Due to the surge of spatio-temporal data volume, the popularity of location-based services
and applications, and the importance of extracted knowledge from spatio-temporal data to …

The high abundance of IoT devices have caused an unprecedented accumulation of
avalanches of geo-referenced IoT spatial data that if could be analyzed correctly would …

Range aggregation is a primitive operation in spatial data applications and there is a
growing demand to support such operations over a data federation, where the entire spatial …

This era is witnessing an exponential growth in spatial data due to the increase in GPS-
enabled devices. Spatial data can be of extreme use to commercial businesses …

Apache Flink is an open-source system for scalable processing of batch and streaming data.
Flink does not natively support efficient processing of spatial data streams, which is a …

SpatialHadoop is an extended MapReduce framework supporting global indexing
techniques that partition spatial datasets across several machines and improve spatial query …

Due to the ubiquitous use of spatial data applications and the large amounts of such data
these applications use, the processing of large-scale distance joins in distributed systems is …

In the current era of big spatial data, the vast amount of produced mobility data (by sensors,
GPS-equipped devices, surveillance networks, radars, etc.) poses new challenges related to …

This article explores the use of deep learning to choose an appropriate spatial partitioning
technique for big data. The exponential increase in the volumes of spatial datasets resulted …

ABSTRACT Apache Sedona (formerly GeoSpark) is a new in-memory cluster computing
system for processing large-scale spatial data, which extends the core of Apache Spark to …