Materialized view selection using discrete genetic operators based particle swarm optimization

2017 ◽  
Author(s):  
Amit Kumar ◽  
T. V. Vijay Kumar
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Genetic Operators ◽  
Swarm Optimization ◽  
View Selection ◽  
Materialized View ◽  
Materialized View Selection

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 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.

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.

Part-of-Speech Tagging for Arabic Text using Particle Swarm Optimization and Genetic Algorithm

2021 ◽  
Vol 14 ◽  
Author(s):  
Ahmad T. Al-Taani ◽  
Fadi A. ALkhazaaleh
Keyword(s):  
Particle Swarm Optimization ◽  
Language Processing ◽  
Question Answering ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Arabic Language ◽  
Genetic Operators ◽  
Swarm Optimization ◽  
Pos Tagging ◽  
Part Of Speech

Background: Part of Speech (POS) Tagging is a process of defining the suitable part of speech for each word in the given context such as defining if a word is a verb, a noun or a particle. POS tagging is an important preprocessing step in many Natural Language Processing (NLP) applications such as question answering, text summarization, and information retrieval. Objective: The performance of NLP applications depends on the accuracy of POS taggers since assigning right tags for the words in a sentence enables the application to work properly after tagging. Many approaches have been proposed for the Arabic language, but more investigations are needed to improve the efficiency of Arabic POS taggers. Method: In this study, we propose a supervised POS tagging system for the Arabic language using Particle Swarm Optimization (PSO) and Genetic Algorithms (GA) as well as Hidden Markov Model (HMM). The tagging process is considered as an optimization problem and illustrated as a swarm which consists of group of particles. Each particle represents sequence of tags. The PSO algorithm is applied to find the best sequence of tags which represent the correct tags of the sentence. The genetic operators: crossover and mutation are used to find personal best, global best, and velocity of the PSO algorithm. HMM is used to find the fitness of particles in the swarm. Results : The performance of the proposed approach is evaluated on the KALIMAT dataset which consists of 18 million words and a tag set consists of 45 tags which covers all Arabic POS tags. The proposed tagger achieved an accuracy of 90.5%. Conclusion: Experimental results revealed that the proposed tagger achieved promising results compared to four existing approaches. Other approaches can identify only three tags: noun, verb and particle. Also, the accuracy for some tags are outperformed those achieved by other approaches.

Robust PID Parameters Optimization Design Based on Improved Particle Swarm Optimization

2013 ◽  
Vol 373-375 ◽  
pp. 1125-1130
Author(s):  
Jin Wei Fan ◽  
Qin Mei ◽  
Xiao Feng Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Design ◽  
Particle Swarm ◽  
Parameters Optimization ◽  
Grinding Wheel ◽  
Genetic Operators ◽  
Swarm Optimization ◽  
Local Optima ◽  
Domain Performance ◽  
Crossover And Mutation

The article, based on satisfying robustness of the system and put forward the objective function of time-domain performance and dynamic characteristics, introduced genetic operators into Particle Swarm Optimization. The algorithm improve the diversity of particles by selection and hybridization operations and strengthen the excellent characteristics of particles in the swarm by introducing crossover and mutation genes, which can avoid bog down into local optima and premature convergence and enhance searching efficiency. The simulation results indicate that when the algorithm is applied to the optimization of PID controller parameters of servo system of grinding wheel rack of MKS8332A CNC camshaft grinder, its performance is better than the single Genetic Algorithms or Particle Swarm Optimization, and it can also satisfy the demand of rapidity, stability and robustness.

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.

scholarly journals A Novel Particle Swarm Optimization with Genetic Operator and Its Application to TSP

2021 ◽  
Vol 15 (4) ◽  
pp. 0-0
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Population Diversity ◽  
Benchmark Problems ◽  
Genetic Operators ◽  
Swarm Optimization ◽  
Genetic Operator ◽  
Hybrid Particle Swarm Optimization ◽  
The Traveling Salesman Problem

To solve some problems of particle swarm optimization, such as the premature convergence and falling into a sub-optimal solution easily, we introduce the probability initialization strategy and genetic operator into the particle swarm optimization algorithm. Based on the hybrid strategies, we propose a improved hybrid particle swarm optimization, namely IHPSO, for solving the traveling salesman problem. In the IHPSO algorithm, the probability strategy is utilized into population initialization. It can save much more computing resources during the iteration procedure of the algorithm. Furthermore, genetic operators, including two kinds of crossover operator and a directional mutation operator, are used for improving the algorithm’s convergence accuracy and population diversity. At last, the proposed method is benchmarked on 9 benchmark problems in TSPLIB and the results are compared with 4 competitors. From the results, it is observed that the proposed approach significantly outperforms others on most the 9 datasets.

scholarly journals A New Auto Adaptive Fuzzy Hybrid Particle Swarm Optimization and Genetic Algorithm

2020 ◽  
Vol 10 (2) ◽  
pp. 95-111 ◽  
Author(s):  
Piotr Dziwiński ◽  
Łukasz Bartczuk ◽  
Józef Paszkowski
Keyword(s):  
Particle Swarm Optimization ◽  
Fitness Landscape ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Fuzzy Rules ◽  
Local Optimum ◽  
Genetic Operators ◽  
Adaptation Mechanism ◽  
Swarm Optimization ◽  
Hybrid Particle Swarm Optimization

AbstractThe social learning mechanism used in the Particle Swarm Optimization algorithm allows this method to converge quickly. However, it can lead to catching the swarm in the local optimum. The solution to this issue may be the use of genetic operators whose random nature allows them to leave this point. The degree of use of these operators can be controlled using a neuro-fuzzy system. Previous studies have shown that the form of fuzzy rules should be adapted to the fitness landscape of the problem. This may suggest that in the case of complex optimization problems, the use of different systems at different stages of the algorithm will allow to achieve better results. In this paper, we introduce an auto adaptation mechanism that allows to change the form of fuzzy rules when solving the optimization problem. The proposed mechanism has been tested on benchmark functions widely adapted in the literature. The results verify the effectiveness and efficiency of this solution.

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.

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