Analytical Processing ◽  
Intelligent Decision Support

Intelligent decision support requires flexible, knowledge-driven analysis of data to solve complex decision problems faced by contemporary decision makers. Recently, online analytical processing (OLAP) and data mining have received much attention from researchers and practitioner alike, as components of an intelligent decision support environment. Little that has been done in developing models to integrate the capabilities of data mining and online analytical processing to provide a systematic model for intelligent decision making that allows users to examine multiple views of the data that are generated using knowledge about the environment and the decision problem domain. This paper presents an integrated model in which data mining and online analytical processing complement each other to support intelligent decision making for data rich environments. The integrated approach models system behaviors that are of interest to decision makers; predicts the occurrence of such behaviors; provides support to explain the occurrence of such behaviors and supports decision making to identify a course of action to manage these behaviors.

Data Mining in Franchise Organizations

2008 ◽  
pp. 2722-2733 ◽  
Author(s):  
Ye-Sho Chen ◽  
Robert Justis ◽  
P. Pete Chong
Keyword(s):  
Data Mining ◽  
Growth Strategy ◽  
Online Analytical Processing ◽  
Pertinent Data ◽  
Analytical Processing ◽  
Customer Services ◽  
Internal Operations ◽  
Comprehensive Framework ◽  
Franchise Organizations

Franchising has been used by businesses as a growth strategy. Based on the authors’ cumulative research and experience in the industry, this paper describes a comprehensive framework that describes both the franchise environment — from customer services to internal operations — and the pertinent data items in the system. The authors identify the most important aspects of a franchising business, the role of online analytical processing (OLAP) and data mining play and the data items that data mining should focus on to ensure its success.

Online Data Mining

2008 ◽  
pp. 75-83
Author(s):  
He´ctor Oscar Nigro ◽  
Sandra Elizabeth González Císaro
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Online Analytical Processing ◽  
Intelligent Data Analysis ◽  
Online Data ◽  
Analytical Processing

Several approaches for intelligent data analysis are not only available but also tried and tested. Online analytical processing (OLAP) and data mining represent two of the most important approaches. They mainly emphasize different aspects of the data and allow deriving of different kinds of information. So far, these approaches have mainly been used in isolation (Schwarz, 2002).

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