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.
[12/10/2014] I was a reecipient 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 sumary of our work on GPU-based geospatial procesisng 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. 

[04/10/2015] Our paper entiled entiled "Tiny GPU Cluster for Big Spatial Data: A Preliminary Performance Evaluation" was accepted by IEEE International Workshop on High-Performance Big Data Computing (HPBDC'15) workshop, colocated  with the main IEEE ICDCS'15 conference. Click here for the PDF file (6 formatted pages- IEEE Conf.).
[04/15/2015] Prof. W. Randolph Franklin from RPI visited our lab. Three talks were given by his research group. His talk, entitled "Algorithms, libraries, and development environments to process huge geoinformatic databases on modern hardware" is online.
Our submission entiled "A Lightweight Distributed Execution Engine for Large-Scale Spatial Join Query Processing" is accepted by IEEE Big Data Congres 2015. The acceptance rate is 20% and it happens to be at NYC this year. Click here for the PDF file (8 formatted pages- IEEE Conf.).
[05/12/2015] A technical report entitled "Spatial Join Query Processing in Cloud: Analyzing Design Choices and Performance Comparisons" is released.
Click here for the PDF file (8 formatted pages- IEEE Conf.) The report 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 due to Spark). Data and steps to repeat the experiments will be posted online later.
[05/12/2015] The GeoTECI lab received an unresitcited 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.

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]