scholarly journals A Multi-Objective Approach for Materialized View Selection

2019 ◽  
Vol 10 (2) ◽  
pp. 1-19 ◽  
Author(s):  
Jay Prakash ◽  
T.V. Vijay Kumar
Keyword(s):  
Decision Making ◽  
Response Time ◽  
Relevant Information ◽  
Maintenance Cost ◽  
View Selection ◽  
Materialized View Selection ◽  
Speed Up ◽  
Transactional Data ◽  
Selection Of ◽  
Better Than

In today's world, business transactional data has become the critical part of all business-related decisions. For this purpose, complex analytical queries have been run on transactional data to get the relevant information, from therein, for decision making. These complex queries consume a lot of time to execute as data is spread across multiple disparate locations. Materializing views in the data warehouse can be used to speed up processing of these complex analytical queries. Materializing all possible views is infeasible due to storage space constraint and view maintenance cost. Hence, a subset of relevant views needs to be selected for materialization that reduces the response time of analytical queries. Optimal selection of subset of views is shown to be an NP-Complete problem. In this article, a non-Pareto based genetic algorithm, is proposed, that selects Top-K views for materialization from a multidimensional lattice. An experiments-based comparison of the proposed algorithm with the most fundamental view selection algorithm, HRUA, shows that the former performs comparatively better than the latter. Thus, materializing views selected by using the proposed algorithm would improve the query response time of analytical queries and thereby facilitate in decision making.

scholarly journals A Multi-Objective Approach for Materialized View Selection

2021 ◽  
pp. 512-533
Author(s):  
Jay Prakash ◽  
T.V. Vijay Kumar
Keyword(s):  
Decision Making ◽  
Response Time ◽  
Relevant Information ◽  
Maintenance Cost ◽  
View Selection ◽  
Complex Queries ◽  
Materialized View Selection ◽  
Speed Up ◽  
Np Complete ◽  
Transactional Data

In today's world, business transactional data has become the critical part of all business-related decisions. For this purpose, complex analytical queries have been run on transactional data to get the relevant information, from therein, for decision making. These complex queries consume a lot of time to execute as data is spread across multiple disparate locations. Materializing views in the data warehouse can be used to speed up processing of these complex analytical queries. Materializing all possible views is infeasible due to storage space constraint and view maintenance cost. Hence, a subset of relevant views needs to be selected for materialization that reduces the response time of analytical queries. Optimal selection of subset of views is shown to be an NP-Complete problem. In this article, a non-Pareto based genetic algorithm, is proposed, that selects Top-K views for materialization from a multidimensional lattice. An experiments-based comparison of the proposed algorithm with the most fundamental view selection algorithm, HRUA, shows that the former performs comparatively better than the latter. Thus, materializing views selected by using the proposed algorithm would improve the query response time of analytical queries and thereby facilitate in decision making.

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.

Information Systems for the Management of the Organizations

2018 ◽  
pp. 313-346
Author(s):  
Antonio Juan Briones Penalver
Keyword(s):  
Decision Making ◽  
Global Economy ◽  
Relevant Information ◽  
First Century ◽  
Strategic Decision ◽  
Industrial Society ◽  
Strategic Decision Making ◽  
Relationship Of ◽  
The Relationship ◽  
Selection Of

This chapter investigates the main concepts and activities of information in strategic decision-making systems. Since information became the global economy value source for organizations, information assumes a key role in contributing to the development of the performance of organizations through the selection of relevant information for businesses. The relationship of the strategic management of information with business activities contributes to the process of strategic decision making for more effective and efficient decisions. The understanding of the importance of information as a strategic resource in the management of organizations is becoming more important to strategists than the formulation of strategy models of industrial society. In the twenty-first century, no manager will be able to set and implement the strategy successfully without a basic understanding of information for strategic decision making.

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 View Selection Using Set Based Particle Swarm Optimization

2018 ◽  
Vol 12 (3) ◽  
pp. 18-39 ◽  
Author(s):  
Amit Kumar ◽  
T.V. Vijay Kumar
Keyword(s):  
Decision Making ◽  
Particle Swarm Optimization ◽  
Data Warehouse ◽  
Response Times ◽  
Particle Swarm ◽  
Selection Algorithm ◽  
Swarm Optimization ◽  
Continuous Growth ◽  
View Selection ◽  
Materialized View Selection

A data warehouse is a central repository of historical data designed primarily to support analytical processing. These analytical queries are exploratory, long and complex in nature. Further, the rapid and continuous growth in the size of data warehouse increases the response times of such queries. Query response times need to be reduced in order to speedup decision making. This problem, being an NP-Complete problem, can be appropriately dealt with by using swarm intelligence techniques. One such technique, i.e. the set-based particle swarm optimization (SPSO), has been proposed to address this problem. Accordingly, a SPSO based view selection algorithm (SPSOVSA), which selects the Top-K views from a multidimensional lattice, is proposed. Experimental based comparison of SPSOVSA with the most fundamental view selection algorithm shows that SPSOVSA is able to select comparatively better quality Top-K views for materialization. The materialization of these selected views would improve the performance of analytical queries and lead to efficient decision making.

Materialized View Selection using Marriage in Honey Bees Optimization

2015 ◽  
Vol 5 (3) ◽  
pp. 1-25 ◽  
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.

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.

MVPP-Based Materialized View Selection in Data Warehouses Using Simulated Annealing

2020 ◽  
Vol 29 (03) ◽  
pp. 2050001
Author(s):  
Mohsen Mohseni ◽  
Mohammad Karim Sohrabi
Keyword(s):  
Simulated Annealing ◽  
Decision Support ◽  
Response Time ◽  
Decision Support Systems ◽  
Support Systems ◽  
Experimental Results ◽  
Total Cost ◽  
View Selection ◽  
Materialized View ◽  
Materialized View Selection

The process of extracting data from different heterogeneous data sources, transforming them into an integrated, unified and cleaned repository, and storing the result as a single entity leads to the construction of a data warehouse (DW), which facilitates access to data for the users of information systems and decision support systems. Due to their enormous volumes of data, processing of analytical queries of decision support systems need to scan very large amounts of data, which has a negative effect on the systems’ response time. Because of the special importance of online analytical processing (OLAP) in these systems, to enhance the performance and improve the query response time of the system, an appropriate number of views of the DW are selected for materialization and will be utilized for responding to the analytical queries, instead of direct access to the base relations. Memory constraint and views maintenance overhead are two main limitations that make it impossible, in most cases, to materialize all views of the DW. Selecting a proper set of views of DW for materialization, called materialized view selection (MVS) problem, is an important research issue that has been focused in various papers. In this paper, we have proposed a method, called P-SA, to select an appropriate set of views using an improved version of simulated annealing (SA) algorithm that utilizes a proper neighborhood selection strategy. P-SA uses the multiple view processing plan (MVPP) structure for selecting the views. Data and queries of a benchmark DW have been used in experimental results for evaluating the introduced method. The experimental results show better performance of the P-SA compared to other SA-based MVS methods for increasing the number of queries, in terms of the total cost of view maintenance and query processing. Moreover, the total cost of queries in the P-SA is also better than the other important SA-based MVS methods of the literature when the size of the DW is increased.

scholarly journals SISTEM PENDUKUNG KEPUTUSAN SELEKSI SUPPLIER BAHAN BAKU DENGAN METODE SIMPLE ADDITIVE WEIGHTING (SAW)

2020 ◽  
Vol 1 (1) ◽  
pp. 24-32
Author(s):  
Oke Hariansyah
Keyword(s):  
Decision Making ◽  
Supplier Selection ◽  
Multiple Attribute Decision Making ◽  
Test Results ◽  
Weighting Method ◽  
Multiple Attribute ◽  
Speed Up ◽  
Simple Additive Weighting ◽  
Industry Needs ◽  
Selection Of

Selection of suppliers for the company or the industry needs to be done to get the supplier truly able to meet the company's needs consistently. In this study, will be appointed a case of finding the best alternative based on the criteria that have been determined using the Fuzzy Multiple Attribute Decision Making (FMADM) Simple Additive Weighting method (SAW). This method was chosen because it is able to select the best alternative from a number of alternatives, in this case the alternative is intended to determine the best supplier based on specified criteria. Based on test results, the application of the system to be implemented to help the work of doing the selection of purchasing parts supplier, can speed up the process of selecting suppliers, and can facilitate the purchasing section chief part in determining the proper supplier selection, proper and appropriate

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