Comparative Evaluation of Various Indexing Techniques of Geospatial Vector Data for Processing in Distributed Computing Environment

Abdul, Jhummarwala; Alkathiri, Mazin; Karamta, Miren; Potdar, M. B.

doi:10.1145/2998476.2998493

Cited by 1 publication

(3 citation statements)

References 11 publications

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“…Figure 2 also shows that the R-tree in JSI consumes very little memory even though it stores rectangles. JSI heavily relies on trove4j 19 collections, which are generally faster to access, and consumes much less memory than Java's Util collections. There are two reasons for low memory consumption.…”

Section: Indexing Costsmentioning

confidence: 99%

“…To the best of our knowledge, no previous work in literature has evaluated the spatial libraries studied here empirically. One research work [19] (1) Best suited for geographic data (2) Active development and support (3) Many practical queries natively supported techniques for big spatial data, where the authors consider many big spatial data systems and one spatial library JSI, only to report the performance of each system/library on a standalone basis. The authors in [71] implement spatial query processing in Apache Spark, and Apache Impala using JTS and GEOS, respectively.…”

Section: Related Workmentioning

confidence: 99%

“…CPU: https ://ark.intel .com/conte nt/www/us/en/ark/produ cts/75272 /intel -xeon-proce ssor-e5-2660-v2-25m-cache -2-20-ghz.html 18. We store points from the datasets as degerate rectangles in SAMs 19. http://trove 4j.sourc eforg e.net/html/bench marks .shtml .…”

mentioning

confidence: 99%

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How Good Are Modern Spatial Libraries?

et al. 2020

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Many applications today like Uber, Yelp, Tinder, etc. rely on spatial data or locations from its users. These applications and services either build their own spatial data management systems or rely on existing solutions. JTS Topology Suite (JTS), its C++ port GEOS, Google S2, ESRI Geometry API, and Java Spatial Index (JSI) are some of the spatial processing libraries that these systems build upon. These applications and services depend on indexing capabilities available in these libraries for high-performance spatial query processing. In this work, we compare these libraries qualitatively and quantitatively based on four different spatial queries using two real world datasets. We also compare these libraries with an open-source implementation of the Vantage Point Tree—an index structure that has been well studied in image retrieval and nearest-neighbor search algorithms for high-dimensional data. We found that Vantage Point Trees are very competitive and even outperform the aforementioned libraries in two queries.

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Section: Indexing Costsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%