Data Science research theme

The Data Science theme provides interdisciplinary linkages between the computer, statistics, mathematics, information, and intelligence sciences, and fosters cross-domain interactions between academia and industry. It encompasses a range of topics including big data architecture and analytical methods; infrastructures, tools and systems focusing on data processing and visualisation/analysis; machine learning algorithms; internet of things (IoT); data integration, governance, and record linkage; cloud/edge computing; predictive maintenance; next digital telecommunications and 5G.

Our past research has, in particular, focused on the real-world data science applications/case studies unlocking the full potential of data-driven decision-making in various domains, including agriculture, business (for example, development of data lake, customer behaviour analysis), environment, finance, healthcare, livestock, social (for example, social/care planning), telecom and transport (for example, aircraft and rail industry). 

Professor Kamran Munir

• Mr Ian Brooks
• Dr Ignacio Deza
• Dr Hisham Ihshaish
• Dr Ahsan Ikram
• Dr Shelan Jeawak
• Dr Qurat-Ul-Ain Mastoi
• Professor Kamran Munir
• Dr Emmanuel Ogunshile
• Dr Trupti Padiya
• Mrs Eman Qaddoumi

Current projects

Application of data-enabled innovations to commercial chicken production for business improvement and optimisation (Innovate UK, 2022-24)

UWE Bristol; Obafemi Awolowo University (OAU), Nigeria; and Taro Agriculture Farm (TAF), Nigeria; have been successful in securing funding through the African AgriFood Knowledge Transfer Partnership. The project team aims to develop infrastructure for real time data capture and data analytics, which will be used to generate retrospective and preventive insights to enhance business capabilities, reduce feed waste, improve chicken survivability and growth. 

Optimal control of aquaponic farms for food production using Internet of Things (IoT) and Artificial Intelligence (AI)

The purpose of this project is to investigate novel mechanisms to maximise the food production in an aquaponics farm by collecting sensor data using Internet of Things (IoT) and apply machine learning algorithms/Artificial Intelligence to optimise the farm parameters.

Airbus 

The Data Analytics Plateau team of Airbus Filton (UK) has partnered with UWE Bristol to build this smart automated solution for the automaton of predictive maintenance of Airbus Aircraft Systems.

SWEL: A domain-specific language for modelling data-intensive workflows

UWE Bristol; University of Cordoba (Spain); ITIS Software; and University of Malaga (Spain) have partnered together to develop SWEL: A domain-specific language for modelling data-intensive workflows. SWEL provides a flexible, extensible, and expressive solution for modelling and executing data-intensive workflows at a high-level of abstraction. 

Recent projects

AgroSupportAnalytics

A Cloud-based decision support system for sustainable farming in Egypt. This project named as Agro Support Analytics, aimed to assist resolving the problem of both the lack of support and advice for farmers, and the inconsistencies in doing so by current manual approach provided by agricultural experts. Funded by the British Council Newton Fund.

Innovate UK - Accelerating Innovation in rail

In association with Big Data Enterprise and Artificial Intelligence Laboratory (Big DEAL). For more details, see IoT-enabled platform for rail assets monitoring and predictive maintenance (i-RAMP). 

Innovate UK - GovTech Challenge Funding for high-tec innovations in adult-care

To improve the delivery of services in adult social care, this project aimed at optimising largescale care workforce allocations, predict anticipated market capacity, and undertake complex contingency planning scenarios. 

For further information about the projects listed above, please contact Professor Kamran Munir.