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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Duangkaew, Jirawat
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Computer Science & IT Engineering

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Enhancing data retrieval efficiency in large-scale javascript object notation datasets by using indexing techniques Srisungsittisunti, Bowonsak; Duangkaew, Jirawat; Mekruksavanich, Sakorn; Chaikaew, Nakarin; Rojanavasu, Pornthep
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp2342-2353

Abstract

The use of javascript object notation (JSON) format as a not only structured query language (NoSQL) storage solution has grown in popularity, but has presented technical challenges, particularly in indexing large-scale JSON files. This has resulted in slow data retrieval, especially for larger datasets. In this study, we propose the use of JSON datasets to preserve data in resource survey processes. We conducted experiments on a 32-gigabyte dataset containing 1,000,000 transactions in JSON format and implemented two indexing methods, dense and sparse, to improve retrieval efficiency. Additionally, we determined the optimal range of segment sizes for the indexing methods. Our findings revealed that adopting dense indexing reduced data retrieval time from 15,635 milliseconds to 55 milliseconds in one-to-one data retrieval, and from 38,300 milliseconds to 1 millisecond in the absence of keywords. In contrast, using sparse indexing reduced data retrieval time from 33,726 milliseconds to 36 milliseconds in one-to-many data retrieval and from 47,203 milliseconds to 0.17 milliseconds when keywords were not found. Furthermore, we discovered that the optimal segment size range was between 20,000 and 200,000 transactions for both dense and sparse indexing.
Evaluating search key distribution impact on searching performance in large data streams Srisungsittisunti, Bowonsak; Duangkaew, Jirawat; Chaikaew, Nakarin
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i3.pp2537-2546

Abstract

The distribution pattern of search keys is assessed in this study by contrasting four methods of index searching on large-scale JSON files with data streams. The Adelson-Velskii and Landis (AVL) tree, binary search tree (BST), linear search (LS), and binary search (BS) are among the search strategies. We look at the normal distribution, left-skewed distribution, and right-skewed distribution of search-key distributions. According to the results, LS performs the slowest, averaging 653.166 milliseconds, whereas AVL tree performs better than the others in dense index, with an average search time of 0.005 milliseconds. With 0.011 milliseconds per keyword for sparse index, BS outperforms LS, which averages 1007.848 milliseconds. For dense indexing, an AVL tree works best; for sparse indexing, BS is recommended.
Co-Authors Chaikaew, Nakarin Mekruksavanich, Sakorn Rojanavasu, Pornthep Srisungsittisunti, Bowonsak