2016/12/21 by Rakesh Kumar Lenka, Lenka, Rakesh K., Rabindra K. Barik +9
Computer Science · Engineering · Social Sciences · #Computers and Society (cs.CY) #Data Management and Algorithms #Distributed #FOS: Computer and information sciences #Geographic Information Systems Studies #Parallel #Traffic Prediction and Management Techniques #and Cluster Computing (cs.DC)
paper · pdf · doi:10.48550/arxiv.1612.07433
openalex publication_date 2016/12/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this digitalised world where every information is stored, the data a are\ngrowing exponentially. It is estimated that data are doubles itself every two\nyears. Geospatial data are one of the prime contributors to the big data\nscenario. There are numerous tools of the big data analytics. But not all the\nbig data analytics tools are capabilities to handle geospatial big data. In the\npresent paper, it has been discussed about the recent two popular open source\ngeospatial big data analytical tools i.e. Spatial- Hadoop and GeoSpark which\ncan be used for analysis and process the geospatial big data in efficient\nmanner. It has compared the architectural view of SpatialHadoop and GeoSpark.\nThrough the architectural comparison, it has also summarised the merits and\ndemerits of these tools according the execution times and volume of the data\nwhich has been used.\n