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Centralized or Federated Data Management Models, IT Professionals’ Preferences

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Collection

2014 ASEE Annual Conference & Exposition

Location

Indianapolis, Indiana

Publication Date

June 15, 2014

Start Date

June 15, 2014

End Date

June 18, 2014

ISSN

2153-5965

Conference Session

Topics in Computing and Information Technologies

Tagged Division

Computing & Information Technology

Page Count

13

Page Numbers

24.266.1 - 24.266.13

Permanent URL

https://peer.asee.org/20157

Download Count

29

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Paper Authors

biography

Gholam Ali Shaykhian NASA

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Ali Shaykhian has received a Master of Science (M.S.) degree in Computer Systems from University of Central Florida and a second M.S. degree in Operations Research from the same university and has earned a Ph.D. in Operations Research from Florida Institute of Technology. His research interests include knowledge management, data mining, object-oriented methodologies, design patterns, software safety, genetic and optimization algorithms and data mining. Dr. Shaykhian is a professional member of the American Society for Engineering Education (ASEE).

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biography

Mohd A. Khairi Najran University

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B.Sc of computer science from Khartoum university. Earned my masters from ottawa university in system science. My doctoral degree in information system from University of Phoenix( my dissertation was in Master Data Management). I worked in IT industry over 20 years, 10 of them with Microsoft in different groups and for the last 4 years was with business intelligence group. My focus was in Master Data Management. For the last 2 years I teach at universify of Najran in the college of Computer Science and Information System . My research interest in Master Data Management.

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Abstract

Data Mining Algorithms: Improving Data Analysis and Knowledge DiscoveryData mining uses pattern based queries, searches, or other analyses of one or more electronicdatabases in order to discover or locate a predictive pattern or anomalies. As such, it can be usedon representative data sets to monitor for subjects such as terrorist activity, criminal activity, orsystem failure.In recent years, throughout industry and government agencies, thousands data systems aredesigned and tailored to serve specific engineering and business needs. Many of these systemsuse relational algebra with structured query language to categorize and retrieve data. In thesesystems, data analyses are limited and require prior explicit knowledge of metadata and databaserelations; lacking exploratory data mining and discoveries. Engineering and scientific dataanalyses can be improved tremendously with the use of data mining techniques, methods andalgorithms.There are numerous algorithms, techniques and methods used to mine data; including neuralnetworks, genetic algorithms, decision trees, neatest neighbor method, rule induction associationanalysis, slice and dice, segmentation, and clustering. Each approach uniquely detects patterns ina dataset to improve knowledge discovery that can best discover the latent information in largequantities of data stored and strengthen data/text mining and trending within datasets.No one technique solves all data mining problems. This paper will discuss different data miningalgorithms and analyses of electronic data stored in one or more databases, document files, emailfiles, or web pages used to discover or locate predictive patterns or for discovery of knowledge.

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