Java Script Data Transformation Library using Fork Join Pool and Web Workers Technology
Drevendy Harianto1, Seng Hansun2, Andre Rusli3
1Drevendy Harianto, Department of Informatics, University, Multimedia Nusantara, Tangerang, Indonesia.
2Seng Hansun, Department of Informatics, University, Multimedia Nusantara, Tangerang, Indonesia.
3Andre Rusli, Department of Informatics, University, Multimedia Nusantara, Tangerang, Indonesia.
Manuscript received on November 25, 2019. | Revised Manuscript received on December 15, 2019. | Manuscript published on December 30, 2019. | PP: 1609-1614 | Volume-9 Issue-2, December, 2019. | Retrieval Number: B2954129219/2019©BEIESP | DOI: 10.35940/ijeat.B2954.129219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Transforming large amounts of data takes a lot of processing time so that the optimization technique is required. One way that can be used to perform optimization is multithreading. Nowadays, processor is proliferating. The average processor in community is multi-core processor that can do parallel processing. Prior to the emergence of Web Workers, JavaScript is a poor programming language for parallel programming. The emergence of Web Workers allows JavaScript to do a better job in parallel programming. Fork Join Pool is a method that implements the Divide and Conquers algorithm, so it is suitable for the use in multithreading. This data transformation library was created by implementing the Fork Join Pool method using Web Workers technology in JavaScript. This program is written in JavaScript and HTML language. Based on results of testing phase that has been done, it is proven that For k Join Pool method can be implemented using Web Workers technology in JavaScript as a data transformation library. In addition, it can be concluded that the data transformation library usage affects the speed of data transformation which depends on the data transformation complexity. The higher the complexity of data transformation performed, the effectiveness in the use of data transformation libraries will increase.
Keywords: Fork Join Pool, JavaScript, Data Transformation Library, Multithreading, Web Workers.