Optimal Data-Space Partitioning of Spatial Data for Parallel I/O (original) (raw)
2005, Distributed and Parallel Databases
Related papers
What Makes Spatial Data Big? A Discussion on How to Partition Spatial Data
2018
The amount of available spatial data has significantly increased in the last years so that traditional analysis tools have become inappropriate to effectively manage them. Therefore, many attempts have been made in order to define extensions of existing MapReduce tools, such as Hadoop or Spark, with spatial capabilities in terms of data types and algorithms. Such extensions are mainly based on the partitioning techniques implemented for textual data where the dimension is given in terms of the number of occupied bytes. However, spatial data are characterized by other features which describe their dimension, such as the number of vertices or the MBR size of geometries, which greatly affect the performance of operations, like the spatial join, during data analysis. The result is that the use of traditional partitioning techniques prevents to completely exploit the benefit of the parallel execution provided by a MapReduce environment. This paper extensively analyses the problem conside...
Loading Preview
Sorry, preview is currently unavailable. You can download the paper by clicking the button above.