Interactive Sequential Pattern Mining of Web Logs

Primary supervisor

Contact admissions office

Funding

  • Competition Funded Project (Students Worldwide)
This research project is one of a number of projects at this institution. It is in competition for funding with one or more of these projects. Usually the project which receives the best applicant will be awarded the funding. Applications for this project are welcome from suitably qualified candidates worldwide. Funding may only be available to a limited set of nationalities and you should read the full department and project details for further information.

Project description

Sequential pattern mining algorithms are extremely useful to identify the most salient patterns in sequences of events [1,2]. These events can be transactions on a database or time-series data. When applying these algorithms on interactions of the user interface the patterns can be used to evaluate the user interface in that they indicate the most common (mis)uses of interactive systems.

However, the output of pattern mining algorithms is typically large and noisy, and requires, typically, the use of trial and error strategies to identify those patterns that are semantically meaningful [3]. This is especially challenging if the users of the mining algorithms are not data mining specialists but developers or user researchers. The objective of this PhD project is to support non-specialists conducting evaluations of interactive systems using sequential pattern mining algorithms. The following three research questions must be answered to address this problem:

RQ1. Can we support researchers in selecting the most suitable sequential pattern mining algorithm that addresses their research objectives while taking into account the type of datasets used?

RQ2. To what extent can this process be automatised?

RQ3. Can we isolate problematic interactions (ie usability problems) from regular patterns of use?

References
[1] Nizar R. Mabroukeh and C. I. Ezeife. 2010. A taxonomy of sequential pattern mining algorithms. ACM Comput. Surv. 43, 1, Article 3 (December 2010), 41 pages. DOI: https://doi.org/10.1145/1824795.1824798
[2] Carl H. Mooney and John F. Roddick. 2013. Sequential pattern mining -- approaches and algorithms. ACM Comput. Surv. 45, 2, Article 19 (March 2013), 39 pages. DOI=http://dx.doi.org/10.1145/2431211.2431218
[3] Himel Dev and Zhicheng Liu. 2017. Identifying Frequent User Tasks from Application Logs. In Proceedings of the 22nd International Conference on Intelligent User Interfaces (IUI '17). ACM, New York, NY, USA, 263-273. DOI: https://doi.org/10.1145/3025171.3025184

Person specification

For information

Essential

Applicants will be required to evidence the following skills and qualifications.

  • You must be capable of performing at a very high level.
  • You must have a self-driven interest in uncovering and solving unknown problems and be able to work hard and creatively without constant supervision.

Desirable

Applicants will be required to evidence the following skills and qualifications.

  • You will possess determination (which is often more important than qualifications) although you'll need a good amount of both.
  • You will have good time management.

General

Applicants will be required to address the following.

  • Discuss your final year Undergraduate project work - and if appropriate your MSc project work.
  • How well does your previous study prepare you for undertaking Postgraduate Research?
  • Comment on your transcript/predicted degree marks, outlining both strong and weak points.
  • Why do you believe you are suitable for doing Postgraduate Research?
▲ Up to the top