Large-Scale Spatiotemporal Data Analytics and Learning
Project Description

A spatial trajectory is a trace generated by a moving object in geographical spaces, where a moving object can be any entity whose locations can be continuously captured using location positioning devices or monitored by various types of sensors. With the rapid development of location-positioning and sensor systems, navigational and social network applications, wireless and telecommunication technologies, a myriad of spatial trajectory data has been available representing the mobility of a variety of moving objects, such as people, vehicles, assets, animals, and natural phenomena. Spatial trajectory data is a typical type of big data, featuring extremely large volumes, a high level of structure and semantic diversity, and the dynamic and streaming nature of the data and the related environment. They contain rich information to monitor and to discover the relationship for moving objects and locations at an unprecedented scale. Spatial trajectory data management and analytics are fundamental technologies to enable intelligent transport systems, location-based services, resource tracking and scheduling, emergency responses, urban planning, IoT and smart city, to derive values from big spatiotemporal data.

This project is part of our leading research to develop a highly scalable, quality-aware, general-purpose, and open system to manage spatiotemporal data. We will focus on new computing and database platforms and machine learning models to support descriptive, predictive and prescriptive trajectory data analytics.

Supervisor
ZHOU Xiaofang
Quota
4
Course type
UROP1100
UROP2100
UROP3100
UROP4100
Applicant's Roles

Depending on the discussion between the student and the supervisor to refine the scope of this project, you will do one or several of the following tasks:

- conduct a literature review and experimentally evaluate existing work on a mutually agreed topic;
- develop algorithms for large road networks;
- use database systems to manage network and spatiotemporal data;
- develop efficient data structures and query processing methods for spatiotemporal data;
- design and implement spatiotemporal data mining methods for spatial trajectory data;
- develop and use machine learning models for road network-related predications;
- visualise road network and trajectory data.

You will work in a small team of researchers and PhD students.

Applicant's Learning Objectives

In this project, you have opportunities to learn the followings:

- use of advanced database technologies for large scale, network and spatiotemporal data;
- design, implementation and use of new algorithms, data mining and machine learning methods in the context of spatiotemporal analytics and intelligent transport applications.
- participation in a team to conduct original and cutting-edge research, to produce research reports and possible publications.

Complexity of the project
Moderate