Book ChapterDOI
Materialized view construction using linearizable nonlinear regression
Soumya Sen,Partha Ghosh,Agostino Cortesi +2 more
- Vol. 395, pp 261-276
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TLDR
This research work applies to recently executed query set to analyze the attribute affinity and then the materialized view is formed based on the result of attribute affinity to keep the benefit of both the approaches.Abstract:
Query processing at runtime is an important issue for data-centric applications. A faster query execution is highly required which means searching and returning the appropriate data of database. Different techniques have been proposed over the time and materialized view construction is one of them. The efficiency of a materialized view (MV) is measured based on hit ratio, which indicates the ratio of number of successful search to total numbers of accesses. Literature survey shows that few research works has been carried out to analyze the relationship between the attributes based on nonlinear equations for materialized view creation. However, as nonlinear regression is slower, in this research work they are mapped into linear equations to keep the benefit of both the approaches. This approach is applied to recently executed query set to analyze the attribute affinity and then the materialized view is formed based on the result of attribute affinity.read more
Citations
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Book ChapterDOI
Association Based Multi-attribute Analysis to Construct Materialized View
TL;DR: An algorithm which generates a materialized view by considering the frequencies of the multiple attributes at a time taken from a database with the help of Apriori algorithm is proposed, which supports scalabilisalubrityty as well as flexibility.
Journal ArticleDOI
Construction and distribution of materialized views in Non-binary data space
TL;DR: In this paper, the authors proposed a non-binary data space approach to construct a weighted materialized view in a distributed environment by applying the association mining technique in the non-binary data space.
Book ChapterDOI
Construction of Materialized Views in Non-Binary Data Space
TL;DR: In this article, the authors proposed a non-binary data space based approach to construct weighted materialized views, based on the association mining techniques, by applying it in a Non-Binary Data Space.
Proceedings ArticleDOI
Materialized View Driven Architecture over Lattice of Cuboids in Data Warehouse
TL;DR: In this article, a fuzzy based materialized data-cube driven warehouse architecture for fast decision making is proposed, which achieves faster data analysis and better hit-miss ratio in the materialised data-cubes.
References
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Proceedings ArticleDOI
Implementing data cubes efficiently
TL;DR: In this article, a lattice framework is used to express dependencies among views and greedy algorithms are presented to determine a good set of views to materialize, with a small constant factor of optimal.
Proceedings Article
Automated Selection of Materialized Views and Indexes in SQL Databases
TL;DR: This paper presents an end-to-end solution to the problem of selecting materialized views and indexes for SQL databases, and describes results of extensive experimental evaluation that demonstrate the effectiveness of the techniques.
Proceedings ArticleDOI
Optimizing queries using materialized views: a practical, scalable solution
TL;DR: A fast and scalable algorithm for determining whether part or all of a query can be computed from materialized views and how it can be incorporated in transformation-based optimizers is presented.
Book
Incremental maintenance of views with duplicates
Timothy G. Griffin,Leonid Libkin +1 more
TL;DR: An algorithm that propagates changes from base relations to materialized views is presented, based on reasoning about equivalence of bag-valued expressions, and it is proved that it is correct and preserves a certain notion of minimality that ensures that no unnecessary tuples are computed.
Journal Article
Clustering-based materialized view selection in data warehouses
TL;DR: In this article, a framework for materialized view selection that exploits a data mining technique (clustering), in order to determine clusters of similar queries is proposed. But it is based on cost models that evaluate the cost of accessing data using views and the costs of storing these views.