Genetic Algorithm for Relational Database Optimization in Reducing Query Execution Time

The relational database is defined as the database by connecting between tables. Each table has a collection of information. The information is processed in the database by using queries, such as data retrieval, data storage, and data conversion. If the information in the table or data has a large size, then the query process to process the database becomes slow. In this paper, Genetic Algorithm is used to process queries in order to optimize and reduce query execution time. The results obtained are query execution with genetic algorithm optimization to show the best execution time. The genetic algorithm processes the query by changing the structure of the relation and rearranging it. The fitness value generated from the genetic algorithm becomes the best solution. The fitness used is the highest fitness of each experiment results. In this experiment, the database used is MySQL sample database which is named as employees. The database has a total of over 3,000,000 rows in 6 tables. Queries are designed by using 5 relations in the form of a left deep tree. The execution time of the query is 8.14247 seconds and the execution time after the optimization of the genetic algorithm is 6.08535 seconds with the fitness value of 0.90509. The time generated after optimization of the genetic algorithm is reduced by 25.3%. It shows that genetic algorithm can reduce query execution time by optimizing query in the part of relation. Therefore, query optimization with genetic algorithm can be an alternative solution and can be used to maximize query performance.

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Schema-Based Mapping Approach for Data Transformation to Enrich Semantic Web

Wireless Communications and Mobile Computing ◽

10.1155/2021/8567894 ◽

2021 ◽

Vol 2021 ◽

pp. 1-15

Author(s):

Senthilselvan Natarajan ◽

Subramaniyaswamy Vairavasundaram ◽

Yuvaraja Teekaraman ◽

Ramya Kuppusamy ◽

Arun Radhakrishnan

Keyword(s):

Relational Database ◽

Resource Description Framework ◽

Data Retrieval ◽

Mapping Method ◽

Data Conversion ◽

Query Translation ◽

On Demand ◽

Conventional Methods ◽

Description Framework ◽

Resource Description

Modern web wants the data to be in Resource Description Framework (RDF) format, a machine-readable form that is easy to share and reuse data without human intervention. However, most of the information is still available in relational form. The existing conventional methods transform the data from RDB to RDF using instance-level mapping, which has not yielded the expected results because of poor mapping. Hence, in this paper, a novel schema-based RDB-RDF mapping method (relational database to Resource Description Framework) is proposed, which is an improvised version for transforming the relational database into the Resource Description Framework. It provides both data materialization and on-demand mapping. RDB-RDF reduces the data retrieval time for nonprimary key search by using schema-level mapping. The resultant mapped RDF graph presents the relational database in a conceptual schema and maintains the instance triples as data graph. This mechanism is known as data materialization, which suits well for the static dataset. To get the data in a dynamic environment, query translation (on-demand mapping) is best instead of whole data conversion. The proposed approach directly converts the SPARQL query into SQL query using the mapping descriptions available in the proposed system. The mapping description is the key component of this proposed system which is responsible for quick data retrieval and query translation. Join expression introduced in the proposed RDB-RDF mapping method efficiently handles all complex operations with primary and foreign keys. Experimental evaluation is done on the graphics designer database. It is observed from the result that the proposed schema-based RDB-RDF mapping method accomplishes more comprehensible mapping than conventional methods by dissolving structural and operational differences.

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A Method of Geospatial Data Files Conversion Based on Semantic

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.303-306.2221 ◽

2013 ◽

Vol 303-306 ◽

pp. 2221-2226

Author(s):

Ming Xiang He ◽

Guan Li ◽

Xin Ming Lu

Keyword(s):

Data Management ◽

Data Storage ◽

Relational Database ◽

Geospatial Data ◽

Management Mode ◽

Data Conversion ◽

Data Files ◽

Database File

Data conversion is a necessary step to establish a unified data storage and management mode. Based on the analysis of the structure of Shapefile and the principle of semantic conversion, this paper proposes a method of geospatial data files conversion based on semantic. This method to a relational database file shape file conversion, the application of this method can not only increase the data read speed, and ease of data management and sharing.

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Application of the Agent-Based Ontology Matching Technology in the Relational Database

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.846-847.1701 ◽

2013 ◽

Vol 846-847 ◽

pp. 1701-1706

Author(s):

Jian Long Ding

Keyword(s):

Data Storage ◽

Relational Database ◽

Data Retrieval ◽

Domain Ontology ◽

Ontology Matching ◽

Semantic Matching ◽

Semantic Retrieval ◽

Matching Problem ◽

Specific Level ◽

Quantitative Calculation

As the relational database be the main data storage mode, but the traditional keyword based syntactic matching defects in precision and recall. This paper provides a ontology matching mechanisms for relational database, which realizes the semantic level of the data retrieval by using the intelligent search technology from Agent and Ontology. The mechanism using DCL domain ontology, SQC services query policy, SearchPolicyMap mapping to solve the problems as data object description, retrieval conditions description, and the mapping between those two description. And provided the preconditions for the semantic matching under the relational database. Solve the semantic matching problem of interaction between human and Agent by the WI interface and CM mechanism . Solve the problem of interaction between Agent and relational database by the service customization Interface SBI. Finally, solve the problem of semantic retrieval and quantitative calculation by querying adapter QA and core algorithm CMA. The mechanism has a strong practicality and application domains independent. Can implement a specific level semantics of relational database retrieval through the application domain ontology creation and mapping configuration.

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SISTEM PENJADWALAN MATA PELAJARAN MENGGUNAKAN ALGORITMA GENETIKA

JURNAL TEKNOLOGI INFORMASI ◽

10.36294/jurti.v3i1.687 ◽

2019 ◽

Vol 3 (1) ◽

pp. 49

Author(s):

Yesri Elva

Keyword(s):

Genetic Algorithm ◽

Genetic Algorithms ◽

Problem Solving ◽

Optimization Algorithm ◽

Learning Process ◽

Optimal Solution ◽

Complex Problem ◽

Complex Problem Solving ◽

Algorithm Optimization ◽

Fitness Value

Abstract - Schedule is one important factor to support the learning process, one of which at SMKN 3 Pariaman. In SMKN 3 Pariaman scheduling process is still done manually, consequently there are conflicting schedules and timing of learning becomes too late. One of completion method to the problem is to use a genetic algorithm, because it is one of the Genetic Algorithm optimization algorithm that is robust and can be used on a wide variety of case studies such as scheduling. This algorithm is also often used to find the optimal solution both in the case of simple to complex problem-solving technique that determines the start and initialization pupulasi chromosomes, determine the value of fitness, selection, crossover, mutation. Mutations done to produce the best fitness value which can be used to determine the final outcome scheduling. If the best fitness values have been obtained, the process is stopped and reach the finish condition.Keywords - Genetic Algorithms, Scheduling Abstrak - Jadwal merupakan salah satu faktor penting untuk penunjang proses belajar mengajar, salah satunya pada SMKN 3 Pariaman. Pada SMKN 3 Pariaman proses penyusunan jadwal masih dilakukan secara manual, akibatnya masih terdapat jadwal yang bentrok dan waktu pelaksanaan belajar mengajar menjadi terlambat. Salah satu metode untuk penyelesain masalah tersebut adalah dengan menggunakan algoritma genetika, karena Algoritma Genetika merupakan salah satu algoritma optimasi yang kuat dan bisa digunakan pada berbagai macam studi kasus seperti penjadwalan. Algoritma ini juga sering digunakan untuk mencari solusi optimal baik pada kasus yang sederhana sampai yang rumit teknik pemecahan masalahnya yaitu menentukan pupulasi awal dan inisialisasi kromosom, menentukan nilai fitness, seleksi crossover, mutasi. Mutasi dilakukan sampai menghasilkan nilai fitness terbaik yang dapat digunakan untuk penentuan hasil akhir penyusunan jadwal. Jika nilai fitness terbaik sudah didapatkan maka proses dihentikan dan mencapai kondisi selesai.Kata kunci - Algoritma Genetika, Penjadwalan

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A Multi-Population Genetic Algorithm for Adaptive Qos-Aware Service Composition in Fog-Iot Healthcare Environment

The International Arab Journal of Information Technology ◽

10.34028/iajit/18/3a/10 ◽

2021 ◽

Author(s):

Idir Aoudia ◽

Saber Benharzallah ◽

Laid Kahloul ◽

Okba Kazar

Keyword(s):

Genetic Algorithm ◽

Service Composition ◽

Execution Time ◽

Population Genetic ◽

Large Scale ◽

Dynamic Change ◽

Healthcare Environment ◽

Fitness Value ◽

Population Genetic Algorithm ◽

Adaptive Qos

The growth of Internet of thing (IoT) implies the availability of a very large number of services which may be similar or the same, managing the Quality of Service (QoS) helps to differentiate one service from another.The service composition provides the ability to perform complex activities by combining the functionality of several services within a single process. Very few works have presented an adaptive service composition solution managing QoS attributes, moreover in the field of healthcare, which is one of the most difficult and delicate as it concerns the precious human life.In this paper, we will present an adaptive QoS-Aware Service Composition Approach (P-MPGA) based on multi-population genetic algorithm in Fog-IoT healthcare environment. To enhance Cloud-IoT architecture, we introduce a Fog-IoT 5-layared architecture. Secondly, we implement a QoS-Aware Multi-Population Genetic Algorithm (P-MPGA), we considered 12 QoS dimensions, i.e., Availability (A), Cost (C), Documentation (D), Location (L), Memory Resources (M), Precision (P), Reliability (R), Response time (Rt), Reputation (Rp), Security (S), Service Classification (Sc), Success rate (Sr), Throughput (T). Our P-MPGA algorithm implements a smart selection method which allows us to select the right service. Also, P-MPGA implements a monitoring system that monitors services to manage dynamic change of IoT environments. Experimental results show the excellent results of P-MPGA in terms of execution time, average fitness values and execution time / best fitness value ratio despite the increase in population. P-MPGA can quickly achieve a composite service satisfying user’s QoS needs, which makes it suitable for a large scale IoT environment.

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