A Conceptual Framework for Data Mining and Knowledge Management

2011 ◽  
pp. 1-15 ◽  
Author(s):  
Shamsul I. Chowdhury
Keyword(s):  
Data Mining ◽  
Knowledge Management ◽  
Knowledge Base ◽  
Business Processes ◽  
Data Warehousing ◽  
Case Based Reasoning ◽  
Integrated Framework ◽  
Large Databases ◽  
Main Components ◽  
Mining Tools

Over the last decade data warehousing and data mining tools have evolved from research into a unique and popular applications, ranging from data warehousing and data mining for decision support to business intelligence and other kind of applications. The chapter presents and discusses data warehousing methodologies along with the main components of data mining tools and technologies and how they all could be integrated together for knowledge management in a broader sense. Knowledge management refers to the set of processes developed in an organization to create, extract, transfer, store and apply knowledge. The chapter also focuses on how data mining tools and technologies could be used in extracting knowledge from large databases or data warehouses. Knowledge management increases the ability of an organization to learn from its environment and to incorporate knowledge into the business processes by adapting to new tools and technologies. Knowledge management is also about the reusability of the knowledge that is being extracted and stored in the knowledge base. One way to improve the reusability is to use this knowledge base as front-ends to case-based reasoning (CBR) applications. The chapter further focuses on the reusability issues of knowledge management and presents an integrated framework for knowledge management by combining data mining (DM) tools and technologies with CBR methodologies. The purpose of the integrated framework is to discover, validate, retain, reuse and share knowledge in an organization with its internal users as well as its external users. The framework is independent of application domain and would be suitable for uses in areas, such as data mining and knowledge management in e-government.

A Conceptual Framework for Data Mining and Knowledge Management

2010 ◽  
pp. 418-432
Author(s):  
Shamsul I. Chowdhury
Keyword(s):  
Data Mining ◽  
Knowledge Management ◽  
Knowledge Base ◽  
Business Processes ◽  
Data Warehousing ◽  
Case Based Reasoning ◽  
Integrated Framework ◽  
Large Databases ◽  
Main Components ◽  
Mining Tools

Over the last decade data warehousing and data mining tools have evolved from research into a unique and popular applications, ranging from data warehousing and data mining for decision support to business intelligence and other kind of applications. The chapter presents and discusses data warehousing methodologies along with the main components of data mining tools and technologies and how they all could be integrated together for knowledge management in a broader sense. Knowledge management refers to the set of processes developed in an organization to create, extract, transfer, store and apply knowledge. The chapter also focuses on how data mining tools and technologies could be used in extracting knowledge from large databases or data warehouses. Knowledge management increases the ability of an organization to learn from its environment and to incorporate knowledge into the business processes by adapting to new tools and technologies. Knowledge management is also about the reusability of the knowledge that is being extracted and stored in the knowledge base. One way to improve the reusability is to use this knowledge base as front-ends to case-based reasoning (CBR) applications. The chapter further focuses on the reusability issues of knowledge management and presents an integrated framework for knowledge management by combining data mining (DM) tools and technologies with CBR methodologies. The purpose of the integrated framework is to discover, validate, retain, reuse and share knowledge in an organization with its internal users as well as its external users. The framework is independent of application domain and would be suitable for uses in areas, such as data mining and knowledge management in e-government.

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.

Data Mining Fundamental Concepts and Critical Issues

2011 ◽  
pp. 418-423
Author(s):  
John Wang ◽  
Qiyang Chen ◽  
James Yao
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Classification Model ◽  
Data Warehouses ◽  
Business Decisions ◽  
Large Databases ◽  
Critical Issues ◽  
Mining Tools

Data mining is the process of extracting previously unknown information from large databases or data warehouses and using it to make crucial business decisions. Data mining tools find patterns in the data and infer rules from them. The extracted information can be used to form a prediction or classification model, identify relations between database records, or provide a summary of the databases being mined. Those patterns and rules can be used to guide decision making and forecast the effect of those decisions, and data mining can speed analysis by focusing attention on the most important variables.

Application of Dynamic Data Mining and Case-Based Reasoning in the Drilling Fault Diagnosis

2012 ◽  
Vol 524-527 ◽  
pp. 1350-1354
Author(s):  
Qi Li ◽  
Peng Zhai ◽  
Yun Li Zhao
Keyword(s):  
Data Mining ◽  
Fault Diagnosis ◽  
Knowledge Base ◽  
Case Based Reasoning ◽  
Dynamic Data ◽  
Mining Technology ◽  
Decision Systems ◽  
Static Data ◽  
Fault Diagnosis System ◽  
Case Based

Most of the traditional drilling fault diagnosis & decision systems use static data mining technology, so the update of knowledge base becomes its bottlenecks in its development. In order to meet the actual needs, this paper puts forward the method, which combines dynamic data mining technology with case-based reasoning technology, to design drilling fault diagnosis & decision systems. First, design drilling fault diagnosis system overall, then describe the realization of how to realize dynamic data mining and case-based reasoning in detail, finally, introduce some question about the update of knowledge base.

Knowledge Creation

2011 ◽  
pp. 527-538 ◽  
Author(s):  
Nilmini Wickramasinghe
Keyword(s):  
Data Mining ◽  
Knowledge Management ◽  
Organizational Performance ◽  
Knowledge Creation ◽  
Business Processes ◽  
Business Environment ◽  
Sustainable Competitive Advantage ◽  
Continuous Innovation ◽  
Knowledge Spiral ◽  
Business Challenges

Knowledge management (KM) is a newly emerging approach aimed at addressing today’s business challenges to increase efficiency and efficacy of core business processes, while simultaneously incorporating continuous innovation. The need for knowledge management is based on a paradigm shift in the business environment where knowledge is now considered to be central to organizational performance and integral to the attainment of a sustainable competitive advantage (Davenport & Grover, 2001; Drucker, 1993). Knowledge creation is not only a key first step in most knowledge management initiatives, but also has far reaching implications on consequent steps in the KM process, thus making knowledge creation an important focus area within knowledge management. Currently, different theories exist for explaining knowledge creation. These tend to approach the area of knowledge creation from either a people perspective—including Nonaka’s Knowledge Spiral, as well as Spender’s and Blackler’s respective frameworks—or from a technology perspective—namely, the KDD process and data mining.

Data Mining for Supply Chain Management in Complex Networks

2001 ◽  
pp. 173-193 ◽  
Author(s):  
Manoj K. Singh ◽  
Mahesh S. Raisinghani
Keyword(s):  
Data Mining ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Complex Networks ◽  
Business Processes ◽  
Operating Efficiency ◽  
Chain Management ◽  
Making Sense ◽  
Large Databases ◽  
Very Large Databases

The concept and philosophy behind supply chain management is to integrate and optimize business processes across all partners in the entire production chain. Since these are not simple supply chains but rather complex networks, tuning these complex networks comprising supply chain/s to the needs of the market can be facilitated by data mining. Data mining is a set of techniques used to uncover previously obscure or unknown patterns and relationships in very large databases. It provides better information for achieving competitive advantage, increases operating efficiency, reduces operating costs and provides flexibility in using the data by allowing the users to pull the data they need instead of letting the system push the data. However, making sense of all this data is an enormous technological and logistical challenge. This chapter helps you understand the key concepts of data mining, its methodology and application in the context of supply chain management of complex networks.

Rapid Privacy Preserving Algorithm for Large Databases

2009 ◽  
pp. 1050-1061
Author(s):  
K. Anbumani ◽  
R. Nedunchezhian
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Time Complexity ◽  
Strategic Decision ◽  
Strategic Decision Making ◽  
Sensitive Information ◽  
Data Mining Techniques ◽  
Large Databases ◽  
Novel Method ◽  
Mining Tools

Data mining techniques have been widely used for extracting non-trivial information from massive amounts of data. They help in strategic decision- making as well as many more applications. However, data mining also has a few demerits apart from its usefulness. Sensitive information contained in the database may be brought out by the data mining tools. Different approaches are being utilized to hide the sensitive information. The proposed work in this article applies a novel method to access the generating transactions with minimum effort from the transactional database. It helps in reducing the time complexity of any hiding algorithm. The theoretical and empirical analysis of the algorithm shows that hiding of data using this proposed work performs association rule hiding quicker than other algorithms.

Data Mining

2011 ◽  
pp. 167-172
Author(s):  
Richard Peterson
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Classification Model ◽  
Data Warehouses ◽  
Business Decisions ◽  
Large Databases ◽  
Mining Tools

Data mining is the process of extracting previously unknown information from large databases or data warehouses and using it to make crucial business decisions. Data mining tools find patterns in the data and infer rules from them. The extracted information can be used to form a prediction or classification model, identify relations between database records, or provide a summary of the databases being mined. Those patterns and rules can be used to guide decision making and forecast the effect of those decisions, and data mining can speed analysis by focusing attention on the most important variables.

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