Jianting Zhang's Picture

Jianting Zhang

Assistant professor in Geographical Information System (GIS)


Affilications:
CS@CUNY City College (Primary)

CS@CUNY Graduate CenterNOAA-CRESTUTRC,
CCSI@ORNL (through DOE VFP)


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.
[12/10/2014] I was a recipient of the CUNY Certificate of Recognition for the year 2014.  
[03/11/2015] GeoTECI Ph.D. Student Simin You has released his SpatialSpark source code at GitHub [Link]. The related CloudDM'15 paper can be found here.
[04/02/2015] A summary of our work on GPU-based geospatial processing over the past five years (2009-2014) entitled "Large-Scale Spatial Data Processing on GPUs and GPU-Accelerated Clusters" appeared at the ACM SIGSPATIAL Special as an invited paper. Click here for a local copy. 
[05/12/2015] The GeoTECI lab received an unrestricted gift fund from Pitney Bowes Inc.,the parent company of MapInfo GIS,  to build collaboration on processing large-scale geospatial data in parallel and distributed computing environments.
[07/03/2015] Our submission entitled "Spatial Join Query Processing in Cloud: Analyzing Design Choices and Performance Comparisons" is accepted by IEEE ICPP/ HPC4BD workshop. Click here for the PDF file (8 formatted pages- IEEE Conf.) The paper compared HadoopGIS, SpatialHadoop and SpatialSpark and concludes that SpatialHadoop generally wins on robustness while SpatialSpark is a clear winner of efficiency/performance (largely due to in-memory processing on Spark). Data and steps to repeat the experiments will be posted online later. 
[07/03/2015] A technical report entitled "High-Performance Partition-based and Broadcast-based Spatial Join on GPU-Accelerated Clusters" is released. Click here for the PDF file (9 formatted pages- ACM Conf.)
[07/03/2015] We have released the source code of our ISP prototypes (including ISP-MC, ISP-MC+ and ISP-GPU). Click here for the code. It has been a very tough yet pleasant journey to learn advanced features of Impala and integrate our single node data parallel spatial indexing and spatial join techniques (on GPUs as well as multi-core CPUs) into Impala. The learnt experiences and lessons lead us to the design and implement of LDE as a succession to ISP.

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: 2015 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]