Multi-Dimensional Data Compression and Query Processing in Array Databases
In recent times, the production of multidimensional data in various domains and their storage in array databases has witnessed a sharp increase; this rapid growth in data volumes necessitates compression in array databases. However, existing compression schemes used in array databases are general-pu...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9923935/ |
| _version_ | 1828164615564951552 |
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| author | Minsoo Kim Hyubjin Lee Yon Dohn Chung |
| author_facet | Minsoo Kim Hyubjin Lee Yon Dohn Chung |
| author_sort | Minsoo Kim |
| collection | DOAJ |
| description | In recent times, the production of multidimensional data in various domains and their storage in array databases has witnessed a sharp increase; this rapid growth in data volumes necessitates compression in array databases. However, existing compression schemes used in array databases are general-purpose and not designed specifically for the databases. They could degrade query performance with complex analytical tasks, which incur huge computing costs. Thus, a compression scheme that considers the workflow of array databases is required. This study presents a compression scheme, SEACOW, for storing and querying multidimensional array data. The scheme is specially designed to be efficient for both dimension-based and value-based exploration. It considers data access patterns for exploration queries and embeds a synopsis, which can be utilized as an index, in the compressed array. In addition, we implement an array storage system, namely MSDB, to perform experiments. We evaluate query performance on real scientific datasets and compared it with those of existing compression schemes. Finally, our experiments demonstrate that SEACOW provides high compression rates compared to existing compression schemes, and the synopsis improves analytical query processing performance. |
| first_indexed | 2024-04-12T01:29:21Z |
| format | Article |
| id | doaj.art-38c323138c424b2394e5272e04b8c861 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-12T01:29:21Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-38c323138c424b2394e5272e04b8c8612022-12-22T03:53:32ZengIEEEIEEE Access2169-35362022-01-011011152811154410.1109/ACCESS.2022.32155259923935Multi-Dimensional Data Compression and Query Processing in Array DatabasesMinsoo Kim0https://orcid.org/0000-0003-3450-9721Hyubjin Lee1https://orcid.org/0000-0003-0046-7316Yon Dohn Chung2https://orcid.org/0000-0003-2070-5123Department of Computer Science and Engineering, Korea University, Seongbuk-gu, Seoul, Republic of KoreaDepartment of Computer Science and Engineering, Korea University, Seongbuk-gu, Seoul, Republic of KoreaDepartment of Computer Science and Engineering, Korea University, Seongbuk-gu, Seoul, Republic of KoreaIn recent times, the production of multidimensional data in various domains and their storage in array databases has witnessed a sharp increase; this rapid growth in data volumes necessitates compression in array databases. However, existing compression schemes used in array databases are general-purpose and not designed specifically for the databases. They could degrade query performance with complex analytical tasks, which incur huge computing costs. Thus, a compression scheme that considers the workflow of array databases is required. This study presents a compression scheme, SEACOW, for storing and querying multidimensional array data. The scheme is specially designed to be efficient for both dimension-based and value-based exploration. It considers data access patterns for exploration queries and embeds a synopsis, which can be utilized as an index, in the compressed array. In addition, we implement an array storage system, namely MSDB, to perform experiments. We evaluate query performance on real scientific datasets and compared it with those of existing compression schemes. Finally, our experiments demonstrate that SEACOW provides high compression rates compared to existing compression schemes, and the synopsis improves analytical query processing performance.https://ieeexplore.ieee.org/document/9923935/Arraysdata compressiondata structuresdatabase systemsdiscrete wavelet transformsHuffman coding |
| spellingShingle | Minsoo Kim Hyubjin Lee Yon Dohn Chung Multi-Dimensional Data Compression and Query Processing in Array Databases IEEE Access Arrays data compression data structures database systems discrete wavelet transforms Huffman coding |
| title | Multi-Dimensional Data Compression and Query Processing in Array Databases |
| title_full | Multi-Dimensional Data Compression and Query Processing in Array Databases |
| title_fullStr | Multi-Dimensional Data Compression and Query Processing in Array Databases |
| title_full_unstemmed | Multi-Dimensional Data Compression and Query Processing in Array Databases |
| title_short | Multi-Dimensional Data Compression and Query Processing in Array Databases |
| title_sort | multi dimensional data compression and query processing in array databases |
| topic | Arrays data compression data structures database systems discrete wavelet transforms Huffman coding |
| url | https://ieeexplore.ieee.org/document/9923935/ |
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