scholarly journals A Benchmark to Select Data Mining Based Classification Algorithms for Business Intelligence and Decision Support Systems

2012 ◽  
Vol 2 (5) ◽  
pp. 25-42 ◽  
Author(s):  
Pardeep Kumar
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Decision Support Systems ◽  
Business Intelligence ◽  
Support Systems ◽  
Classification Algorithms

Business Intelligence and Data Mining

2013 ◽  
pp. 57-70
Author(s):  
Zsolt T. Kardkovács
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Strategic Planning ◽  
Decision Support Systems ◽  
Business Intelligence ◽  
Business Processes ◽  
Support Systems ◽  
Decision Makers ◽  
Key Factors ◽  
Short Introduction

Whenever decision makers find out that they want to know more about how the business works and progresses, or why customers do what they do, then data miners are summoned, and business intelligence is to be built or altered. Data mining aims at retrieving valid, interesting, explicable connection between key factors for either operative reporting or supporting strategic planning. While data mining discovers static connections between factors, business intelligence visualizes relevant data for decision makers in order to make them identify fast changes and analyze precisely business states. In this chapter, the authors give a short introduction for data oriented decision support systems with data mining and business intelligence in it. While these techniques are widely used in business processes, there are much more bad practices than good ones. We try to make an attempt to demystify and clear the myths about these technologies, and determine who should and how (not) to use them.

A Perspective on Data Mining Integration with Business Intelligence

Data Mining ◽  
2013 ◽  
pp. 1873-1892
Author(s):  
Ana Azevedo ◽  
Manuel Filipe Santos
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Literature Review ◽  
Decision Support Systems ◽  
Business Intelligence ◽  
Support Systems ◽  
Underlying Structure ◽  
The Past ◽  
Definition Of

Business Intelligence (BI) is an emergent area of the Decision Support Systems (DSS) discipline. Over the past years, the evolution in this area has been considerable. Similarly, in the last years, there has been a huge growth and consolidation of the Data Mining (DM) field. DM is being used with success in BI systems, but a truly DM integration with BI is lacking. The purpose of this chapter is to discuss the relevance of DM integration with BI, and its importance to business users. From the literature review, it was observed that the definition of an underlying structure for BI is missing, and therefore a framework is presented. It was also observed that some efforts are being done that seek the establishment of standards in the DM field, both by academics and by people in the industry. Supported by those findings, this chapter introduces an architecture that can conduct to an effective usage of DM in BI. This architecture includes a DM language that is iterative and interactive in nature. This chapter suggests that the effective usage of DM in BI can be achieved by making DM models accessible to business users, through the use of the presented DM language.

Extending QMBE Language with Clustering

2013 ◽  
Vol 5 (4) ◽  
pp. 59-77 ◽  
Author(s):  
Ana Azevedo ◽  
Manuel Filipe Santos
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Decision Support Systems ◽  
Business Intelligence ◽  
Support Systems ◽  
Classification Rules ◽  
The Past

Business Intelligence (BI) is an important area of the Decision Support Systems (DSS) discipline. Over the past years, the evolution in this area has been considerable. Similarly, in the last years, there has been a huge growth and consolidation of the Data Mining (DM) field. DM is being used with success in BI systems, but a truly DM integration with BI is lacking. This creates a gap between DM and BI systems. With the purpose of closing this gap a new DM language for BI, named as Query-Models-By-Example (QMBE), was envisaged and implemented with success, but addressing only classification rules. This paper presents an extension of QMBE language to include clustering. This represents one more step towards the integration of DM with BI, which constitutes an important issue.

scholarly journals Web Mining for the Integration of Data Mining with Business Intelligence in Web-Based Decision Support Systems

2015 ◽  
pp. 120-142 ◽  
Author(s):  
Marcos Aurélio Domingues ◽  
Alípio Mário Jorge ◽  
Carlos Soares ◽  
Solange Oliveira Rezende
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Decision Support Systems ◽  
Business Intelligence ◽  
Web Mining ◽  
Support Systems ◽  
Web Documents ◽  
Web Based ◽  
Use Of Data ◽  
Computer Based

Web mining can be defined as the use of data mining techniques to automatically discover and extract information from web documents and services. A decision support system is a computer-based information system that supports business or organizational decision-making activities. Data mining and business intelligence techniques can be integrated in order to develop more advanced decision support systems. In this chapter, the authors propose to use web mining as a process to develop advanced decision support systems in order to support the management activities of a website. They describe the web mining process as a sequence of steps for the development of advanced decision support systems. By following such a sequence, the authors can develop advanced decision support systems, which integrate data mining with business intelligence, for websites.

A Perspective on Data Mining Integration with Business Intelligence

2010 ◽  
pp. 109-129 ◽  
Author(s):  
Ana Azevedo ◽  
Manuel Filipe Santos
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Literature Review ◽  
Decision Support Systems ◽  
Business Intelligence ◽  
Support Systems ◽  
Underlying Structure ◽  
The Past ◽  
Definition Of

Business Intelligence (BI) is an emergent area of the Decision Support Systems (DSS) discipline. Over the past years, the evolution in this area has been considerable. Similarly, in the last years, there has been a huge growth and consolidation of the Data Mining (DM) field. DM is being used with success in BI systems, but a truly DM integration with BI is lacking. The purpose of this chapter is to discuss the relevance of DM integration with BI, and its importance to business users. From the literature review, it was observed that the definition of an underlying structure for BI is missing, and therefore a framework is presented. It was also observed that some efforts are being done that seek the establishment of standards in the DM field, both by academics and by people in the industry. Supported by those findings, this chapter introduces an architecture that can conduct to an effective usage of DM in BI. This architecture includes a DM language that is iterative and interactive in nature. This chapter suggests that the effective usage of DM in BI can be achieved by making DM models accessible to business users, through the use of the presented DM language.

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