Proceedings ArticleDOI
Dynamic incremental maintenance of materialized view based on attribute affinity
Partha Ghosh,Soumya Sen +1 more
- pp 12-17
TLDR
In this paper authors adopt an incremental view maintenance policy based on attribute affinity to update the materialized views at run time without using extra space and minimizing the data transfer between the secondary memory and primary memory (where the active materialization views reside).Abstract:
View materialization is being practiced over several years in large data centric applications like database, data warehouse, data mining etc. for faster query processing. Initially the materialized views are formed based on some methodologies, however the performance (hit-miss ratio) of the materialized views may degrade after certain time if the incoming query pattern changes. This situation could be handled efficiently by employing a view maintenance scheme which works dynamically during query execution at run time. As these materialized views involves huge amount of data, consideration of time and space complexity during the maintenance process plays an important role. In this paper authors adopt an incremental view maintenance policy based on attribute affinity to update the materialized views at run time without using extra space and minimizing the data transfer between the secondary memory and primary memory (where the active materialized views reside). This in turn reduces time complexity and supports incremental maintenance eliminating the requirement of full replacement of existing materialized views.read more
Citations
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DissertationDOI
Reducing the View Selection Problem through Code Modeling: Static and Dynamic approaches
TL;DR: This thesis work defines a process to determine the minimal set of workload queries and the set of views to materialize and proposes a dynamic process that allows users to upgrade the CoDe model with a context-aware editor, and build an optimized lattice structure able to minimize the effort to recalculate it.
Posted Content
Frequent Query Matching in Dynamic Data Warehousing
TL;DR: An experimental platform is developed using real data-sets to evaluate the effectiveness in terms of performance and precision of the proposed techniques to remove the mismatch between the MV collection and reporting requirements.
Journal ArticleDOI
View materialization using fuzzy MAX–MIN composition with association rule mining (VMFCA)
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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Posted Content
Clustering-Based Materialized View Selection in Data Warehouses
TL;DR: In this paper, 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.
Proceedings ArticleDOI
Graph structured views and their incremental maintenance
Yue Zhuge,Hector Garcia-Molina +1 more
TL;DR: This work defines simple views and materialized views for such graph structured data, analyzing options for representing record identity and references in the view and develops incremental maintenance algorithms for these views.
Proceedings ArticleDOI
A framework for designing materialized views in data warehousing environment
TL;DR: This work addresses some issues related to determining this set of shared views to be materialized in order to achieve the best combination of good performance and low maintenance, and provides an algorithm for achieving this goal.
Proceedings ArticleDOI
View maintenance after view synchronization
TL;DR: The approach is to regard the complex changes done to a view definition after synchronization as an atomic unit; another is to exploit knowledge of how the view definition was synchronized, especially the containment information between the old and new views.
Book ChapterDOI
Clustering-based materialized view selection in data warehouses
TL;DR: A framework for materialized view selection is proposed that exploits a data mining technique (clustering) in order to determine clusters of similar queries and a view merging algorithm that builds a set of candidate views, as well as a greedy process for selecting aSet of views to materialize.
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