Examining the Performance of Vertical Fragmentation using FP-MAX Algorithm

Today’s business Environment has an increasing need for consistent, scalable, reliable and accessible information which grows steadily.The purpose of this work is to analyse the performance of Vertical Fragmentation on large as well as small database such as educational database, data warehouses, medical databases. Vertical Fragmentation has an important impact in improving the performance of modern applications like document management, multimedia and hypermedia applications. With vertical partitioning, the disk access can be reduced by minimizing the access to irrelevant instance variables when executing the queries. In Present work FP-MAX data mining algorithm is used to extract frequent item set attributes of a large database table. The frequent accessed instance variables are grouped called Vertical fragments. The cost model is applied on Vertical fragments. The results are analyzed for small as well as large datasets in form of Max memory usage, Total time, cost and Frequent itemset count