Abstract
SpatialHadoop is an extended MapReduce framework that supports global indexing that spatial partitions the data across machines providing orders of magnitude speedup, compared to traditional Hadoop. In this paper, we describe seven alternative partitioning techniques and experimentally study their effect on the quality of the generated index and the performance of range and spatial join queries. We found that using a 1% sample is enough to produce high quality partitions. Also, we found that the total area of partitions is a reasonable measure of the quality of indexes when running spatial join. This study will assist researchers in choosing a good spatial partitioning technique in distributed environments.
| Original language | English (US) |
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| Title of host publication | Proceedings of the VLDB Endowment |
| Editors | Christophe Claramunt, Simonas Saltenis, Ki-Joune Li |
| Publisher | Association for Computing Machinery |
| Pages | 1602-1605 |
| Number of pages | 4 |
| Volume | 8 |
| Edition | 12 12 |
| DOIs | |
| State | Published - 2015 |
| Event | 3rd Workshop on Spatio-Temporal Database Management, STDBM 2006, Co-located with the 32nd International Conference on Very Large Data Bases, VLDB 2006 - Seoul, Korea, Republic of Duration: Sep 11 2006 → Sep 11 2006 |
Other
| Other | 3rd Workshop on Spatio-Temporal Database Management, STDBM 2006, Co-located with the 32nd International Conference on Very Large Data Bases, VLDB 2006 |
|---|---|
| Country/Territory | Korea, Republic of |
| City | Seoul |
| Period | 9/11/06 → 9/11/06 |