Algorithms and Systems for Big Spatial and Spatiotemporal Data

GLOBAL RESEARCH INSTITUTES

  • Algorithms and Systems for Big Spatial and Spatiotemporal Data

Algorithms and Systems for Big Spatial and Spatiotemporal Data

Principal Supervisor: Dr. Cheng Long

Second Supervisor: Prof. Weiru Liu

+ Project Description

With advances in remote sensors, sensor networks, and the proliferation of location sensing devices in daily life activities and common business practices, the generation of disparate, dynamic, and geographically distributed spatial and spatiotemporal data has exploded in recent years. Though these improvements are leading to big data and making it hard to manage and process, they are also enabling new applications. For example, multispectral and temporal remote sensing data can be used for monitoring biomass to nuclear proliferation, mapping crops to human settlements. Likewise streaming geosocial media data can be exploited for better understanding the pulse of the cities or for generating near-real time alerts for emergency responders. Other promising areas where big spatial and spatiotemporal data is very much involved are Smart Cities and Urban Computing.

Unfortunately, the urgent need to manage and analyse big spatial data is hampered by the lack of specialized systems, techniques, and algorithms to support such data. Thus, novel and scalable data management and analytical frameworks are needed to meet the challenges posed by the big spatial and spatiotemporal data. 

To this end, this project is to contribute to the advancement of knowledge in big spatiotemporal data management and analytics. Specifically, this project is to develop novel indexing methods for massive geospatial data, scalable algorithms based on high performance computing frameworks (e.g., MapReduce), methods for spatial and spatiotemporal data mining, visualization and analysis of massive geospatial data, and customizations and extensions of existing software infrastructures such as SpatialHadoop for spatial and spatiotemporal data management and analysis.

+ How to Apply

Applicants should apply electronically through the Queen’s online application portal at: https://dap.qub.ac.uk/portal/

+ Contact Details

Supervisor Name: Dr. Cheng Long         
Address:

Queens University of Belfast
School of EEECS
Rm 03.003, Computer Science Building

Email:

cheng.long@qub.ac.uk

Tel:

+44 (0)28 9097 4783