Big Data has emerged in the past few years as a new paradigm providing abundant data
and opportunities to improve and/or enable research and decision-support applications with …

Geospatial big data refers to spatial data sets exceeding capacity of current computing
systems. A significant portion of big data is actually geospatial data, and the size of such …

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 …

The rapidly increasing amount of location data available in many applications has made it
desirable to process their large-scale spatial queries in Cloud for performance and …

The term 'Big Data'describes innovative techniques and technologies to capture, store,
distribute, manage and analyze petabyte-or larger-sized datasets with high-velocity and …

SpatialHadoop is an extended MapReduce framework that supports global indexing that
spatial partitions the data across machines providing orders of magnitude speedup …

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

Remote sensing data collected by satellites are now made publicly available by several
space agencies. This data is very useful for scientists pursuing research in several …

Spatial vector data with high-precision and wide-coverage has exploded globally, such as
land cover, social media, and other data-sets, which provides a good opportunity to enhance …

Big Data sets can contain all types of information: from server log files to tracking information
of mobile users with their location at a point in time. Apache Spark has been widely …