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 …

Owing to the continuous development of earth observation technology in recent years,
substantial amount of remote sensing image and natural resources business data have …

大数据时代下, 移动互联网发展与移动终端的普及形成了海量移动对象轨迹数据.
轨迹数据含有丰富的时空特征信息, 通过轨迹数据处理技术, 可以挖掘人类活动规律与行为特征 …

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

Accurate and reliable forestry data can be obtained by means of continuous monitoring of
forests using advanced technologies, which provides a major opportunity for the …