Improved Quality View Selection for Analytical Query Performance Enhancement Using Particle Swarm Optimization

2017 ◽  
Vol 24 (06) ◽  
pp. 1740001 ◽  
Author(s):  
Amit Kumar ◽  
T. V. Vijay Kumar
Keyword(s):  
Decision Making ◽  
Particle Swarm Optimization ◽  
Response Times ◽  
Particle Swarm ◽  
Strategic Decision ◽  
Experimental Comparison ◽  
Selection Algorithm ◽  
Interaction Operator ◽  
Swarm Optimization ◽  
View Selection

A data warehouse, which is a central repository of the detailed historical data of an enterprise, is designed primarily for supporting high-volume analytical processing in order to support strategic decision-making. Queries for such decision-making are exploratory, long and intricate in nature and involve the summarization and aggregation of data. Furthermore, the rapidly growing volume of data warehouses makes the response times of queries substantially large. The query response times need to be reduced in order to reduce delays in decision-making. Materializing an appropriate subset of views has been found to be an effective alternative for achieving acceptable response times for analytical queries. This problem, being an NP-Complete problem, can be addressed using swarm intelligence techniques. One such technique, i.e., the similarity interaction operator-based particle swarm optimization (SIPSO), has been used to address this problem. Accordingly, a SIPSO-based view selection algorithm (SIPSOVSA), which selects the Top-[Formula: see text] views from a multidimensional lattice, has been proposed in this paper. Experimental comparison with the most fundamental view selection algorithm shows that the former is able to select relatively better quality Top-[Formula: see text] views for materialization. As a result, the views selected using SIPSOVSA improve the performance of analytical queries that lead to greater efficiency in decision-making.

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.

Automatic View Selection Algorithm Based on Particle Swarm Optimization

2014 ◽  
Vol 687-691 ◽  
pp. 1399-1403
Author(s):  
Xiao Ling Yao ◽  
Yan Ni Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Transfer Function ◽  
Optimization Problem ◽  
Particle Swarm ◽  
Selection Algorithm ◽  
Swarm Optimization ◽  
Optimal Position ◽  
Viewpoint Selection ◽  
View Selection ◽  
Interesting Part

This paper proposes an automatic viewpoint selection algorithm based on the particle swarm optimization. The method introduces particle swarm optimization algorithm into the design of transfer function and viewpoint selection. By the method, the search for the transfer function and the optimal viewpoint are redeveloped as a global optimization problem to reduce the reluctant computations and interactions. And the image sequence focuses on the interesting part and displays the objects on the optimal position.

Materialized View Selection Using Self-Adaptive Perturbation Operator-Based Particle Swarm Optimization

2020 ◽  
Vol 11 (3) ◽  
pp. 50-67
Author(s):  
Amit Kumar ◽  
T. V. Vijay Kumar
Keyword(s):  
Particle Swarm Optimization ◽  
Response Time ◽  
Data Warehouse ◽  
Particle Swarm ◽  
Data Warehouses ◽  
Perturbation Operator ◽  
Selection Algorithm ◽  
Swarm Optimization ◽  
View Selection ◽  
Self Adaptive

A data warehouse is a central repository of time-variant and non-volatile data integrated from disparate data sources with the purpose of transforming data to information to support data analysis. Decision support applications access data warehouses to derive information using online analytical processing. The response time of analytical queries against speedily growing size of the data warehouse is substantially large. View materialization is an effective approach to decrease the response time for analytical queries and expedite the decision-making process in relational implementations of data warehouses. Selecting a suitable subset of views that deceases the response time of analytical queries and also fit within available storage space for materialization is a crucial research concern in the context of a data warehouse design. This problem, referred to as view selection, is shown to be NP-Hard. Swarm intelligence have been widely and successfully used to solve such problems. In this paper, a discrete variant of particle swarm optimization algorithm, i.e. self-adaptive perturbation operator based particle swarm optimization (SPOPSO), has been adapted to solve the view selection problem. Accordingly, SPOPSO-based view selection algorithm (SPOPSOVSA) is proposed. SPOPSOVSA selects the Top-K views in a multidimensional lattice framework. Further, the proposed algorithm is shown to perform better than the view selection algorithm HRUA.

scholarly journals Developing an approach for decision making units integration using multi objective particle swarm optimization

2014 ◽  
Vol 4 (1) ◽  
pp. 48 ◽  
Author(s):  
Abdorrahman Haeri ◽  
Kamran Rezaie ◽  
Seyed Morteza Hatefi
Keyword(s):  
Decision Making ◽  
Particle Swarm Optimization ◽  
Performance Measures ◽  
Particle Swarm ◽  
Final Decision ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Decision Making Unit ◽  
The Difference ◽  
Decision Making Units

In recent years, integration between companies, suppliers or organizational departments attracted much attention. Decision making about integration encounters with major concerns. One of these concerns is which units should be integrated and what is the effect of integration on performance measures. In this paper the problem of decision making unit (DMU) integration is considered. It is tried to integrate DMUs so that the considered criteria are satisfied. In this research two criteria are considered that are mean of efficiencies of DMUs and the difference between DMUs that have largest and smallest efficiencies. For this purpose multi objective particle swarm optimization (MOPSO) is applied. A case with 17 DMUs is considered. The results show that integration has increased both considered criteria effectively.  Additionally this approach can presents different alternatives for decision maker (DM) that enables DM to select the final decision for integration.

Materialized View Selection Using Swap Operator Based Particle Swarm Optimization

2021 ◽  
Vol 13 (1) ◽  
pp. 58-73
Author(s):  
Amit Kumar ◽  
T. V. Vijay Kumar
Keyword(s):  
Particle Swarm Optimization ◽  
Data Warehouse ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Selection Problem ◽  
Data Repository ◽  
Swarm Optimization ◽  
View Selection ◽  
Particle Swarm Optimization Technique ◽  
Swap Operator

The data warehouse is a key data repository of any business enterprise that stores enormous historical data meant for answering analytical queries. These queries need to be processed efficiently in order to make efficient and timely decisions. One way to achieve this is by materializing views over a data warehouse. An n-dimensional star schema can be mapped into an n-dimensional lattice from which Top-K views can be selected for materialization. Selection of such Top-K views is an NP-Hard problem. Several metaheuristic algorithms have been used to address this view selection problem. In this paper, a swap operator-based particle swarm optimization technique has been adapted to address such a view selection problem.

A hybrid feature selection algorithm combining ReliefF and Particle swarm optimization for high-dimensional medical data

2021 ◽  
pp. 1-15
Author(s):  
Zhaozhao Xu ◽  
Derong Shen ◽  
Yue Kou ◽  
Tiezheng Nie
Keyword(s):  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Random Forest ◽  
Classification Accuracy ◽  
Particle Swarm ◽  
Medical Data ◽  
High Dimensional ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Swarm Optimization

Due to high-dimensional feature and strong correlation of features, the classification accuracy of medical data is not as good enough as expected. feature selection is a common algorithm to solve this problem, and selects effective features by reducing the dimensionality of high-dimensional data. However, traditional feature selection algorithms have the blindness of threshold setting and the search algorithms are liable to fall into a local optimal solution. Based on it, this paper proposes a hybrid feature selection algorithm combining ReliefF and Particle swarm optimization. The algorithm is mainly divided into three parts: Firstly, the ReliefF is used to calculate the feature weight, and the features are ranked by the weight. Then ranking feature is grouped according to the density equalization, where the density of features in each group is the same. Finally, the Particle Swarm Optimization algorithm is used to search the ranking feature groups, and the feature selection is performed according to a new fitness function. Experimental results show that the random forest has the highest classification accuracy on the features selected. More importantly, it has the least number of features. In addition, experimental results on 2 medical datasets show that the average accuracy of random forest reaches 90.20%, which proves that the hybrid algorithm has a certain application value.

Multi-objective integrated planning and scheduling model for operating rooms under uncertainty

2018 ◽  
Vol 232 (9) ◽  
pp. 930-948 ◽  
Author(s):  
Javad Ansarifar ◽  
Reza Tavakkoli-Moghaddam ◽  
Faezeh Akhavizadegan ◽  
Saman Hassanzadeh Amin
Keyword(s):  
Genetic Algorithm ◽  
Decision Making ◽  
Particle Swarm Optimization ◽  
Heuristic Algorithms ◽  
Particle Swarm ◽  
Operating Rooms ◽  
Programming Approach ◽  
Test Problems ◽  
Swarm Optimization ◽  
Multi Objective

This article formulates the operating rooms considering several constraints of the real world, such as decision-making styles, multiple stages for surgeries, time windows for resources, and specialty and complexity of surgery. Based on planning, surgeries are assigned to the working days. Then, the scheduling part determines the sequence of surgeries per day. Moreover, an integrated fuzzy possibilistic–stochastic mathematical programming approach is applied to consider some sources of uncertainty, simultaneously. Net revenues of operating rooms are maximized through the first objective function. Minimizing a decision-making style inconsistency among human resources and maximizing utilization of operating rooms are considered as the second and third objectives, respectively. Two popular multi-objective meta-heuristic algorithms including Non-dominated Sorting Genetic Algorithm and Multi-Objective Particle Swarm Optimization are utilized for solving the developed model. Moreover, different comparison metrics are applied to compare the two proposed meta-heuristics. Several test problems based on the data obtained from a public hospital located in Iran are used to display the performance of the model. According to the results, Non-dominated Sorting Genetic Algorithm-II outperforms the Multi-Objective Particle Swarm Optimization algorithm in most of the utilized metrics. Moreover, the results indicate that our proposed model is more effective and efficient to schedule and plan surgeries and assign resources than manual scheduling.

A Novel Particle Swarm Optimization Algorithm for Multi-Objective Combinatorial Optimization Problem

2012 ◽  
pp. 1-16
Author(s):  
Rahul Roy ◽  
Satchidananda Dehuri ◽  
Sung Bae Cho
Keyword(s):  
Decision Making ◽  
Particle Swarm Optimization ◽  
Combinatorial Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Discrete Values ◽  
Continuous Domain

The Combinatorial problems are real world decision making problem with discrete and disjunctive choices. When these decision making problems involve more than one conflicting objective and constraint, it turns the polynomial time problem into NP-hard. Thus, the straight forward approaches to solve multi-objective problems would not give an optimal solution. In such case evolutionary based meta-heuristic approaches are found suitable. In this paper, a novel particle swarm optimization based meta-heuristic algorithm is presented to solve multi-objective combinatorial optimization problems. Here a mapping method is considered to convert the binary and discrete values (solution encoded as particles) to a continuous domain and update it using the velocity and position update equation of particle swarm optimization to find new set of solutions in continuous domain and demap it to discrete values. The performance of the algorithm is compared with other evolutionary strategy like SPEA and NSGA-II on pseudo-Boolean discrete problems and multi-objective 0/1 knapsack problem. The experimental results confirmed the better performance of combinatorial particle swarm optimization algorithm.

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