一本道

Computer & Information SciencesCollaborate with Computer & Information Sciences

Develop innovative solutions for your business

Great collaboration pushes the boundaries of all partners, and ideas spark when teams come together. Computer and Information Sciences (CIS) at the University of 一本道 has a strong track record of growing solutions for business.

Our research helps us to understand how computing can improve people’s lives and how it can be used every day. We undertake world-leading research in areas such as:

  • Digital Health and Wellness
  • Cybersecurity
  • Machine Learning / AI
  • Human Computer Interaction
  • Interactive information retrieval
  • Theoretical Computer Science.

Our research can build software and transform complex data in ways that provide key insights for business, science and society.

Here are some of the challenges our business partners are facing:

  • Can AI help me better understand my customer needs, leading to increased retention and sales?
  • In what ways does “play” help children to develop information-seeking behaviours?
  • I plan more Cloud-based business activity; how do I best prepare for the likely additional security risks?
  • How can I automatically discover redundancies, conflicts, and gaps in our access control policies?
  • How does a welder interact with devices when wearing gloves and working in a hostile environment such as the North Sea?

 

Our case studies illustrate successes and our impact. There are a variety of ways to jointly fund projects, including:

  • Innovation Vouchers: up to £7,500 for short-term projects.
  • Scottish Innovation Centre (IC) Funding: building collaborations across key sectors.
  • Funded PhDs: extended research engagement (3 years) to explore more fundamental questions at a cost of around £20k per annum.
  • Knowledge Transfer Partnership (KTP): embedding expertise within your business. The KTP scheme is supported by Innovate UK who provide funds to enable an associate from CIS to carry out KE while being located within an organisation over a 6-36 month period.
  • Consultancy: Our academics can offer advice, work on projects together wit you, or create something that you need, funded by a bespoke costing.

These are only some of the ways to engage with CIS. Contact our Knowledge Exchange Team to learn more: cis-ke@strath.ac.uk.

 

Case studies

BiP Solutions

Improving search relevance with smart retrieval

  • Industry: Commercial procurement.
  • Benefits: Greater relevance in search results leads to improved rates of customer retention.
  • Types of engagement: Data Lab funding followed by KTP Award.
  • Challenge: BiP Solutions is a leading provider of procurement support. Their business intelligence services identify which public sector tenders, from the largest tender database in Europe, should be presented to their customers. Improving the relevance of results is important for increasing customer retention.

We have already seen a number of assumptions challenged. . . but feel we are only scratching the surface of insights yet to come.

-Grahame Steed, BI Director, BiP

  • Approach: With funding from the Data Lab, exploration of improving search relevance began. Suggestions were made for changes in BiP business intelligence services’ search functionality to use machine learning algorithms that learn from customer interactions. Ongoing work looks to improve relevance, simplify queries and explore novel algorithms.
  • Outcome: Outcome: Additional funding was raised to develop a proof-of-concept, that demonstrated an improved customer experience and was the first step towards a cutting-edge tender retrieval system that BiP believes can transform their business. This was followed by a multi-year engagement using the Innovate UK Knowledge Transfer Partnership (KTP) scheme.

Rakuten Mobile

Using AI to bring autonomy to complex networks

  • Industry: Mobile Network Provider.
  • Benefits: A clear illustration of how evolutionary algorithms can be structured and configured to bring autonomy to complex network applications.
  • Type of engagement: Directly funded research.

Autonomous networks will play a key role in adapting the networks of the future, and we are excited to have the opportunity to collaborate with the world-class team at the University of 一本道 on research into this field.

-Pierre Imai, Head of R & I, Rakuten Mobile

  • Challenge: Modern mobile networks are so complex that responsibility for running them is beyond the capacity of a few individuals, and autonomy is critical to ensure both their smooth, continuous operation and ability to adapt to new, potentially unimagined scenarios.
  • Approach: CIS researchers built a proof-of-concept implementation which demonstrated how AI — in this case, a hierarchy of evolutionary algorithms — could be deployed to control a large-scale complex network.
    Outcome: The team of post-doctoral researchers showcased their model on a real-world content delivery network subjected to a variety of challenging scenarios. The prototype code was delivered to Rakuten Mobile to serve as a blueprint for future development.

Tunstall

Future-proofed data analytics processes for telecare

  • Industry: Healthcare.
  • Benefits: A clearer understanding of how collected data can be used for earlier identification and prevention of adverse events.
  • Type of engagement: Glasgow City District Innovation Zone funding.
  • Challenge: Telecare devices, systems, and users produce vast amounts of data, but we do not know how to use that data to predict or even identify people at risk, or how to best support and future-proof data collection.

The early analysis done by CIS may facilitate previously reactive services to become more proactive and predictive, helping to target care where and when it’s needed most.

-Lucille Whitehead, BDM, Tunstall HealthCare

  • Approach: CIS researchers carried out a scoping review of the literature, examined Tunstall’s existing data using descriptive statistics, and carried out qualitative interviews with social care staff in order to understand first-hand perspectives on how data can be.
  • Outcome: Concrete ways on how to improve and future-proof data collection were identified, and a set of recommendations and actions were compiled going forward.