Self-Funded Phd Research Project

School of Engineering and Built Environment Self-Funded PhD Research Project

Project Title: Big Data Streaming Analytics Project Reference Number: SEBE_SELF_YZ1 Key words: Big Data, Internet of Things, Data Analytics Background Big data features 4 V’s, namely, Volume, Velocity, Variety and Veracity. Data Stream is generated continuously by tens and thousands of data sources including embedded sensors, Internet-of-Things (IoT) devices, web clicks, transactions, or mobile apps. Data streams are intrinsic of the real-world problems such as sensory data driven healthcare monitoring, smart sustainable environments, network security monitoring, etc. For data stream real-world problems, the most prominent big data challenge amongst all lies with the capability of handling the velocity of the data stream. Because of the streaming nature of the real-world data, if the processing and analysis fail to capture timely the changing patterns in the data stream, then, as the data has moved on, the knowledge crucial for deep analysis and decision would not be seized. The primary motivation of this project is to investigate a common methodological framework that is underpinning a class of sensory data driven domains, such as home care/tele-monitoring for patients with chronic illness, energy and condition monitoring of smart built environments, and network security monitoring. Aim and Objectives The aim of the project is to conduct a systematic investigation of data stream processing and streaming analytics that are well suited for a class of sensory data driven real-world problems. This is mapped to the following inter-linked objectives:  To investigate an efficient architecture that incorporates both stream and batch processing of data stream. The project will adapt the Lambda architecture to best fit the sensory data driven real-world problems in healthcare monitoring, smart built environments and network security monitoring. The stream processing should capture the one-pass nature of the data while the batch processing is supported as well to accumulate long-term a priori knowledge.  To develop a novel framework of streaming analytics to best fit the sensory data driven real-world problems in healthcare monitoring, smart built environments and network security monitoring. The project will critically evaluate stream mining algorithms, including clustering learning, predictive learning (e.g., deep learning), novelty detection, frequent pattern mining, against their technical suitability for the sensory data driven real-world problems.  To prototype the stream processing and streaming analytics platform for applications in sensory data driven real-world problems. The project will be based on technical evaluation of open-source stream processing platforms, especially Apache Flink, Storm, Spark Streaming, Samza, Kafka, against their technical suitability for sensory data driven real-world problems, e.g., home care/tele- monitoring for patients, energy and condition monitoring of smart built environments and network security monitoring. Research Supervisor(s) Candidates are encouraged to contact the following researchers for further details:  Dr Yan Zhang ([email protected] )  Professor Huaglory Tianfield ([email protected] ) Mode(s) of Study This project is available as a:  PhD: 3 years full-time  PhD: 4-6 years part-time (provided UK Visa eligibility criteria are satisfied)  1 + 3 route to PhD: Undertaking MRes [1 year full-time] + PhD as above APPLICATION DETAILS Eligibility Applicants will normally hold a UK honours degree 2:1 (or equivalent); or a Masters degree in a subject relevant to the research project. Equivalent professional qualifications and any appropriate research experience may be considered. A minimum English language level of IELTS score of 6.5 (or equivalent) with no element below 6.0 is required. Some research disciplines may require higher levels. Specific requirements of the project: The successful applicant will have good mathematical background. Previous experience with real time implementations and programming is desirable. How to Apply Candidates are encouraged to contact the research supervisor(s) for the project before applying. Applicants should download and complete the GCU Research Application Form (available from: http://www.gcu.ac.uk/media/gcalwebv2/study/postgrad/GCU-Postgrad-Research-App-Form-Oct %2014.pdf stating the Project Title and Reference Number (listed above). Or they may attach an alternative research proposal (see Guidance on writing a research proposal) that is related to the themes and expertise of the School (http://www.gcu.ac.uk/ebe/research/phdopportunities/). The completed GCU Research Application form should be sent with copies of academic qualifications (including IELTS if required), 2 references and any other relevant documentation to: [email protected]. Applicants shortlisted for a PhD will be contacted for an interview. Application Deadlines The PhD programmes commence in 01 October, 01 February or 01 May of each year. The application deadlines are as follows:

Start Date Application Deadline October 2017 1 July 2017 February 2018 1 October 2017 May 2018 1 January 2018 October 2018 1 June 2018 February 2019 1 October 2018

Research Degree Fees Current fee information: http://www.gcu.ac.uk/study/postgraduate/feesandfunding/tuitionfees/