The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Statistical Sampling to Instantiate Materialized View Selection Problems in Data Warehouses
|
|
Author(s): Mesbah U. Ahmed (University of Toledo, USA), Vikas Agrawal (Fayetteville State University, USA), Udayan Nandkeolyar (University of Toledo, USA)and P. S. Sundararaghavan (University of Toledo, USA)
Copyright: 2008
Pages: 25
Source title:
Data Warehousing and Mining: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): John Wang (Montclair State University, USA)
DOI: 10.4018/978-1-59904-951-9.ch130
Purchase
|
Abstract
In any online decision support system, the backbone is a data warehouse. In order to facilitate rapid response to complex business decision support queries, it is a common practice to materialize an appropriate set of the views at the data warehouse. However, it typically requires the solution of the Materialized View Selection (MVS) problem to select the right set of views to materialize in order to achieve a certain level of service given a limited amount of resource such as materialization time, storage space, or view maintenance time. Dynamic changes in the source data and the end users requirement necessitate rapid and repetitive instantiation and solution of the MVS problem. In an online decision support context, time is of the essence in finding acceptable solutions to this problem. In this chapter, we have used a novel approach to instantiate and solve four versions of the MVS problem using three sampling techniques and two databases. We compared these solutions with the optimal solutions corresponding to the actual problems. In our experimentation, we found that the sampling approach resulted in substantial savings in time while producing good solutions.
Related Content
|
Md Sakir Ahmed, Abhijit Bora.
© 2024.
15 pages.
|
|
Lakshmi Haritha Medida, Kumar.
© 2024.
18 pages.
|
|
Gypsy Nandi, Yadika Prasad.
© 2024.
16 pages.
|
|
Saurav Bhattacharjee, Sabiha Raiyesha.
© 2024.
14 pages.
|
|
Naren Kathirvel, Kathirvel Ayyaswamy, B. Santhoshi.
© 2024.
26 pages.
|
|
K. Sudha, C. Balakrishnan, T. P. Anish, T. Nithya, B. Yamini, R. Siva Subramanian, M. Nalini.
© 2024.
25 pages.
|
|
Sabiha Raiyesha, Papul Changmai.
© 2024.
28 pages.
|
|
|