Shaykhian, G. A., & Khairi, M. A. (2014, June), Centralized or Federated Data Management Models, IT Professionals’ Preferences  Paper presented at 2014 ASEE Annual Conference & Exposition, Indianapolis, Indiana. 10.18260/1-2--20157

\bibitem{asee_peer_20157}
  Shaykhian, G. A., \& Khairi, M. A. (2014, June), \emph{Centralized or Federated Data Management Models, IT Professionals’ Preferences}
  Paper presented at 2014 ASEE Annual Conference \& Exposition, Indianapolis, Indiana.
  10.18260/1-2--20157

Gholam Ali Shaykhian and Mohd A. Khairi.  "Centralized or Federated Data Management Models, IT Professionals’ Preferences".  2014 ASEE Annual Conference & Exposition, Indianapolis, Indiana, 2014, June.  ASEE Conferences, 2014.  https://peer.asee.org/20157 Internet.  22 Apr, 2025

\bibitem{asee_peer_20157}
  Gholam Ali Shaykhian and Mohd A. Khairi.
  "Centralized or Federated Data Management Models, IT Professionals’ Preferences".
  \emph{2014 ASEE Annual Conference \& Exposition, Indianapolis, Indiana, 2014, June}.
  ASEE Conferences, 2014.
  https://peer.asee.org/20157 Internet.  22 Apr, 2025

@INPROCEEDINGS{asee_peer_20157
author = "Gholam Ali Shaykhian and Mohd A. Khairi"
title = "Centralized or Federated Data Management Models, IT Professionals’ Preferences"
booktitle = "2014 ASEE Annual Conference \& Exposition"
year = "2014"
month = "June"
address = "Indianapolis, Indiana"
publisher = "ASEE Conferences"
note = {https://peer.asee.org/20157}
number = {10.18260/1-2--20157}
}

TY  - CPAPER
AB  - 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.
AU  - Gholam Ali Shaykhian
AU  - Mohd A. Khairi
CY  - Indianapolis, Indiana
DA  - 2014/06/15
PB  - ASEE Conferences
TI  - Centralized or Federated Data Management Models, IT Professionals’ Preferences
UR  - https://peer.asee.org/20157
DO  - 10.18260/1-2--20157
ER  -