NSF PAR Search | NSF Public Access Repository

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

OSMX: spark-based geospatial data extractor from OpenStreetMap

https://doi.org/10.1145/3557915.3560954

Singla, Samriddhi; Zhang, Yaming; Eldawy, Ahmed (November 2022, the 30th International Conference on Advances in Geographic Information Systems)

Full Text Available
A novel GCN-based point cloud classification model robust to pose variances

https://doi.org/10.1016/j.patcog.2021.108251

Wang, Huafeng; Zhang, Yaming; Liu, Wanquan; Gu, Xianfeng; Jing, Xin; Liu, Zicheng (January 2022, Pattern Recognition)

Full Text Available
Beast: Scalable Exploratory Analytics on Spatio-temporal Data

https://doi.org/10.1145/3459637.3481897

Eldawy, Ahmed; Hristidis, Vagelis; Ghosh, Saheli; Saeedan, Majid; Sevim, Akil; Siddique, A.B.; Singla, Samriddhi; Sivaram, Ganesh; Vu, Tin; Zhang, Yaming (October 2021, Conference on Information and Knowledge Management (CIKM))

Full Text Available
Evaluating computational geometry libraries for big spatial data exploration

https://doi.org/10.1145/3403896.3403969

Zhang, Yaming; Eldawy, Ahmed (June 2020, Sixth International ACM SIGMOD Workshop on Managing and Mining Enriched Geo-Spatial Data (GeoRich’20))

With the rise of big spatial data, many systems were developed on Hadoop, Spark, Storm, Flink, and similar big data systems to handle big spatial data. At the core of all these systems, they use a computational geometry library to represent points, lines, and polygons, and to process them to evaluate spatial predicates and spatial analysis queries. This paper evaluates four computational geometry libraries to assess their suitability for various workloads in big spatial data exploration, namely, GEOS, JTS, Esri Geometry API, and GeoLite. The latter is a library that we built specifically for this paper to test some ideas that are not present in other li- braries. For all the four libraries, we evaluate their computational efficiency and memory usage using a combination of micro- and macro-benchmarks on Spark. The paper gives recommendations on how to use these libraries for big spatial data exploration.
more » « less
Full Text Available

Search for: All records