Support of high performance queries on large volumes of spatial data becomes increasingly
important in many application domains, including geospatial problems in numerous fields …

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 …

In this paper, MapReduce is used to speed up and make possible three large-scale medical
image processing use-cases:(i) parameter optimization for lung texture classification using …

Various indexing methods of spatial data have come out after rigorous efforts put by many
researchers for fast processing of spatial queries. Parallelizing spatial index building and …

In the field of big data research, analytics on spatio-temporal data from social media is one
of the fastest growing areas and poses a major challenge on research and application. An …

Spatial query processing involves complex multidimensional objects and compute intensive
spatial operations, and therefore requires a high performance approach to meet the rapid …

Geospatial big data analysis (GBDA) is extremely significant for time-constraint applications
such as disaster response. However, the time-constraint analysis is not yet a trivial task in …

Recent years have witnessed an explosion of geospatial data, especially in the form of
Volunteered Geographic Information (VGI). As a prominent example, OpenStreetMap (OSM) …

With the growing number of Open Data initiatives and the increased volume of related data,
new forms of civic engagement are emerging. This engagement leads to novel applications …

It is very important to evaluate the MapReduce-based frameworks for scientific data
processing applications. Scientists need a low-cost, scalable, easy-to-use and fault …