A Data Allocation Strategy for Geocomputation Based on Shape Complexity in A Cloud Environment Using Parallel Overlay Analysis of Polygons as an Example
| Autor: | Baoxuan Jin, Hong Fan, Mei Yang, Kang Zhao |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Theoretical computer science
Geospatial analysis General Computer Science Computer science data skew 0211 other engineering and technologies Cloud computing 02 engineering and technology computer.software_genre Data type Big data 0202 electrical engineering electronic engineering information engineering General Materials Science Complex polygon Spatial analysis 021101 geological & geomatics engineering business.industry parallel computing General Engineering 020207 software engineering overlay analysis shape complexity Parallel processing (DSP implementation) Polygon lcsh:Electrical engineering. Electronics. Nuclear engineering business computer lcsh:TK1-9971 |
| Zdroj: | IEEE Access, Vol 8, Pp 185981-185991 (2020) |
| ISSN: | 2169-3536 |
| Popis: | Given the explosive growth of geospatial data, parallel computing technologies have become widely used in the spatial analysis of these massive types of data. The data used in geographic computing often exhibit a complex graphic structure, which is an important cause of data skew in parallel computing. The shape complexity is crucial to the task allocation strategy of parallel computing. The effect of polygon shape features on the performance of spatial analysis was investigated in this study. A quantitative polygon-shaped complexity evaluation model was established through regression analysis. The Hilbert data partition strategy weighted by shape complexity was used as a spatial data allocation method for parallel spatial analysis. This study established a shape complexity evaluation model for overlay analysis and used the Spark parallel computing paradigm to carry out a comparative experiment of a massive, complex polygon. Experimental results showed that the spatial data allocation strategy based on the complexity of polygon shape computing effectively solved the problem of data skew in the parallel spatial analysis of massive complex polygons. |
| Databáze: | OpenAIRE |
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