Multifaceted Applications of Data Mining, Business Intelligence, and Knowledge Management

2018 ◽  
pp. 810-825 ◽  
Author(s):  
Kijpokin Kasemsap
Keyword(s):  
Data Mining ◽  
Knowledge Management ◽  
Literature Review ◽  
Human Resources ◽  
Business Intelligence ◽  
Core Competencies ◽  
Huge Amount ◽  
Practical Applications ◽  
Business Information ◽  
Business Opportunities

This article reviews the literature in the search for the multifaceted applications of data mining (DM), business intelligence (BI), and knowledge management (KM). The literature review highlights the overviews of DM, BI, and KM; the practical applications of DM, BI, and KM; and the prospects of DM, BI, and KM in terms of marketing, business, human resources, and manufacturing. DM plays a key role in organizing huge amount of data and condensing it into valuable information. BI involves the delivery and integration of relevant and useful business information in an organization. KM allows companies to manage a system of core competencies in order to maximize business opportunities and minimize the risk of losing business opportunities. The findings present valuable insights and further understanding of the way in which DM, BI, and KM efforts should be focused.

Multifaceted Applications of Data Mining, Business Intelligence, and Knowledge Management

2016 ◽  
Vol 5 (1) ◽  
pp. 57-69 ◽  
Author(s):  
Kijpokin Kasemsap
Keyword(s):  
Data Mining ◽  
Knowledge Management ◽  
Literature Review ◽  
Human Resources ◽  
Business Intelligence ◽  
Core Competencies ◽  
Huge Amount ◽  
Practical Applications ◽  
Business Information ◽  
Business Opportunities

This article reviews the literature in the search for the multifaceted applications of data mining (DM), business intelligence (BI), and knowledge management (KM). The literature review highlights the overviews of DM, BI, and KM; the practical applications of DM, BI, and KM; and the prospects of DM, BI, and KM in terms of marketing, business, human resources, and manufacturing. DM plays a key role in organizing huge amount of data and condensing it into valuable information. BI involves the delivery and integration of relevant and useful business information in an organization. KM allows companies to manage a system of core competencies in order to maximize business opportunities and minimize the risk of losing business opportunities. The findings present valuable insights and further understanding of the way in which DM, BI, and KM efforts should be focused.

The Role of Data Mining for Business Intelligence in Knowledge Management

2015 ◽  
pp. 12-33 ◽  
Author(s):  
Kijpokin Kasemsap
Keyword(s):  
Data Mining ◽  
Knowledge Management ◽  
Human Resources ◽  
Organizational Performance ◽  
Business Intelligence ◽  
Statistical Significance ◽  
Practical Applications ◽  
Business Goals ◽  
Emerging Trends

This chapter introduces the role of Data Mining (DM) for Business Intelligence (BI) in Knowledge Management (KM), thus explaining the concept of KM, BI, and DM; the relationships among KM, BI, and DM; the practical applications of KM, BI, and DM; and the emerging trends toward practical results in KM, BI, and DM. In order to solve existing BI problems, this chapter also describes practical applications of KM, BI, and DM (in the fields of marketing, business, manufacturing, and human resources) and the emerging trends in KM, BI, and DM (in terms of larger databases, high dimensionality, over-fitting, evaluation of statistical significance, change of data and knowledge, missing data, relationships among DM fields, understandability of patterns, integration of other DM systems, and users' knowledge and interaction). Applying DM for BI in the KM environments will enhance organizational performance and achieve business goals in the digital age.

scholarly journals Retention and Task Shifting in Human Resources for Health through Data Mining

2017 ◽  
Vol 10 (5) ◽  
pp. 29
Author(s):  
Cheng-Kun Wang
Keyword(s):  
Data Mining ◽  
Literature Review ◽  
Human Resources ◽  
Rural Areas ◽  
Health Workers ◽  
Human Resources For Health ◽  
Task Shifting ◽  
Good Tool ◽  
Medical Manpower ◽  
And Task

Human resources for health (HRH) are the backbone of the healthcare system, but a shortage of medical manpower and the misdistribution of human resources are critical problems in the rural areas of many countries till 2017. The shortage of medical manpower is a big issue between 2004 and 2013. Data mining of bibliometrics is a good tool to find the solutions for shortage of medical manpower. By analyzing 118,092 citations in 2,000 articles published in the SSCI and SCI databases addressing HRH from 2004 to 2013, we plotted the networks among authors in the field. We combine quantitative bibliometrics and a qualitative literature review to determine the important articles and to realize the relationships between important topics in this field. We find that retention and task shifting are the hot topics in HRH field between 2004 and 2013, and find out the solutions for these issues through literature review in later papers. The solution to the HRH shortage is to determine the motivations of health workers and to provide incentives to maintain their retention. Task shifting is another solution to the HRH crisis.

scholarly journals Elements of the Persistence in Innovation: Systematic Literature Review

2020 ◽  
Vol 10 (4) ◽  
pp. 83
Author(s):  
Juan Jesus Arenas ◽  
Juan Erasmo Gómez ◽  
Efraín Ortiz ◽  
Freddy Paz ◽  
Carlos Parra
Keyword(s):  
Knowledge Management ◽  
Literature Review ◽  
Human Resources ◽  
Competitive Advantage ◽  
Systematic Literature Review ◽  
Continuous Production

The persistence of innovation is a topic that has been used in recent years. Companies must be in continuous production of innovations to achieve a competitive advantage in the market and for this, it is necessary to have elements that positively influence the persistence of innovating. The objective of the article is to describe the elements that positively influence the persistence of innovation through a systematic literature review in the range of the last 10 years (2010–2019). As a result, 34 articles were obtained and it was identified that investment in R & D, human resources and knowledge management positively influences the persistence of innovation.

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.

scholarly journals AN EMPIRICAL ANALYSIS ON DATA MINING SYSTEM AND ITS TECHNIQUES .

2020 ◽  
pp. 30-36
Keyword(s):  
Data Mining ◽  
Business Intelligence ◽  
The Other ◽  
Huge Amount ◽  
Mining System ◽  
New Horizons ◽  
Analytical Processing ◽  
Data Mining System ◽  
Information Harvesting ◽  
Data Dredging

Data mining is an extraction of knowledge discovery from huge amount of data which is previously unknown and potentially useful for analytical processing and decision making. The other acronyms of data mining are such as Data archeology, Data dredging, Information harvesting and Business Intelligence. The various data mining techniques are used to find the hidden interestingness or new patter to store the data. These techniques and approaches of data mining can efficiently build the new environment for analyzing and predictions. This paper highlights data mining process and its various techniques to find the interestingness. Finally, concluded with its limitations. The objective of the paper is opens new horizons for researchers of forthcoming generations.

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.

Business Intelligence

2012 ◽  
pp. 1-13 ◽  
Author(s):  
Mouhib Alnoukari ◽  
Humam Alhammami Alhawasli ◽  
Hatem Abd Alnafea ◽  
Amjad Jalal Zamreek
Keyword(s):  
Data Mining ◽  
Knowledge Management ◽  
Business Intelligence ◽  
Data Warehousing ◽  
Historical Overview ◽  
Data Marts ◽  
Main Components

This chapter attempts to define the knowledge body of Business Intelligence. It provides an overview of the context we have been working in. The chapter starts with a historical overview of Business Intelligence stating its different stages and progressions. Then, the authors present an overview of what Business Intelligence is, its architecture and goals, and its main components including: data mining, data warehousing, and data marts. Finally, the Business Intelligence ‘marriage’ with knowledge management is discussed in details. The authors hope to contribute to the recent discussions about Business Intelligence goals, concepts, architecture, and components.

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