Data Science

2018 ◽  
pp. 141-151
Author(s):  
Sabitha Rajagopal
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Research And Development ◽  
Real Time ◽  
Data Science ◽  
Data Warehousing ◽  
Holistic Approach ◽  
Digital Data ◽  
Data Application ◽  
Development Analysis

Data Science employs techniques and theories to create data products. Data product is merely a data application that acquires its value from the data itself, and creates more data as a result; it's not just an application with data. Data science involves the methodical study of digital data employing techniques of observation, development, analysis, testing and validation. It tackles the real time challenges by adopting a holistic approach. It ‘creates' knowledge about large and dynamic bases, ‘develops' methods to manage data and ‘optimizes' processes to improve its performance. The goal includes vital investigation and innovation in conjunction with functional exploration intended to notify decision-making for individuals, businesses, and governments. This paper discusses the emergence of Data Science and its subsequent developments in the fields of Data Mining and Data Warehousing. The research focuses on need, challenges, impact, ethics and progress of Data Science. Finally the insights of the subsequent phases in research and development of Data Science is provided.

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.

Factor Analysis in Data Mining

2011 ◽  
pp. 498-502 ◽  
Author(s):  
Zu-Hsu Lee ◽  
Richard L. Peterson ◽  
Chen-Fu Chien ◽  
Ruben Xing
Keyword(s):  
Data Mining ◽  
Information Technology ◽  
Factor Analysis ◽  
Rapid Growth ◽  
Data Warehousing ◽  
Analytical Techniques ◽  
Information Resources ◽  
Business People ◽  
New Information

The rapid growth and advances of information technology enable data to be accumulated faster and in much larger quantities (i.e., data warehousing). Faced with vast new information resources, scientists, engineers, and business people need efficient analytical techniques to extract useful information and effectively uncover new, valuable knowledge patterns.

Security issues in data warehousing and data mining: panel discussion

1998 ◽  
pp. 3-16 ◽  
Author(s):  
Bhavani Thuraisingham ◽  
Linda Schlipper ◽  
Pierangela Samarati ◽  
T. Y. Lin ◽  
Sushil Jajodia ◽  
...  
Keyword(s):  
Data Mining ◽  
Data Warehousing ◽  
Panel Discussion ◽  
Security Issues

Data mining solutions and the establishment of a data warehouse: Corporate nirvana for the 21st Century

First Monday ◽  
1997 ◽  
Author(s):  
Christine Maxwell ◽  
Howard Gutowitz
Keyword(s):  
Data Mining ◽  
Data Warehouse ◽  
21St Century ◽  
Complex Adaptive Systems ◽  
Adaptive Systems ◽  
Data Warehousing ◽  
Knowledge Retrieval ◽  
End User ◽  
Information Mining ◽  
Complex Adaptive

Addresses the need to broaden the meaning of data mining and data warehousing to encompass information mining and knowledge retrieval into complex adaptive systems with the business end user in mind.

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