Scalable Track Analytics

This project is focused on using large historic records of sensor data from port security systems to identify the types of ship present and accurately reflect the sub-types present, i.e., perform classification and clustering in a dynamic context.

Ports deploy radars, cameras and other sensors to maintain awareness of the ships in and around the port. While ships can deliberately communicate their type (e.g., fishing boat, ferry, yacht, tug), some do not. Even those that do fail to fully characterise all the information that might be pertinent to the port (e.g., the type of fishing boat etc). A current project at the University of Liverpool is investigating how to use geometric data structures to create an abstraction of such historic data. Using this abstraction, the project is then performing behavioural classification and clustering. Unfortunately, the current processing chain requires the iterative sequential reprocessing of the data (i.e., iteratively analysing the data from start to finish) and assumes a feed-forward model (i.e., such that the pre-processing that generates the abstraction cannot be refined to improve the classification and clustering).

This project will seek to develop algorithmic approaches that can replace the current sequential and feed-forward processes with alternatives that are better suited to fully exploiting implementation on emerging many cored processing architectures. The project will involve extensive interactions with Denbridge Marine, a small company based near to Liverpool which delivers port security systems to customers around the globe. Denbridge Marine will help provide access to pertinent data, ensure that the research is well focused on challenges that their customers pose and provide a route for the techniques developed to be applied in the context of meeting those customers' needs.

This CDT's focus on using "Future Computing Systems" to move "Towards a Data-driven Future" resonates strongly with two themes of non-academic organisation. In both themes, albeit for slightly different reasons, commodity data science is insufficient and there is a hunger both for the future leaders that this CDT will produce and the high-performance solutions that the students will develop.

The first theme is associated with defence and security. In this context, operational performance is of paramount importance. Government organisations (e.g., Dstl, GCHQ and the NCA) will benefit from our graduates' ability to configure many-core hardware to maximise the ability to extract value from the available data. The CDT's projects and graduates will achieve societal impact by enabling these government organisations to better protect the world's population from threats posed by, for example, international terrorism and organised crime.

There is then a supply chain of industrial organisations that deliver to government organisations (both in the UK and overseas). These industrial organisations (e.g., Cubica, Denbridge Marine, FeatureSpace, Leonardo, MBDA, Ordnance Survey, QinetiQ, RiskAware, Sintela, THALES (Aveillant) and Vision4ce) operate in a globally competitive marketplace where operational performance is a key driver. The skilled graduates that this CDT will provide (and the projects that will comprise the students' PhDs) are critical to these organisations' ability to develop and deliver high-performance products and services. We therefore anticipate economic impact to result from this CDT.

The second theme is associated with high-value and high-volume manufacturing. In these contexts, profit margins are very sensitive to operational costs. For example, a change to the configuration of a production line for an aerosol manufactured by Unilever might "only" cut costs by 1p for each aerosol, but when multiplied by half a billion aerosols each year, the impact on profit can be significant. In this context, industry (e.g., Renishaw, Rolls Royce, Schlumberger, ShopDirect and Unilever) is therefore motivated to optimise operational costs by learning from historic data. This CDT's graduates (and their projects) will help these organisations to perform such data-driven optimisation and thereby enable the CDT to achieve further economic impact.

Other organisations (e.g., IBM) provide hardware, software and advice to those operating in these themes. The CDT's graduates will ensure these organisations can be globally competitive.

The specific organisations mentioned above are the CDT's current partners. These organisations have all agreed to co-fund studentships. That commitment indicates that, in the short term, they are likely to be the focus for the CDT's impact. However, other organisations are likely to benefit in the future. While two (Lockheed Martin and Arup) have articulated their support in letters that are attached to this proposal, we anticipate impact via a larger portfolio of organisations (e.g., via studentships but also via those organisations recruiting the CDT's graduates either immediately after the CDT or later in the students' careers). Those organisations are likely to include those inhabiting the two themes described above, but also others. For example, an entrepreneurial CDT student might identify a niche in another market sector where Distributed Algorithms can deliver substantial commercial or societal gains. Predicting where such niches might be is challenging, though it seems likely that sectors that are yet to fully embrace Data Science while also involving significant turn-over are those that will have the most to gain: we hypothesise that niches might be identified in health and actuarial science, for example.

As well as training the CDT students to be the leaders of tomorrow in Distributed Algorithms, we will also achieve impact by training the CDT's industrial supervisors.