Jianting Zhang

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Cluster computing, Cloud computing and GPU computing play overlapping and complementary roles in parallel processing of geospatial data within the general HPC framework. The fast increasing hardware capacities of modern personal computers equipped with chip multiprocessor CPUs and massively parallel GPUs have made high performance computing of large-scale(More)
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 scalability. We report our designs and implementations of two prototype systems that are ready for Cloud deployments: SpatialSpark based on Apache Spark and ISP-MC based on Cloudera(More)
Finding an effective method to quantify species compositional changes in time and space has been an important task for ecologists and biogeographers. Recently, exploring regional floristic patterns using data derived from satellite imagery, such as the normalized difference vegetation index (NDVI) has drawn considerable research interests among ecologists.(More)
Hyperspectral image processing based on grid computing technology is attractive due to the large data volumes of hyperspectral images and intensive computation requirements for processing. Many existing grid workflow tools do not provide integrated visual workflow composition environments and/or do not have workflow validation mechanisms to ensure(More)
Volumes of GPS recorded trajectory data in ubiquitous urban sensing applications are increasing fast. Many trajectory queries are both I/O and computing intensive. In this study, we propose to develop the USTRA prototype system to efficiently manage large-scale GPS trajectory data using General Purpose computing on Graphics Processing Units (GPGPU)(More)
Data volumes of GPS recorded locations and many other types of geospatial data are fast increasing. Processing large-scale spatial joins in Cloud for performance and scalability is becoming increasingly popular. In this study, we compare three leading Cloud-based spatial data management systems, namely Hadoop GIS, Spatial Hadoop and Spatial Spark, both(More)
Geospatial analyses of distributed data from surveys and sensors are often stored and managed in diverse regional, national and global repositories. The nature of scientific processes requires composition of these resources in a meaningful order to solve a specific geoscience problem. These tasks can be viewed as scientific workflows. Web based interfaces(More)
Point-in-Polygon (PIP) test is fundamental to spatial databases and GIS. Motivated by the slow response times in joining large-scale point locations with polygons using traditional spatial databases and GIS, we have designed and developed an end-to-end system completely on Graphics Processing Units (GPUs) to associate points with the polygons that they fall(More)
R-Trees are popular spatial indexing techniques that have been widely adopted in many geospatial applications. As commodity GPUs (Graphics Processing Units) are increasingly becoming available on personal workstations and cluster computers, there are considerable research interests in applying the massive data parallel GPGPU (General Purpose computing on(More)