Vertical Fragmentation in Databases Using Data-Mining Technique

2009 ◽  
pp. 2543-2563 ◽  
Author(s):  
Narasimhaiah Gorla ◽  
Pang Wing Yan Betty
Keyword(s):  
Data Mining ◽  
Relational Databases ◽  
Real Life ◽  
Optimal Solution ◽  
Database Systems ◽  
Future Research ◽  
Data Mining Technique ◽  
Mining Technique ◽  
Vertical Fragmentation ◽  
Using Data

A new approach to vertical fragmentation in relational databases is proposed using association rules, a data-mining technique. Vertical fragmentation can enhance the performance of database systems by reducing the number of disk accesses needed by transactions. By adapting Apriori algorithm, a design methodology for vertical partitioning is proposed. The heuristic methodology is tested using two real-life databases for various minimum support levels and minimum confidence levels. In the smaller database, the partitioning solution obtained matched the optimal solution using exhaustive enumeration. The application of our method on the larger database resulted in the partitioning solution that has an improvement of 41.05% over unpartitioned solution and took less than a second to produce the solution. We provide future research directions on extending the procedure to distributed and object-oriented database designs.

Vertical Fragmentation in Databases Using Data-Mining Technique

2010 ◽  
pp. 2248-2268
Author(s):  
Narasimhaiah Gorla ◽  
Pang W.Y. Betty
Keyword(s):  
Data Mining ◽  
Relational Databases ◽  
Real Life ◽  
Optimal Solution ◽  
Database Systems ◽  
Future Research ◽  
Data Mining Technique ◽  
Mining Technique ◽  
Vertical Fragmentation ◽  
Using Data

A new approach to vertical fragmentation in relational databases is proposed using association rules, a data-mining technique. Vertical fragmentation can enhance the performance of database systems by reducing the number of disk accesses needed by transactions. By adapting Apriori algorithm, a design methodology for vertical partitioning is proposed. The heuristic methodology is tested using two real-life databases for various minimum support levels and minimum confidence levels. In the smaller database, the partitioning solution obtained matched the optimal solution using exhaustive enumeration. The application of our method on the larger database resulted in the partitioning solution that has an improvement of 41.05% over unpartitioned solution and took less than a second to produce the solution. We provide future research directions on extending the procedure to distributed and object-oriented database designs.

Vertical Fragmentation in Databases Using Data-Mining Technique

2011 ◽  
pp. 178-197 ◽  
Author(s):  
Gorla Narasimhaiah ◽  
Wing Yan Betty Pang
Keyword(s):  
Data Mining ◽  
Relational Databases ◽  
Optimal Solution ◽  
Database Systems ◽  
Future Research ◽  
Data Mining Technique ◽  
Confidence Levels ◽  
Mining Technique ◽  
Vertical Fragmentation ◽  
Using Data

A new approach to vertical fragmentation in relational databases is proposed using association rules, a data-mining technique. Vertical fragmentation can enhance the performance of database systems by reducing the number of disk accesses needed by transactions. By adapting Apriori algorithm, a design methodology for vertical partitioning is proposed. The heuristic methodology is tested using two reallife databases for various minimum support levels and minimum confidence levels. In the smaller database, the partitioning solution obtained matched the optimal solution using exhaustive enumeration. The application of our method on the larger database resulted in the partitioning solution that has an improvement of 41.05% over unpartitioned solution and took less than a second to produce the solution. We provide future research directions on extending the procedure to distributed and object-oriented database designs.

scholarly journals Implementation of Hospital Examination Reservation System Using Data Mining Technique

2015 ◽  
Vol 21 (2) ◽  
pp. 95
Author(s):  
Hyo Soung Cha ◽  
Tae Sik Yoon ◽  
Ki Chung Ryu ◽  
Il Won Shin ◽  
Yang Hyo Choe ◽  
...  
Keyword(s):  
Data Mining ◽  
Data Mining Technique ◽  
Mining Technique ◽  
Reservation System ◽  
Using Data

scholarly journals Analysis on Insurance Policy and Related Customer Behavior using Data Mining Technique

2016 ◽  
Vol 139 (6) ◽  
pp. 46-47
Author(s):  
M. Ashrafa ◽  
D. Asha ◽  
D. Radha ◽  
M. Sangeetha ◽  
R. Jayaparvathy
Keyword(s):  
Data Mining ◽  
Customer Behavior ◽  
Insurance Policy ◽  
Data Mining Technique ◽  
Mining Technique ◽  
Using Data

scholarly journals Impact of Data Mining Technique in Education Institutions

2015 ◽  
Vol 4 (02) ◽  
pp. 01-07
Author(s):  
Mr. Bhushan Bandre, Ms. Rashmi Khalatkar
Keyword(s):  
Data Mining ◽  
Educational Institution ◽  
Educational Institutions ◽  
Data Mining Technique ◽  
Admission Process ◽  
Data Mining Techniques ◽  
Mining Technique ◽  
Major Parameter ◽  
Using Data ◽  
Major Decision

Major decision making process using large amount of data can be done by various techniques using data mining. In education sectors various data mining techniques are implemented to analyze the student’s data from the admission process itself. Due to large number of educational institution in India, excellence becomes a major parameter for the institutions to grow and with stand. Nowadays education institutions use data mining techniques to show their excellence. The main objective of this work to present an analysis of individual semester wise results of engineering college students using different techniques of data mining. Here we used different classification algorithms like decision tree, rule based, function based and Bayesian algorithms to analyze the semester results and comparison is made by considering parameters like accuracy and error rate. Our output shows the most suited algorithm for analyzing data in educational institutions.

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