Data mining technologies and decision support systems for business and scientific applications (original) (raw)
Related papers
Encyclopedia of Data Warehousing and Mining, Idea Group Inc., June 2005
Information by itself is no longer perceived as an asset. Billions of business transactions are recorded in enterprise scale data warehouses every day. Acquisition, storage and management of business information are commonplace and often automated. Recent advances in (remote or other) sensor technologies have led to the development of scientific data repositories.
Cross Industry Survey on Data mining Applications
International Journal of …, 2011
Data mining, the extraction of hidden predictive information from large databases, is a powerful new technology with great potential to help companies focus on the most important information in their data warehouses. Data mining tools predict future trends and behaviors, allowing businesses to make proactive, knowledge-driven decisions. The automated, prospective analysis offered by data mining move beyond the analysis of past events provided by retrospective tools typical of decision support systems. Data mining tools can answer business questions that traditionally were too time consuming to resolve. They scour databases for hidden patterns, finding predictive information that experts may miss because it lies outside their expectations. Most companies already collect and refine massive quantities of data. Data mining techniques can be implemented rapidly on existing software and hardware platforms to enhance the value of existing information resources, and can be integrated with new products and systems as they are brought on-line. When implemented on high performance client/server or parallel processing computers, data mining tools can analyze massive databases to deliver answers to are most likely to respond to my next promotional mailing. This paper explores on survey of the current basic technologies of data mining. Examples of profitable applications illustrate its relevance to today’s business environment as well as a basic description of how data warehouse architectures can evolve to deliver the value of data mining to end users.
Data Mining and Decision Support
2003
Data mining and decision support: integration and collaboration / edited by Dunja Mladenic ... [et al.]. p. cm.-(The Kluwer international series in engineering and computer science ; SECS 745) Includes bibliographical references and index.
Scientific Data Mining and Knowledge Discovery
2010
The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use.
Data mining is a combination of database and artificial intelligence technologies. Although the AI field has taken a major dive in the last decade; this new emerging field has shown that AI can add major contributions to existing fields in computer science. In fact, many experts believe that data mining is the third hottest field in the industry behind the Internet, and data warehousing.
Human brain is the most powerful tool in the world, but when it comes to the ability for analyzing a plethora of complex data sets within a given period, even the human brain needs a bit of assistance. This is when data mining comes into the picture. Initially, data mining started to spread its impact in the field of business related activities, financial applications and market analysis. Gradually, it expanded its ambit, and today, it has virtually become one of the most essential spheres with its multifaceted tools and technologies. Earlier unheard of fields (with respect to data mining), like genomics, molecular design, hydrology, forensic science, etc. have leaped forwarded successfully with the assistance of data mining techniques. This paper discusses the early days of data mining, the current scenario (taking into account the categories and applications of data mining), the opportunities, challenges and future prospects.
Deployable suite of data mining web services for online science data repositories
23rd Conference on IIPS, 2007
1 Abstract A project is currently underway to create a suite of specialized deployable data mining web services designed specifically for science data. The project leverages the Algorithm Development and Mining (ADaM) toolkit as the basis. The ADaM toolkit is a robust, mature and freely available science data mining toolkit that is being used by different research organizations and educational institutions worldwide. These deployable services will give the scientific community a powerful and versatile data mining capability that can ...