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Factors Influencing Data Management Models Selection

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Conference

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

12

Page Numbers

24.593.1 - 24.593.12

Permanent URL

https://peer.asee.org/20484

Download Count

40

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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 Abdelgadir 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 doctorate degree in information system from University of Phoenix( my dissertation was in MDM). I worked in IT industry over 20 years, 10 of them with Microsoft in different groups and for the last 4 years (while at Microsoft) was with business intelligence group. My focus was in Master Data Management. For the last 2 years I teach at universify of Najran in computer and information system college.

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Abstract

Improve Data Mining and Knowledge Discovery: Data Analysis Tools and Algorithms Abstract Data mining is widely used to mine business, engineering, and scientific data. Datamining uses pattern based queries, searches, or other analyses of one or more electronicdatabases/datasets in order to discover or locate a predictive pattern or anomaly indicative ofsystem failure, criminal or terrorist activity. There are various algorithms, techniques andmethods used to mine data; including neural networks, genetic algorithms, decision trees, neatestneighbor method, rule induction association analysis, slice and dice, segmentation, andclustering. These algorithms, techniques and methods each uniquely detects patterns in a dataset,have been instrumentals in the development of numerous open source and commerciallyavailable products and technology for data mining. Data mining is best realized when latent information in a large quantity of data stored isdiscovered. No one technique solves all data mining problems; challenges are to selectalgorithms or methods appropriate to strengthen data/text mining and trending within a givendatasets. In recent years, throughout industry, academia and government agencies, thousands ofdata systems are designed and tailored to serve specific engineering and business needs. Many ofthese systems use databases with relational algebra and structured query language to categorizeand retrieve data. In these systems, data analyses are limited and require prior explicit knowledgeof metadata and database relations; lacking exploratory data mining and discoveries of latentinformation. This presentation introduces MatLab® (MATrix LABoratory), an engineering andscientific data analyses tool to perform data mining. MatLab was originally intended to performpurely numerical calculations (a glorified calculator). Now, in addition to having hundreds ofmathematical functions, it is a programming language with hundreds built in standard functionsand numerous available toolboxes. MatLab’s ease of data processing, visualization and itsenormous availability of built in functionalities and toolboxes make it suitable to performnumerical computations and simulations as well as a data mining tool. Engineers and scientistscan take advantage of the readily available functions/toolboxes to gain wider insight in theirperspective data mining experiments.

Shaykhian, G. A., & Khairi, M. A. (2014, June), Factors Influencing Data Management Models Selection Paper presented at 2014 ASEE Annual Conference & Exposition, Indianapolis, Indiana. https://peer.asee.org/20484

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