SPEEDING UP MATERIALIZED VIEW SELECTION IN DATA WAREHOUSES USING A RANDOMIZED ALGORITHM

2001 ◽  
Vol 10 (03) ◽  
pp. 327-353 ◽  
Author(s):  
MINSOO LEE ◽  
JOACHIM HAMMER
Keyword(s):  
Randomized Algorithm ◽  
Linear Increase ◽  
Selection Problem ◽  
Maintenance Cost ◽  
Materialized Views ◽  
Dramatic Improvement ◽  
Heterogeneous Information ◽  
View Selection ◽  
Heterogeneous Information Sources ◽  
Materialized View Selection

A data warehouse stores information that is collected from multiple, heterogeneous information sources for the purpose of complex querying and analysis. Information in the warehouse is typically stored in the form of materialized views, which represent pre-computed portions of frequently asked queries. One of the most important tasks when designing a warehouse is the selection of materialized views to be maintained in the warehouse. The goal is to select a set of views in such a way as to minimize the total query response time over all queries, given a limited amount of time for maintaining the views (maintenance-cost view selection problem). In this paper, we propose an efficient solution to the maintenance-cost view selection problem using a genetic algorithm for computing a near-optimal set of views. Specifically, we explore the maintenance-cost view selection problem in the context of OR view graphs. We show that our approach represents a dramatic improvement in time complexity over existing search-based approaches using heuristics. Our analysis shows that the algorithm consistently yields a solution that lies within 10% of the optimal query benefit while at the same time exhibiting only a linear increase in execution time. We have implemented a prototype version of our algorithm which is used to simulate the measurements used in the analysis of our approach.

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

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.

Research on Materialized Views under Disk-Space Constraint in Data Warehouse

2014 ◽  
Vol 989-994 ◽  
pp. 1955-1958
Author(s):  
Lei Ma
Keyword(s):  
Data Warehouse ◽  
Space Constraint ◽  
Maintenance Cost ◽  
Materialized Views ◽  
Ranking Algorithm ◽  
Disk Space ◽  
View Selection ◽  
Materialized View ◽  
Processing Cost ◽  
Materialized View Selection

Materialized view is an important topic in data warehouse research, and also affects the query efficiency and maintenance cost. The disk-space view-selection problem is to select a set of materialized views for the purpose of minimizing the total query processing cost and the total maintenance cost. In this paper we introduce evolutionary algorithm using stochastic ranking algorithm, which can enable materialized view selection under disk-space constraint. The algorithm improve the stochastic ranking algorithm, which can find a near-optimal feasible solution. This paper use the algorithm in Police data warehouse.

scholarly journals Optimal Materialized View Management in Distributed Environment Using Random Walk Approach

2016 ◽  
Vol 1 ◽  
pp. 67
Author(s):  
Purushottam Bagale ◽  
Shashidhar Ram Joshi
Keyword(s):  
Research Work ◽  
Search Time ◽  
Distributed Database ◽  
Maintenance Cost ◽  
Materialized Views ◽  
Distributed Environment ◽  
View Selection ◽  
Materialized View ◽  
Business Application ◽  
Materialized View Selection

<p>Materialized View selection and maintenance is a critical problem in many applications. In large databases particularly in distributed database, query response time plays an important role as timely access to information and it is the basic requirement of successful business application. The materialization of all views is not possible because of the space constraint and maintenance cost constraint. Materialized views selection is one of the crucial decisions in designing a data warehouse for optimal efficiency. Selecting a suitable set of views that minimizes the total cost associated with the materialized views is the key component in distributed database environment. Several solutions have been proposed in the literature to solve this problem. However, most studies do not encompass search time, storage constrains and maintenance cost. In this research work two algorithms are depicted; first for materialized view selection and maintenance in distributed environment where database is distributed, Second algorithm is for node selection in distributed environment. </p><p><em>Journal of Advanced College of Engineering and Management, Vol.1</em>, 2015, 69-75</p>

Research on Materialized View Selection in the Data Warehouse

2011 ◽  
Vol 55-57 ◽  
pp. 361-366
Author(s):  
Li Juan Zhou ◽  
Hai Jun Geng ◽  
Ming Sheng Xu
Keyword(s):  
Data Warehouse ◽  
High Efficiency ◽  
Resource Constraints ◽  
Maintenance Cost ◽  
Materialized Views ◽  
View Selection ◽  
Materialized View ◽  
Cost Constraints ◽  
Processing Cost ◽  
Materialized View Selection

Materialized view is an effective method for improving the efficiency of queries in data warehouse system, and the problem of materialized view selection is one of the most important decisions. In this paper, an algorithm was proposed to select a set of materialized views under maintenance cost constraints for the purpose of minimizing the total query processing cost; the algorithm adopts the dynamic penalty function to solve the resource constraints view selection. The experimental study shows that the algorithm has better solutions and high efficiency.

Materialized View Selection in the Data Warehouse

2010 ◽  
Vol 29-32 ◽  
pp. 1133-1138 ◽  
Author(s):  
Li Juan Zhou ◽  
Hai Jun Geng ◽  
Ming Sheng Xu
Keyword(s):  
Decision Support ◽  
Data Warehouse ◽  
Materialized Views ◽  
Storage Space ◽  
View Selection ◽  
Materialized View ◽  
Query Response Time ◽  
Materialized View Selection ◽  
Optimal Efficiency ◽  
The Cost

A data warehouse stores materialized views of data from one or more sources, with the purpose of efficiently implementing decision-support or OLAP queries. Materialized view selection is one of the crucial decisions in designing a data warehouse for optimal efficiency. The goal is to select an appropriate set of views that minimizes sum of the query response time and the cost of maintaining the selected views, given a limited amount of resource, e.g., materialization time, storage space, etc. In this article, we present an improved PGA algorithm to accomplish the view selection problem; the experiments show that our proposed algorithm shows it’s superior.

A Hybrid Approach for Data Warehouse View Selection

2008 ◽  
pp. 3085-3115
Author(s):  
Biren Shah ◽  
Karthik Ramachandran ◽  
Vijay Raghavan
Keyword(s):  
Data Warehouse ◽  
Hybrid Approach ◽  
Greedy Heuristic ◽  
Materialized Views ◽  
View Selection ◽  
Materialized View Selection ◽  
Optimal Efficiency ◽  
Access Patterns ◽  
Multiple Query ◽  
Selection Of

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.

Handling Constraints Using Penalty Functions in Materialized View Selection

2019 ◽  
Vol 8 (2) ◽  
pp. 1-17
Author(s):  
Anjana Gosain ◽  
Kavita Sachdeva
Keyword(s):  
Query Processing ◽  
Penalty Function ◽  
Penalty Functions ◽  
Maintenance Cost ◽  
Penalty Function Method ◽  
View Selection ◽  
Materialized View ◽  
Processing Cost ◽  
Adaptive Penalty ◽  
Materialized View Selection

Materialized view selection (MVS) plays a vital role for efficiently making decisions in a data warehouse. This problem is NP-hard and constrained optimization problem. The authors have handled both the space and maintenance cost constraint using penalty functions. Three penalty function methods i.e. static, dynamic and adaptive penalty functions have been used for handling constraints and Backtracking Search Optimization algorithm (BSA) has been used for optimizing the total query processing cost. Experiments were conducted comparing the static, dynamic and adaptive penalty functions on varying the space constraint. The adaptive penalty function method yields the best results in terms of minimum query processing cost and achieves the optimality, scalability and feasibility of the problem on varying the lattice dimensions and on increasing the number of user queries. The authors proposed work has been compared with other evolutionary algorithms i.e. PSO and genetic algorithm and yields better results in terms of minimum total query processing cost of the materialized views.

Materialized Views in Multidimensional Databases

2003 ◽  
pp. 222-251 ◽  
Author(s):  
Stefano Paraboschi ◽  
Giuseppe Sindoni ◽  
Elena Baralis ◽  
Ernst Teniente
Keyword(s):  
Data Models ◽  
Selection Problem ◽  
Materialized Views ◽  
Incremental Maintenance ◽  
View Selection ◽  
Materialized View ◽  
Multidimensional Databases ◽  
Maintenance Techniques ◽  
Definition Of

This chapter presents materialized views in the context of multidimensional databases (MDDBs). A materialized view is a view whose content is explicitly stored in the database. The advantage of materializing views is that it is not necessary to recompute the query every time the view is accessed. The shortcoming is that it has to be kept consistent with the updates on the base tables. However, efficient incremental maintenance techniques have been proposed. MDDBs are an ideal environment for materialized views because frequency of updates is low, MDDB data models permit easy adoption of incremental maintenance, and queries can be modeled in such a way to allow an easy definition of the view selection problem, i.e., the problem of selecting which query to materialize in an MDDB. Hence, we present the problems of choosing and maintaining materialised views with the corresponding solutions.

Materialized View Selection using Improvement based Bee Colony Optimization

2015 ◽  
Vol 7 (4) ◽  
pp. 35-61 ◽  
Author(s):  
Biri Arun ◽  
T.V. Vijay Kumar
Keyword(s):  
Optimization Algorithm ◽  
Strategic Decision ◽  
Materialized Views ◽  
Data Warehouses ◽  
View Selection ◽  
Bee Colony ◽  
New Information ◽  
Corporate Entity ◽  
Bee Colony Optimization ◽  
Materialized View Selection

In the present information age, data and information are vital not just for the survival of any corporate entity, but also to provide it with an edge over its competitors. Data warehouses have become the foundational databases of almost every corporation. However, extracting new information from these data warehouses takes hours, and even days, which is practically unacceptable. Materialized views have been popularly used to facilitate fast information extraction. However, the selection of appropriate views, which significantly accelerate information synthesis is an NP-Complete problem. The aim of this paper is to select near optimal sets of views for materialization using the improvement bee colony optimization algorithm. The experimental results indicate that the improvement bee colony optimization algorithm performs better than the constructive bee colony optimization algorithm and the fundamental view selection algorithm HRUA. The views thus selected would significantly minimize the response time of analytical queries, when materialized, resulting in efficient strategic decision making.

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