Title: Partitioning Technology and Fast Content Movements of Big Data

Year of Publication: Sep - 2015
Page Numbers: 1-10
Authors: Te-Yuan Lin , Chiou-Shann Fuh
Conference Name: The International Conference on Database, Data Warehouse, Data Mining and Big Data (DDDMBD2015)
- Indonesia

Abstract:


Database storage storing abundant data usually accompanies slow performance of query and data manipulation. This thesis presents a model and methodology of faster data manipulation (insert/delete) of mass data rows stored in a big table. In this thesis, it depicts the solution to manipulate large data sets of one table which moves into and out of another logical table with outstanding efficiency compared with traditional transactional way. With this idea, the table structure needs to be redesigned to accommodate and keep data, in other words, the table needs to be “partitioned”. It also covers partitioning strategies which are applied to various scenarios such as the data sliding window scenario, data archiving, and partition consolidation and movement practice.