SbMBR Tree—A Spatiotemporal Data Indexing and Compression Algorithm for Data Analysis and Mining

In the field of data analysis and mining, adopting efficient data indexing and compression techniques to spatiotemporal data can significantly reduce computational and storage overhead for the abilities to control the volume of data and exploit the spatiotemporal characteristics. However, traditiona...

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Vydáno v:Applied sciences Ročník 13; číslo 19; s. 10562
Hlavní autoři: Guan, Runda, Wang, Ziyu, Pan, Xiaokang, Zhu, Rongjie, Song, Biao, Zhang, Xinchang
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.10.2023
Témata:
Algorithms
clustering
Cost control
Data analysis
Data compression
data indexing
Efficiency
lossy compression
Methods
Oceanic analysis
Queries
Spatial data
spatiotemporal data
ISSN:2076-3417, 2076-3417
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Shrnutí:In the field of data analysis and mining, adopting efficient data indexing and compression techniques to spatiotemporal data can significantly reduce computational and storage overhead for the abilities to control the volume of data and exploit the spatiotemporal characteristics. However, traditional lossy compression techniques are hardly suitable due to their inherently random nature. They often impose unpredictable damage to scientific data, which affects the results of data mining and analysis tasks that require certain precision. In this paper, we propose a similarity-based minimum bounding rectangle (SbMBR) tree, a tree-based indexing and compression method, to address the aforementioned problem. Our method can hierarchically select appropriate minimum bounding rectangles according to the given maximum acceptable errors and use the average value contained in each selected MBR to replace the original data to achieve data compression with multi-layer loss control. This paper also provides the corresponding tree construction algorithm and range query processing algorithm for the indexing structure mentioned above. To evaluate the data quality preservation in cross-domain data analysis and mining scenarios, we use mutual information as the estimation metric. Experimental results emphasize the superiority of our method over some of the typical indexing and compression algorithms.
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ISSN:2076-3417
2076-3417
DOI:10.3390/app131910562