This paper introduces GeoSpark an in-memory cluster computing framework for processing
large-scale spatial data. GeoSpark consists of three layers: Apache Spark Layer, Spatial …

This paper describes SpatialHadoop; a full-fledged MapReduce framework with native
support for spatial data. SpatialHadoop is a comprehensive extension to Hadoop that injects …

The paper presents the details of designing and developing GeoSpark, which extends the
core engine of Apache Spark and SparkSQL to support spatial data types, indexes, and …

Natural hazards result in devastating losses in human life, environmental assets and
personal, and regional and national economies. The availability of different big data such as …

This paper presents ST-Hadoop; the first full-fledged open-source MapReduce framework
with a native support for spatio-temporal data. ST-Hadoop is a comprehensive extension to …

Hadoop, employing the MapReduce programming paradigm, has been widely accepted as
the standard framework for analyzing big data in distributed environments. Unfortunately …

The recent explosion in the amount of spatial data calls for specialized systems to handle
big spatial data. In this paper, we discuss the main features and components that needs to …

Recently, MapReduce frameworks, eg, Hadoop, have been used extensively in different
applications that include tera-byte sorting, machine learning, and graph processing. With the …

The recent explosion in the amount of spatial data calls for specialized systems to handle
big spatial data. In this survey, we summarize the state-of-the-art work in the area of big …

Due to the proliferation of GPS-enabled devices in vehicles or with people, large amounts of
position data are recorded every day and the management of such mobility data, also called …