GeoSpark: a cluster computing framework for processing large-scale spatial data

By Jia Yu, Jinxuan Wu, Mohamed Sarwat. SIGSPATIAL/GIS, 2015

401 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/6d3420dfdf0a7b60d61ef2eac01332611c870663

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Gao Cong
2. J. Shane Culpepper
3. Zhifeng Bao
4. Sheng Wang

In Information Systems

1. Gao Cong
2. J. Shane Culpepper
3. Joshua Zhexue Huang
4. Ruslan Dautov
5. Salman Salloum
6. Patrick Xiaogang Peng
7. Xiaojun Chen

In Signal Processing

1. Gao Cong
2. J. Shane Culpepper
3. Joshua Zhexue Huang
4. Zhifeng Bao
5. Sheng Wang

Citations in the corpus

• A Survey of Traffic Prediction: from Spatio-Temporal Data to Intelligent Transportation (2021, neutral): “For example, GeoSpark [84] and Simbda [85] are two useful systems for processing spatial data.”
• A Survey on Trajectory Data Management, Analytics, and Learning (2020, methodology): “SpatialSpark [188] and GeoSpark [189] are two representative open-source systems from the research field of spatial databases, which also support the storage and querying of LineString.”
• Big data analytics on Apache Spark (2016, methodology): “In addition to the research highlights we presented in the previous sections, there are other research works which have been done using Apache Spark as a core engine for solving data problems in machine learning and data mining [5,36], graph processing [16], genomic analysis [60,65], time series dat”