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What Makes Spatial Data Big? A Discussion on How to Partition Spatial Data
2018
International Conference Geographic Information Science
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
doi:10.4230/lipics.giscience.2018.2
dblp:conf/giscience/BelussiCMNP18
fatcat:6hpoqzyxojetldkel65yk3yrsu