A Survey of Various Query Optimization Techniques

John, Rini; Palaskar, Nikita

doi:10.5120/ijca2017915286

Cited by 3 publications

(3 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Dalam sistem penyimpanan data yang besar, penting untuk mengoptimalkan kerja queri agar sistem dapat digunakan secara optimal dan waktu pemrosesan dapat diminimalkan. (Thangam & Peter, 2016). Optimasi query adalah proses menentukan strategi eksekusi yang optimal untuk pemrosesan query (Elmasri, R. and Navathe, 2011).…”

Section: Pendahuluanunclassified

Perbandingan Efisiensi Waktu Proses Pengaksesan Data Antara Query Berbentuk Join Dengan Subselect

Sumantri

Subari

Mahardika

et al. 2023

jmika

View full text Add to dashboard Cite

Queries in a database application system, have a great influence on the success of application development. Frequently used query forms include subselect and join. Choosing the right query form will affect the performance of the database application. Database applications are sometimes slow to respond because of slowness in their databases. The selection of the form of the query to be used is the first step of forming a query, because the resulting query will affect access from the database. The purpose of this study is to determine the form of SQL with a faster data access time, whether join or subselect. The subject of this study is queries made in the form of joins and subselects. The methods that will be used in this study are literature and experimental methods. The result of this study is a comparison table of access time between join and subselect queries. From the results of the query comparison, it can be concluded that the join form query is a faster SQL form compared to subselect. A faster query is determined from testing done by creating a simple application using both forms of the query.

show abstract

Section: Pendahuluanunclassified

Perbandingan Efisiensi Waktu Proses Pengaksesan Data Antara Query Berbentuk Join Dengan Subselect

Sumantri

Subari

Mahardika

et al. 2023

jmika

View full text Add to dashboard Cite

show abstract

“…Big statistics units are also saved the use of the Hadoop dispensed document system [6]. Framework for Big Data categorization and Pre-processing [7] In view that Google first announced MR in 2004, it has grown to be a properly-liked framework for managing big datasets. Primarily based at the MR paradigm, the modernday open-source Hadoop challenge gives the parallel processing of widespread volumes of records, automatic facts splitting, records distribution, fault tolerance, & load balancing control, main to dependable in parallel throughout a dispensed network of dozens to heaps of commodity machines via breaking them up into smaller, greater conceivable chunks.…”

Section: Introductionmentioning

confidence: 99%

“…Figure 1. Traditional Block Diagram of Query Optimization basedFramework for Big Data categorization and Pre-processing[7] …”

mentioning

confidence: 99%

An Enhanced Query Optimization Implemented in Hadoop using Bio-Inspired Algorithm with HDFS Technique

Abhijit Banubakode

2023

IJRITCC

View full text Add to dashboard Cite

A more effective method for massive data query optimization using HDFS and the Bio-inspired algorithm. Big Data configuration and query optimization are the two phases of the process. To remove redundant data, the input data is first per-processed using HDFS. Then, utilizing entropy calculation, features like closed frequent pattern, support, and confidence are extracted and managed. The Bio-inspired Horse Herd approach is used to group pertinent information based on this outcome. In the second step, the Big Data queries are used to obtain the same features. The optimized query is then located using the Bio-inspired technique, and the similarity assessment procedure is run. The proposed algorithm, according to this research, outperforms other ones that is unique in use. It is challenging to determine the veracity of this claim without more information regarding the experimental setup and the precise measures employed to assess the algorithm's effectiveness. Furthermore, it is unknown how the proposed algorithm stacks up against other cutting-edge query optimization methods. Finally, the assess has efficiency of using this method, more optimistic query achieved and comparison analysis are proved.

show abstract