Scientific research is built on the notion of sharing experimental results for peer review, verification, replication and validation. To facilitate this, many scientific disciplines not only share their experimental protocols and results through academic papers but also share their datasets and related experimental meta-data to facilitate replication, evaluation and benchmarking. Within the pervasive healthcare community, the Open Data Initiative (ODI) has been proposed and prototyped to address this need. Key features of this initiative are an ontology representation for description and interrogation of experiment meta-data (activities, devices, participants, protocols, locations), XES (eXtensible Event Stream) mark-up for describing event data in a semantically consistent manner, and integration with open platforms for dataset management. Through this work, many ideas have been generated and questions raised as to the optimum manner in which to specify, model and share experimental datasets in pervasive computing.
The aim of this project is to revise and extend existing work in the Open Data Initiative to address:
*what is the appropriate level of granularity at which to specify concepts in the ODI ontology to maximize both relevance of a dataset to other researchers and also reproducibility of the experiment? This includes the extent to which a dataset repository can facilitate transfer learning methods in activity recognition.
*what is the most useful format for representing event data? Using simple mark-up such as labelled csv files facilitates ease of use but is limited in the semantic information conveyed in the dataset and related meta-data. On the other hand, rich mark-up such as XES embraces semantic richness at the expense of simplicity.
*how should such a standardized approach to data sharing accommodate varying levels of experimental setup and data quality? For example, on the one hand, the experiment protocol might precisely specify dataset features as part of a randomized control trial. On the other hand, the protocol might only specify such features at a high level of abstraction (device, number of participants), to facilitate the quick sharing of results for data exploration.
Given this aim, the proposed project is expected to focus on:
*automated ontological modelling of experimental configurations in pervasive computing to enable flexible modelling granularity, supporting a range of experimental protocols.
*modelling of a workflow which will assist researchers in preparing and assembling their data for sharing with others through the ODI. This will include necessary steps and checks to ensure data integrity and quality before dissemination.
*how to automate the mark-up of collected data in a way which is (a) compliant with open data interchange formats and (b) in a format which facilitates rich semantic description of the data for dataset sharing, matching and reuse by other researchers.
*prototyping the above ideas in an ODI tool through which feedback and evaluation can be meaningfully conducted.
In summary, the aim of the work is to lead towards an ODI tool which will support pervasive computing researchers in the preparation and conduct of experiments which are ready for rapid dissemination within the research community.
If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.
The University offers the following awards to support PhD study and applications are invited from UK, EU and overseas for the following levels of support:
Full award (full-time PhD fees + DfE level of maintenance grant + RTSG for 3 years).
This scholarship will cover full-time PhD tuition fees and provide the recipient with £15,000 maintenance grant per annum for three years (subject to satisfactory academic performance). This scholarship also comes with £900 per annum for three years as a research training studentship grant (RTSG) allocation to help support the PhD researcher.
Part award (full-time PhD fees + 50% DfE level of maintenance grant + RTSG for 3 years).
This scholarship will cover full-time PhD tuition fees and provide the recipient with £7,500 maintenance grant per annum for three years (subject to satisfactory academic performance). This scholarship also comes with £900 per annum for three years as a research training studentship grant (RTSG) allocation to help support the PhD researcher.
Fees only award (PhD fees + RTSG for 3 years).
This scholarship will cover full-time PhD tuition fees for three years (subject to satisfactory academic performance). This scholarship also comes with £900 per annum for three years as a research training studentship grant (RTSG) allocation to help support the PhD researcher.
The scholarship will cover tuition fees at the Home rate and a maintenance allowance of £ 15,009 per annum for three years. EU applicants will only be eligible for the fee’s component of the studentship (no maintenance award is provided). For Non-EU nationals the candidate must be "settled" in the UK. This scholarship also comes with £900 per annum for three years as a research training studentship grant (RTSG) allocation to help support the PhD researcher.
Due consideration should be given to financing your studies; for further information on cost of living etc. please refer to: www.ulster.ac.uk/doctoralcollege/postgraduate-research/fees-and-funding/financing-your-studies
As Senior Engineering Manager of Analytics at Seagate Technology I utilise the learning from my PhD ever day
Adrian Johnston - PhD in InformaticsWatch Video
Friday 7 February 2020
Late March 2020
The largest of Ulster's campuses
Monday 2 December 2019
Subject: Computer Science and Informatics
When applying for this PhD opportunity please quote reference number: