The usage of Knowledge Management and Data Mining Techniques to Improve Business Processes

2019 ◽  
Vol 11 (1) ◽  
pp. 43
Author(s):  
Zeeshan Khan
Keyword(s):  
Data Mining ◽  
Knowledge Management ◽  
Business Processes ◽  
Data Mining Techniques

scholarly journals Using Data Mining Techniques to Develop Knowledge Management in Organizations: A Review.

2017 ◽  
Vol 7 (2) ◽  
pp. 80-89
Author(s):  
Bagher Dastyar ◽  
◽  
Hanieh Kazemnejad ◽  
Alireza Asgari Sereshgi ◽  
Mohammad Amin Jabalameli ◽  
...  
Keyword(s):  
Data Mining ◽  
Knowledge Management ◽  
Data Mining Techniques ◽  
Using Data

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.

The Application of Data-Mining to Recommender Systems

2011 ◽  
pp. 45-50 ◽  
Author(s):  
J. Ben Schafer
Keyword(s):  
Data Mining ◽  
Knowledge Management ◽  
Electronic Commerce ◽  
Recommender Systems ◽  
Digital Libraries ◽  
Buying Behavior ◽  
Data Mining Techniques ◽  
The Past ◽  
A Site ◽  
Product Description

In a world where the number of choices can be overwhelming, recommender systems help users find and evaluate items of interest. They connect users with items to “consume” (purchase, view, listen to, etc.) by associating the content of recommended items or the opinions of other individuals with the consuming user’s actions or opinions. Such systems have become powerful tools in domains from electronic commerce to digital libraries and knowledge management. For example, a consumer of just about any major online retailer who expresses an interest in an item – either through viewing a product description or by placing the item in his “shopping cart” – will likely receive recommendations for additional products. These products can be recommended based on the top overall sellers on a site, on the demographics of the consumer, or on an analysis of the past buying behavior of the consumer as a prediction for future buying behavior. This paper will address the technology used to generate recommendations, focusing on the application of data mining techniques.

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.

Path Mining in Web Processes Using Profiles

2011 ◽  
pp. 896-901 ◽  
Author(s):  
Jorge Cardoso
Keyword(s):  
Data Mining ◽  
Quality Of Service ◽  
Business Process ◽  
Management System ◽  
Business Process Management ◽  
Process Management ◽  
Business Processes ◽  
Data Mining Techniques ◽  
Using Data

Business process management systems (BPMSs) (Smith & Fingar, 2003) provide a fundamental infrastructure to define and manage business processes, Web processes, and workflows. When Web processes and workflows are installed and executed, the management system generates data describing the activities being carried out and is stored in a log. This log of data can be used to discover and extract knowledge about the execution of processes. One piece of important and useful information that can be discovered is related to the prediction of the path that will be followed during the execution of a process. I call this type of discovery path mining. Path mining is vital to algorithms that estimate the quality of service of a process, because they require the prediction of paths. In this work, I present and describe how process path mining can be achieved by using data-mining techniques.

Application of Data Mining to Recommender Systems

2011 ◽  
pp. 44-48 ◽  
Author(s):  
J. Ben Schafer
Keyword(s):  
Data Mining ◽  
Knowledge Management ◽  
Electronic Commerce ◽  
Recommender Systems ◽  
Digital Libraries ◽  
Buying Behavior ◽  
Data Mining Techniques ◽  
The Past ◽  
A Site ◽  
Product Description

In a world where the number of choices can be overwhelming, recommender systems help users find and evaluate items of interest. They connect users with items to “consume” (purchase, view, listen to, etc.) by associating the content of recommended items or the opinions of other individuals with the consuming user’s actions or opinions. Such systems have become powerful tools in domains from electronic commerce to digital libraries and knowledge management. For example, a consumer of just about any major online retailer who expresses an interest in an item – either through viewing a product description or by placing the item in his “shopping cart” – will likely receive recommendations for additional products. These products can be recommended based on the top overall sellers on a site, on the demographics of the consumer, or on an analysis of the past buying behavior of the consumer as a prediction for future buying behavior. This paper will address the technology used to generate recommendations, focusing on the application of data mining techniques.

Prototype System of Knowledge Management Based on Data Mining

2013 ◽  
Vol 411-414 ◽  
pp. 251-254
Author(s):  
Yu Dong Guo
Keyword(s):  
Data Mining ◽  
Economic Development ◽  
Knowledge Management ◽  
Knowledge Discovery ◽  
Prototype System ◽  
Or Education ◽  
Data Mining Techniques ◽  
The Core ◽  
Core Competences ◽  
Important Measure

Knowledge is a very crucial resource to promote economic development and society progress which includes facts, information, descriptions, or skills acquired through experience or education. With knowledge has being increasingly prominent, knowledge management has become important measure for the core competences promotion of a corporation. The paper begins with knowledge managements definition, and studies the process of knowledge discovery from databases (KDD),data mining techniques and SECI(Socialization, Externalization, Combination, Internalization) model of knowledge dimensions. Finally, a simple knowledge management prototype system was proposed which based on the KDD and data mining.

scholarly journals Knowledge Creation

2011 ◽  
pp. 326-335 ◽  
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.

scholarly journals An Architectural Framework for Generating Food Safety Key Performance Indicators

2020 ◽  
Vol 55 (5) ◽  
Author(s):  
Fatma Abogabal ◽  
Shimaa M. Ouf ◽  
Amira M. Idrees ◽  
Ayman E. Khedr
Keyword(s):  
Data Mining ◽  
Information Technology ◽  
Food Safety ◽  
Performance Indicators ◽  
Food Industry ◽  
Business Processes ◽  
Food Contamination ◽  
Data Mining Techniques ◽  
Threshold Conditions ◽  
Architectural Framework

Information Technology proved its effectiveness in all industry fields, taking the competition to unexpectedly high levels. Identifying the essential parameters is vital to success. In different fields, business processes monitoring is also essential. In the food industry, for example, food hazards may occur in any stage of generating food, from agriculture to serving. This research uses data mining techniques to propose an architectural framework that can be utilized as a guide for food contamination prevention. The proposed framework aims at detecting the current food status, determining the suitability of the current conditions compared with the required conditions, and alerting users of near-threshold conditions. The framework predicts the available parameters for maintaining the food’s acceptability and includes a plan to follow. The research provides a prototype with a benchmark dataset for proving the applicability of the proposed framework.

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