The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Bitmap Join Indexes vs. Data Partitioning
Abstract
Scientific databases and data warehouses store large amounts of data ith several tables and attributes. For instance, the Sloan Digital Sky Survey (SDSS) astronomical database contains a large number of tables with hundreds of attributes, which can be queried in various combinations (Papadomanolakis & Ailamaki, 2004). These queries involve many tables using binary operations, such as joins. To speed up these queries, many optimization structures were proposed that can be divided into two main categories: redundant structures like materialized views, advanced indexing schemes (bitmap, bitmap join indexes, etc.) (Sanjay, Chaudhuri & Narasayya, 2000) and vertical partitioning (Sanjay, Narasayya & Yang 2004) and non redundant structures like horizontal partitioning (Sanjay, Narasayya & Yang 2004; Bellatreche, Boukhalfa & Mohania, 2007) and parallel processing (Datta, Moon, & Thomas, 2000; Stöhr, Märtens & Rahm, 2000). These optimization techniques are used either in a sequential manner ou combined. These combinations are done intra-structures: materialized views and indexes for redundant and partitioning and data parallel processing for no redundant. Materialized views and indexes compete for the same resource representing storage, and incur maintenance overhead in the presence of updates (Sanjay, Chaudhuri & Narasayya, 2000). None work addresses the problem of selecting combined optimization structures. In this paper, we propose two approaches; one for combining a non redundant structures horizontal partitioning and a redundant structure bitmap indexes in order to reduce the query processing and reduce the maintenance overhead, and another to exploit algorithms for vertical partitioning to generate bitmap join indexes. To facilitate the understanding of our approaches, for review these techniques in details.
Related Content
|
Renjith V. Ravi, Mangesh M. Ghonge, P. Febina Beevi, Rafael Kunst.
© 2022.
24 pages.
|
|
Manimaran A., Chandramohan Dhasarathan, Arulkumar N., Naveen Kumar N..
© 2022.
20 pages.
|
|
Ram Singh, Rohit Bansal, Sachin Chauhan.
© 2022.
19 pages.
|
|
Subhodeep Mukherjee, Manish Mohan Baral, Venkataiah Chittipaka.
© 2022.
17 pages.
|
|
Vladimir Nikolaevich Kustov, Ekaterina Sergeevna Selanteva.
© 2022.
23 pages.
|
|
Krati Reja, Gaurav Choudhary, Shishir Kumar Shandilya, Durgesh M. Sharma, Ashish K. Sharma.
© 2022.
18 pages.
|
|
Nwosu Anthony Ugochukwu, S. B. Goyal.
© 2022.
23 pages.
|
|
|