.png){kind=icon}
.png){kind=icon}
Return
Data storage optimization strategy in distributed column-oriented database by considering spatial adjacency
.png){kind=icon}
PRE
AI
DOI：10.1007/s10586-017-1081-3.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Scan operation will involve many fragments and cause many extra invalid partitioning query operations in distributed column-oriented database which affects query efficiency seriously, especially for spatial data. To solve this question, this paper refers to partitioning strategy in distributed column-oriented database and advocates a spatial data storage optimization strategy named 'SPPS'. This strategy makes adjacent spatial objects stored in the same data fragment with considering spatial adjacency, and reserves the spatial information of each fragment. Thus spatial query operation can locate the relevant fragment on basis of spatial information of fragment, and extra invalid partitioning scan operations would be lighted. Then the storage and query efficiency would be improved. To verify the validity of 'SPPS' optimization strategy, this paper carries on relevant experiments based on HBase and records spatial query efficiency with and without 'SPPS' respectively. The experiments results indicate that 'SPPS' strategy can optimize the storage and query efficiency in distributed column-oriented databases.
Keywords:
Partitioning
Spatial data
Spatial adjacency
Distributed column-oriented database
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
C
IF:
4.1
Papers:
5.0K
Citations:
7.5K