Publication: Digital Quran With Storage Optimization Through Duplication Handling And Compressed Sparse Matrix Method
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Date
2022-11
Authors
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Publisher
Universiti Sains Islam Malaysia
Abstract
This research looks into the topic of the Digital Quran as an application used by many people worldwide and via various devices (e.g. smartphones, tablets, and computers). The current digital Quran text-based format benefitted the Hexadecimal representation. However, space was not optimized due to the string concatenation approach of the Unicode of each letter that occurs in a word. Thus, the storage size for the Unicode representation is directly proportional with the length of a word. In this regard, the current study proposes a new Digital Quran Model (DQM) that aims to reduce storage requirements through word conversion into Hexadecimal instead of combining letter sequences. This approach is further optimized through Quranic word duplication handling. This is paralleled with a compressed sparse matrix representing Quranic verses and surah. The focus of this research is storage optimization for text-based Quran content formats. DQM was implemented using Visual Studio and Java servers, where the solution quality was measured by comparing the file size before and after applying the DQM model. For surah Al-Baqarah, the reduction in the storage size was 50.76%, and Al-Fatihah was 69.81%. The results of this research are consistent with other studies in the same context and provide implications in both theoretical and practical domains. In addition to space reduction analysis, the current research interviews domain experts on the proposed digital Quran applications. The outcome helps the researchers to develop a Digital Quran that users can benefit from due to its reliability, validity, and optimized storage so that it can be used as a standard application.
Description
Matric: 4120083 (FST)
Keywords
Digital Quran, Qurʼan -- Online algorithms.
Citation
Ashraf Saleh Mohammad Alomoush. (2022). Digital Quran with Storage Optimization Through Duplication Handling and Compressed Sparse Matrix Method [Doctoral dissertation, Universiti Sains Islam Malaysia]. USIM Research Repository.