Integrating web-based data mining tools with business models for knowledge management

2003 ◽  
Vol 35 (1) ◽  
pp. 103-112 ◽  
Author(s):  
John H. Heinrichs ◽  
Jeen-Su Lim
Keyword(s):  
Data Mining ◽  
Knowledge Management ◽  
Business Models ◽  
Web Based ◽  
Mining Tools

scholarly journals Data Mining as Support to Knowledge Management in Marketing

2015 ◽  
Vol 6 (2) ◽  
pp. 18-30 ◽  
Author(s):  
Marijana Zekić-Sušac ◽  
Adela Has
Keyword(s):  
Data Mining ◽  
Neural Networks ◽  
Knowledge Management ◽  
Association Rules ◽  
Marketing Strategies ◽  
Web Based ◽  
Data Mining Techniques ◽  
Data Mining Approach ◽  
Model Generalization ◽  
Retail Managers

Abstract Background: Previous research has shown success of data mining methods in marketing. However, their integration in a knowledge management system is still not investigated enough. Objectives: The purpose of this paper is to suggest an integration of two data mining techniques: neural networks and association rules in marketing modeling that could serve as an input to knowledge management and produce better marketing decisions. Methods/Approach: Association rules and artificial neural networks are combined in a data mining component to discover patterns and customers’ profiles in frequent item purchases. The results of data mining are used in a web-based knowledge management component to trigger ideas for new marketing strategies. The model is tested by an experimental research. Results: The results show that the suggested model could be efficiently used to recognize patterns in shopping behaviour and generate new marketing strategies. Conclusions: The scientific contribution lies in proposing an integrative data mining approach that could present support to knowledge management. The research could be useful to marketing and retail managers in improving the process of their decision making, as well as to researchers in the area of marketing modelling. Future studies should include more samples and other data mining techniques in order to test the model generalization ability.

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.

Organic Knowledge Management for Web-Based Customer Service

2011 ◽  
pp. 92-108
Author(s):  
Stephen D. Durbin ◽  
Doug Warner ◽  
J. Neal Richter ◽  
Zuzana Gedeon
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Knowledge Management ◽  
Customer Service ◽  
System Architecture ◽  
The Internet ◽  
Web Based ◽  
Information Navigation ◽  
Knowledge Processes ◽  
Meta Information

This chapter introduces practical issues of information navigation and organizational knowledge management involved in delivering customer service via the Internet. An adaptive, organic approach is presented that addresses these issues. This approach relies on both a system architecture that embodies effective knowledge processes, and a knowledge base that is supplemented with meta-information acquired automatically through various data mining and artificial intelligence techniques. An application implementing this approach, RightNow eService Center, and the algorithms supporting it are described. Case studies of the use of eService Center by commercial, governmental and other types of organizations are presented and discussed. It is suggested that the organic approach is effective in a variety of information-providing settings beyond conventional customer service.

Organic Knowledge Management for Web-Based Customer Service

2011 ◽  
pp. 1971-1986
Author(s):  
Stephen D. Durbin ◽  
Doug Warner ◽  
J. Neal Richter ◽  
Zuzana Gedeon
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Knowledge Management ◽  
Customer Service ◽  
System Architecture ◽  
The Internet ◽  
Web Based ◽  
Information Navigation ◽  
Knowledge Processes ◽  
Meta Information

This chapter introduces practical issues of information navigation and organizational knowledge management involved in delivering customer service via the Internet. An adaptive, organic approach is presented that addresses these issues. This approach relies on both a system architecture that embodies effective knowledge processes, and a knowledge base that is supplemented with meta-information acquired automatically through various data mining and artificial intelligence techniques. An application implementing this approach, RightNow eService Center, and the algorithms supporting it are described. Case studies of the use of eService Center by commercial, governmental and other types of organizations are presented and discussed. It is suggested that the organic approach is effective in a variety of information-providing settings beyond conventional customer service.

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.

scholarly journals GeneCite and PathwayScreen: web based high throughput bioinformatics search and data mining tools

2006 ◽  
Vol 20 (4) ◽  
Author(s):  
Rasha Hammamieh ◽  
Nabarun Chakraborty ◽  
Yan Wang ◽  
John Mulligan ◽  
Marti Jett
Keyword(s):  
Data Mining ◽  
High Throughput ◽  
Web Based ◽  
Mining Tools
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