A benchmark for best view selection of 3D objects

Materialized view selection is one of the crucial decisions in designing a data warehouse for optimal efficiency. Static selection of views may materialize certain views that are not beneficial as the data and usage trends change over time. On the contrary, dynamic selection of views works better only for queries demanding a high degree of aggregation. These facts point to the need for a technique that combines the improved response time of the static approach and the automated tuning capability of the dynamic approach. In this article, we propose a hybrid approach for the selection of materialized views. The idea is to partition the collection of all views into a static and dynamic set such that views selected for materialization from the static set are persistent over multiple query (and maintenance) windows, whereas views selected from the dynamic set can be queried and/or replaced on the fly. Highly aggregated views are selected on the fly based on the query access patterns of users, whereas the more detailed static set of views plays a significant role in the efficient maintenance of the dynamic set of views and in answering certain detailed view queries. We prove that our proposed strategy satisfies the monotonicity requirements, which is essential in order for the greedy heuristic to deliver competitive solutions. Experimental results show that our approach outperforms Dynamat, a well-known dynamic view management system that is known to outperform optimal static view selection.

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Best view selection and compression of moving objects in IR sequences

2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221) ◽

10.1109/icassp.2001.941245 ◽

2002 ◽

Cited By ~ 1

Author(s):

N. Vaswani ◽

R. Chellappa

Keyword(s):

Moving Objects ◽

View Selection ◽

Best View Selection

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Greedy Selection of Materialized Views

International Journal of Computer and Communication Technology ◽

10.47893/ijcct.2010.1006 ◽

2010 ◽

pp. 47-58

Author(s):

T.V. Vijay Kumar ◽

Aloke Ghoshal

Keyword(s):

Polynomial Time ◽

Execution Time ◽

Time Complexity ◽

Materialized Views ◽

View Selection ◽

Multidimensional Lattice ◽

View Materialization ◽

Good Set ◽

Selection Of

Greedy based approach for view selection at each step selects a beneficial view that fits within the space available for view materialization. Most of these approaches are focused around the HRU algorithm, which uses a multidimensional lattice framework to determine a good set of views to materialize. The HRU algorithm exhibits high run time complexity as the number of possible views is exponential with respect to the number of dimensions. The PGA algorithm provides a scalable solution to this problem by selecting views for materialization in polynomial time relative to the number of dimensions. This paper compares the HRU and the PGA algorithm. It was experimentally deduced that the PGA algorithm, in comparison with the HRU algorithm, achieves an improved execution time with lowered memory and CPU usages. The HRU algorithm has an edge over the PGA algorithm on the quality of the views selected for materialization.

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Multi-Objective Materialized View Selection Using Improved Strength Pareto Evolutionary Algorithm

International Journal of Artificial Intelligence and Machine Learning ◽

10.4018/ijaiml.2019070101 ◽

2019 ◽

Vol 9 (2) ◽

pp. 1-21

Author(s):

Jay Prakash ◽

T. V. Vijay Kumar

Keyword(s):

Evolutionary Algorithm ◽

Selection Problem ◽

Maintenance Cost ◽

Materialized Views ◽

Trade Off ◽

View Selection ◽

Materialized View ◽

Strength Pareto Evolutionary Algorithm ◽

Materialized View Selection ◽

Selection Of

A data warehouse system uses materialized views extensively in order to speedily tackle analytical queries. Considering that all possible views cannot be materialized due to maintenance cost and storage constraints, the selection of an appropriate set of views to materialize that achieve an optimal trade-off among query response time, maintenance cost, and the storage constraint becomes an essential necessity. The selection of such an appropriate set of views for materialization is referred to as the materialized views selection problem, which is an NP-Complete problem. In the last two decades, several new selection approaches, based on heuristics, have been proposed. Most of these have used a single objective or weighted sum approach to address the various constraints. In this article, an attempt has been made to address the bi-objective materialized view selection problem, where the objective is to minimize the view evaluation cost of materialized views and the view evaluation cost of the non-materialized views, using the Improved Strength Pareto Evolutionary Algorithm. The experimental results show that the proposed multi-objective view selection algorithm is able to select the Top-K views that achieves a reasonable trade-off between the two objectives. Materializing these selected views would reduce the query response times for analytical queries and thereby facilitates the decision-making process.

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Materialized View Selection using Marriage in Honey Bees Optimization

International Journal of Natural Computing Research ◽

10.4018/ijncr.2015070101 ◽

2015 ◽

Vol 5 (3) ◽

pp. 1-25 ◽

Cited By ~ 12

Author(s):

Biri Arun ◽

T.V. Vijay Kumar

Keyword(s):

Data Warehouse ◽

Honey Bees ◽

Response Times ◽

Large Data ◽

Strategic Decision ◽

Selection Algorithm ◽

View Selection ◽

Materialized View Selection ◽

On Line ◽

Selection Of

Data warehouse was designed to cater to the strategic decision making needs of an organization. Most queries posed on them are on-line analytical queries, which are complex and computation intensive in nature and have high query response times when processed against a large data warehouse. This time can be substantially reduced by materializing pre-computed summarized views and storing them in a data warehouse. All possible views cannot be materialized due to storage space constraints. Also, an optimal selection of subsets of views is shown to be an NP-Complete problem. This problem of view selection has been addressed in this paper by selecting a beneficial set of views, from amongst all possible views, using the swarm intelligence technique Marriage in Honey Bees Optimization (MBO). An MBO based view selection algorithm (MBOVSA), which aims to select views that incur the minimum total cost of evaluating all the views (TVEC), is proposed. In MBOVSA, the search has been intensified by incorporating the royal jelly feeding phase into MBO. MBOVSA, when compared with the most fundamental greedy based view selection algorithm HRUA, is able to select comparatively better quality views.

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