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. 

[/12/24/2014] Our submission entitled "Large-Scale Spatial Join Query Processing in Cloud " is accepted by ICDE CloudDM'15 workshop.  The work provides high-level descriptions and performance comparisons of the SpatialSpark and ISP-MC prototypes that we we have developed.  The two systems are targeted for exisiting Cloud computing resouces by using existing mature geometry libraries (JTS and GEOS, respective). The technical report version of the paper is here
[12/312014] A sumary of our work on GPU-based geospatial procesisng over the past five years (2009-2014) can be found in a technical report entitled "Large-Scale Spatial Data Processing on GPUs and GPU-Accelerated Clusters". A formatted version to appear at the ACM SIGSPATIAL Special as an invited paper.

[01/06/2014] Our submission entitled "Scalable and Efficient Spatial Data Management on Multi-Core CPU and GPU Clusters: A Preliminary Implementation based on Impala" has been accepted by ICDE HardBD'15 workshop. By reusing ISP-MC framework, developing new AOS (Array of Structures) for geometry layout and integrating data parallel designs for efficient single-node compuitng, both ISP-MC+ (for multi-core CPU clusters) and ISP-GPU (for GPU-accelerated clusters) have achieved impressive performance. The technical report version of the paper is here.

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]