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Cloud-based Approaches to Streaming Data Analysis

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  • Full or part time
    Dr W Pang
    Prof G M Coghill
  • Application Deadline
    Applications accepted all year round

Project Description

The use of sensors, both in conventional industrial settings and in the modern ‘internet of things’ settings, generates large quantities of data – so called Big Data. One of the major challenges in Big Data is to perform real-time analysis on multiple high-velocity data streams, examples of which include credit card fraud detection from large-scale business transactions and real-time anomaly detection on high-resolution weather sensor data.

Recent development in cloud computing makes it possible to develop scalable machine learning algorithms to infer appropriate models, which can predict the behaviour and discover novel patterns across multiple data streams.

In this project, we are going to explore how to develop efficient model learning algorithms in the cloud for better analysing streaming data, and we want to answer the following questions:

1. How to effectively capture and pre-process multiple data streams in the cloud environment.
2. How to develop model learning algorithms by making use of existing cloud computing techniques.
3. How to perform model selection given a set of candidate models.
4. Whether can we learn ab initio models from existing knowledge and data or learn models through composition of model fragments?

To achieve this we will investigate the development of evolutionary model learning approaches on the cloud. We will study the suitability of different cloud platforms for processing multiple streams. We will test our model learning algorithm by both synthetic and real world data.

The successful applicant will have a first or upper second class degree (or equivalent) in Computing Science or related disciplines. Knowledge- Essential: machine learning basics; cloud computing basics; programming in Java, Python, Ruby, or Scala. Desirable: evolutionary computing, experience in Hadoop; programming with MapReduce; Experience in Scalding or Storm.

Funding Notes

There is no funding attached to this project, it is for self-funded students only.


Application Process:

Formal applications can be completed online: You should apply for PhD in Computing Science, to ensure that your application is passed to the correct College for processing. Please ensure that you quote the project title and supervisor on the application form.

Informal inquiries can be made to Dr W Pang ([email protected]) with a copy of your curriculum vitae and cover letter. All general enquiries should be directed to the Graduate School Admissions Unit ([email protected]).

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