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...

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Vydáno v:IEEE access Ročník 10; s. 1
Hlavní autoři: Kim, Minsoo, Lee, Hyubjin, Chung, Yon Dohn
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Arrays
Cost analysis
Data compression
Data structures
Database systems
Discrete wavelet transforms
Huffman coding
Image coding
Indexes
Low-pass filters
Multidimensional data
Performance degradation
Performance evaluation
Queries
Query processing
Scientific computing
Storage
Task analysis
Task complexity
Tree data structures
Workflow
ISSN:2169-3536, 2169-3536
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Abstract 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.
AbstractList 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.
Author Chung, Yon Dohn
Lee, Hyubjin
Kim, Minsoo
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SubjectTerms Arrays
Cost analysis
Data compression
Data structures
Database systems
Discrete wavelet transforms
Huffman coding
Image coding
Indexes
Low-pass filters
Multidimensional data
Performance degradation
Performance evaluation
Queries
Query processing
Scientific computing
Storage
Task analysis
Task complexity
Tree data structures
Workflow
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Title Multi-dimensional Data Compression and Query Processing in Array Databases
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