Jianting Zhang's Picture

Jianting Zhang

Assistant professor in Geographical Information System (GIS)

CS@CUNY City College (Primary)


Geospatial Technologies and Environmental Cyberinfrastructure (GeoTECI) Lab

NSF IIS-Medium Collaborative: Spaital Data and Trajectory Data Managment on GPUs

Facutly Profile[Education/Training] [Professional Experiences] [Courses] [Contact Info][Archieved News]

GeoTECI Lab [Overview (9 slides)] [Students] [Hardware] [Software][Data] [Research Code and Online Demos]

Recent News:
[08/20/2014] A brief introduction to my research and my group (GeoTECI lab) with 9 slides is added.

[11/23/2014] The implementation and experiment details of ISP-GPU and ISP-MC+ have been documented in a technical report entitled "Scalable and Efficient Spatial Data Management on Multi-Core CPU and GPU Clusters: A Preliminary Implementation based on Impala". Please see the project page maintained by Simin You for more informaiton.

[11/23/2014] Comparisons between SpatialSpark and ISP-MC have been documented in a  technical report entitled "Large-Scale Spatial Join Query Processing in Cloud ". SpatialSpark  is based on Apache Spark. ISP-MC is a variant of ISP-MC+ that uses the mature GEOS for spatial refinment which is  more robust and suitable for Cloud deployment.  

Research Interests

·        High-Performance Geospatial Computing (HPC-G)

·        Spatial Databases (SDB) and GIS Applications (GIS)

·        Environmental Cyberinfrastructure (CI)

·        Geospatial Visual Analytics (GVA)

·        Multispectral and Hyperspectral Remote Sensing Data Processing (RS)

Publication (DBLP)

        ·        By Topics (Selected): HPC-G, SDB, GIS, RS, GVA, CI

        ·        By Year: 2014, 2013, 2012, 2011, 2010 2009, 2008, 2007, 2006, 2005, 2004, 2003, 2002, 2001, 2000, 1999, 1998, 1997


Research Initiatives and Exploratory Projects [Funded Projects]

·   High-Performance Geospatial Computing on GPGPUs [Overview] (* Experimental Source Code)
(1) Previous Results: [HPC-G (Position Paper)] [BMMQ-Tree] [BQ-Tree] [NNI-DEM][Polygon Rasterization]

                        (2) Reference Primitives based Implementations [PrimQuad*][PrimCSPTP*][PrimTrQuery*] [PrimSpJoin*]
                        (3) Spatial Indexing and Query Optimization: [R-Tree][Selectivity Estimation]

                        (4) End-to-End Systems for Spatial Joins (10-40X over in-memory systems and 3-4 orders of speedups over disk-resident systems)
                             [Point to Network][Point in Polygon][Point to Polygon][Trajectory to Trajectory]

                        (5) In-Progress (Algorithms): [Indexing Polygon Internals] [Increamental Refining Filtering]  [Polygon to Polygon Join (Overlay)]
                        (6) In-Progress (Systems) [LLVM-based Spatial SQL Front-end] [Hybrid CPU-GPU Systems] [Scaling-Out to Clusters]

·     Managing Large-Scale  OD/Trajectory Data with Applications to Data Mining of Traffic and Travel Patterns [Overview]
[U2SOD-DB] [U2SOD-VA][NYC Case Studies] [Frequenet Sequence Mining using Shortcuts]

·     Managing Large-Scale Environmental and Species Distribution Data with Applications to Understanding Global and Regional Biodiversity Patterns  [Overview] [Visual Exploreations of Rasters] [Lightweight Compression for Rasters] [USGS Little Tree Range Map] [NatureServe Bird Range Map in DBMSs] [Zonal Summation of GBIF Data on GPUs]

·    Web-based Query-Driven Visual Exploration of Large-Scale Geospatial Data (*Online Accessible)
[Overview] [NYC CrashMap*][BirdsQuests*][RasterExplorer*] [HP-GVE]