Impact case study database

Simulation and optimisation can significantly improve the competitive position of companies and public sector organisations by reducing their costs as a result of more efficient development, production, procurement, logistics or financial processes. However, the take up of simulation / optimisation software by SMEs and the public sector has been low due to barriers of entry such as hardware prices, licensing costs and required technical expertise. With Prof Kiss and his team leading the research and technical development efforts, CSSP/CFGP, combined with MiCADO, proved efficient to overcome these barriers and has since generated significant economic impact and further efficiency gains for a large number of organisations in Europe.

Impact on SME clients:

By April 2020, 86 SMEs from over 20 European countries – manufacturing, engineering and technology companies that participated in the CloudSME, COLA and CloudiFacturing projects as beneficiaries or as Third Parties – reported the following estimated economic impact as a direct result of applying either CSSP or CFGP, in tandem with the MiCADO secure autoscaling framework: a cumulative yearly turnover increase of over 100 million Euros, the development of over 550 new / enhanced products or services, contribution to the creation of 650 new jobs, and the establishment of 1,100 new business/commercial partners or customers. This economic impact occurred between 2015 and 2020.

These figures are evidenced in the official project reports/deliverables of the CloudSME, COLA, and CloudiFacturing projects that were provided by executives of the participating companies and approved by the European Commission [a, b, c, d]. This approval affirms the credibility and accuracy of the forecasted economic growth by these 86 companies, as attributable to the CPC’s research across these three projects. Figures 1, 2 and 3, on pages 4 and 5 below, summarise the above detailed numbers.

Figure 1 summarises the impact of CSSP on 24 companies within the CloudSME project. Figure 2 provides details of the economic impact of utilising MiCADO with the CSSP by 10 companies within COLA. Figure 3 encompasses the impact of the CFGP on 52 companies that were supported in three waves during the CloudiFacturing project (please note that due to the very large number of companies, only cumulative figures are shown here, details are available in [c,d]).

Additionally, these companies reported improved business processes, improved business practices, reduction in time to market, and improved customer satisfaction. Two online videos made by Hobsons Brewery [e], a UK-based craft-brewer, and Podoactiva [f], a Spanish manufacturer of tailored foot insoles, provide narrative accounts detailing these impacts.

Beyond the directly reported figures, it is also important to note that significant economic impact has been generated for the clients of the directly supported / involved companies, across a wide range of sectors. For instance, the Managing Director of Saker Solutions, which provides “ a range of 3D simulation and modelling services” to clients within industrial and commercial sectors and is one of the 86 SMEs mentioned above, states the company itself benefitted from “ an increase in turnover of at least £200k pa since 2014 which has allowed us to employ additional staff” and also estimates that “over the last 7 years we have generated some £2 to £3 million of benefit” for their clients: “Primarily this benefit would be in the Nuclear sector but savings will have also been generated within Manufacturing, Defence and Retail” [g]. He concludes that: “ Overall, Prof Kiss has had a major impact on our company” [g].

Impact on CloudSME UG; the commercial service provider:

CSSP/CFGP is primarily marketed and provided by CloudSME UG, a for-profit start-up company that also offers related expertise and consultancy to clients. Established in January 2016, the company’s profits have substantially grown through its continued collaboration with the Westminster researchers, as the Managing Partner confirms: “the product and service portfolio offered by CloudSME UG is based and heavily relying on novel and innovative technologies that were invented, designed and implemented by Prof. Kiss and the Centre for Parallel Computing research team” [h]. The technological and research contribution of the CPC in the creation of CSSP was also acknowledged via the appointment of Kiss as the Chair of the Scientific Advisory Board of the company. In this unpaid role, Kiss acts as their main scientific and technology consultant and helps to define their current and future direction. The company made steady growth on the market between 2016 and 2020 by securing and successfully delivering its commercial contracts. As a result of utilising the CSSP, CloudSME reported 222,701 Euros turnover in 2019, with 39,823 Euros annual profit [h].

As CloudSME’s CEO states, since July 2019, “ the product portfolio of my company [CloudSME] has been further extended and we are now offering fully managed professional cloud-based services utilising MiCADO” [h]. As part of this activity German Data Center HKN, their business partner, is offering fully managed enterprise data clusters based on MiCADO. CloudSME expects an additional contribution of 387,030 Euros to its turnover by the end of 2020 as a direct result of commercialising MiCADO, as evidenced in document [i].

Impact on Government and Charity Sectors:

Within the COLA project public sector organisations, namely the Aragon Local Government (ALG) in Spain and [text removed for publication], a charity organisation funded by the UK Arts Council, have also benefitted from the CSSP coupled with MiCADO [b]. These organisations run data intensive web applications for which resources are managed and optimised by MiCADO.

Applications offered by ALG via its public company SARGA – which “ acts as manager of all public infrastructures and services related to agriculture and environment” – utilise the CSSP and MiCADO, and the technical solutions provided by the CPC team “ *have improved the recommendation engines of the Aragón Government in order to provide better services to the people of Aragón and to result in higher general satisfaction within the people of the region” [j]. SARGA and ALG representatives specify that: “ Based on the technical solution invented and developed by Prof Kiss and his team, the operational organization of our IT infrastructure have improved significantly” such that *“we have achieved more efficient processes related to software development and public services delivery” [j].

By July 2019, [text removed for publication] had achieved an estimated increase of 100 (10%) new business partners/customers, 212K Euros (7.5%) increase in turnover, 20% more efficient business processes, 3% increase in employment, 50% reduction in time to market, 10% increase in customer satisfaction, and 4 (15%) new service offerings as a result of applying MiCADO [b]. The company’s planned exploitation of CSSP and MICADO is expected to have increased, by Jan 2022, to 200 (20%) new business partners/customers, 1.35 million Euros (15%) increase in turnover, 30% more efficient business processes, 6% increase in employment, 50% reduction in time to market, 30% increase in customer satisfaction, and 8 (30%) new service offerings, as a result of applying CSSP and MiCADO [b].

Figure 1 - Reported impact figures by 24 companies in CloudSME on 31/03/2016

Figure 2 - Reported impact figures by 14 partners (10 companies) in COLA on 31/08/2018

Figure 3 - Reported cumulative impact figures by 52 companies in CloudiFacturing on 31/03/2019

Impact 1: Professional Practice - Open Source and Standardisation

Since August 2014, the MPJ specification has been included in the core distribution of the widely used Open MPI (Open-Source Message Passing Interface) software environment. Developed and maintained by a consortium of research, academic, and industry partners, Open MPI is a suite of open-source software libraries implementing the MPI standard for High Performance Computing (HPC) [a-i]. The Java binding developed in Open MPI is based on the ground-breaking results of the International MPJ Working Group chaired by Vladimir Getov (output [1]) and incorporate the four ETSI standards that resulted from the GridComp project (outputs [4], [5] and [6]). The Open MPI team have highlighted that these ‘standardized classes’ of Java are of benefit to an open-source library as they ‘provide a platform-independent way to access host-specific features such as threads, graphics, file management, and networking’ [a-ii, p.4 & 2]. In other words, these standardised classes guarantee full compatibility and portability across a wide variety of platforms and applications.

In recent years, Open MPI has become the most popular MPI library, as is evidenced by the MPI International Survey, which received over 800 user responses from 42 different countries between Feb & July 2019 [a-iii, p.1]. Of the MPI users identified, 85.8% (718 respondents) use Open MPI [a-iv]. As such, through its inclusion in this open-source library, MPJ has been reaching a wide range of users, 80% of which belong to non-profit organisations, including government institutes, as well as software and hardware vendors, with the use of Java being especially popular in the UK and Russia [a-i, p.13 & 16].

Regarding the significance of this wide range of users, the Open MPI team explain that MPJ was added as it is one of the three key parallel computing technologies upon which HPC depends and has several benefits. For instance, it ‘provides efficient built-in support for threads’ which increases the software development productivity. In addition, ‘some numerical libraries are based on multithreading’ and MPI itself ‘can benefit from Java because its widespread use makes it likely to find new uses beyond traditional HPC applications’ [a-ii, p.1-2]. As such the primary impact on professional practice relates to providing easy access to Java binding for programmers who can exploit these benefits (output [2]). Secondary impacts arise through specific examples of such usage of MPJ via Open MPI. For instance, the PPF library for parallel particle filtering applications allows application programmers to write quickly and easily shared- and distributed-memory PPF codes in Java [a-v], producing the benefit of ‘ reducing the cost of traditional particle filters by approximating the likelihood with a mixture of uniform distributions over pre-defined cells or bins.’

Impact 2. Economic Impact

An example of the significant economic impact on users of Prof Getov’s innovative component based MPJ development methodology is its contribution to ‘the long-term research and development collaboration for IBM’s Cloud Computing agenda and strategy’, through knowledge exchange activities at IBM Research Centers in Zurich, Almaden, Watson, and Dublin, as well as direct collaborative research [b]. Dr Peter Buhler, Head of Computer Science at the Zurich Laboratory states that this has resulted in their ‘using and developing further the adopted component-based methodology and development platform which has [in turn] influenced the professional practice in building complex applications for grid and cloud systems’ at IBM [b].

For instance, Buhler [b] confirms that IBM Watson was ‘designed by reusing the principles developed and standardized by the European Telecommunications Standards Institute as part of Vladimir Getov’s work’ (output [5]). This supercomputer is a unique artificial intelligence system capable of answering questions posed in natural language and its economic significance to IBM is indicated by its global market share of 16.1% placing it amongst the three dominant systems in the Machine Learning category [d-i]. IBM Watson has been used by nearly 2,800 companies as of July 2020 due to its usefulness across a variety of application domains. Statistics by revenue show that 39% of Watson users are large companies (>$1000M), 37% are small (<$50M), and 16% are medium-sized, and that they cut across different sectors of industry – the largest being Computer Software (22%), Hospital & Health Care (14%), Higher Education (8%), and Information Technology and Services (7%) [d-i].

Buhler also cites ‘the Biometric Identification System application developed in collaboration with Vladimir’s DIS RG team and delivered by IBM Research – Zurich Laboratory’ (Fig. 1), as having a ‘significant economic impact’ through its producing ‘quicker return on investment […] achieved because of the much higher productivity and shorter development cycle provided by the invented component-based methodology and development process’ [b]. This biometric identification system contributes to the Global Technology Services segment of IBM’s business, which in 2019 comprised the largest share of IBM's total revenue at nearly 36% ($27.4 billion) [c-ii]. The use of Getov’s component based MPJ framework provided ‘pre-built template components’ from which a system can be built that ‘guarantee[d] real-time biometric identification functionality over a very large, constantly growing database of enrolled identities (fingerprints)’ [b]. Furthermore, Getov’s ICBE (outputs [4], [5] and [6]) ‘has been instrumental in continuously monitoring and maintaining the system’ [b], which has been a significant aspect of IBM’s commercial offering to corporations and governments [c-i].

Fig. 1: Security Biometric Identification System [c-iii]

Another company that has economically benefitted from Getov’s innovative component-based MPJ methodology, as well as the underpinning ETSI standards, is ActiveEon – a France-based software company that provides innovative open-source solutions for IT automation, acceleration and scalability, big data, distributed computing and application orchestration. During the GridCOMP project (06/2006 – 02/2009), Getov and colleagues had initially demonstrated the usefulness of the component based MPJ development methodology to industry by using it to wrap and Grid-enable aerodynamic wing modelling software at ActiveEon, and to prove the integration of data staging for the input and output files into this sweeping / optimisation process for any given configuration.

As Prof Denis Caromel, CEO of ActiveEon, states, since then the company ‘has been directly exploiting the results of the project, including the GCM and MPJ framework (output [3]), particularly in its ProActive workflows and scheduling open-source middleware which has been used for delivering solutions for a range of commercial customers’ [e-i]. Caromel further specifies the economic impact of this adoption of Getov’s innovations: ‘over the years since August 2013, ActiveEon has achieved revenue growth of 18M USD starting with less than 1M USD in 2013 and reaching revenue of 19M USD in 2020. Our number of employees has been increasing significantly over the same period from 30 employees at the end of 2013 to 106 employees in 2020. Those successful results would not have been possible without Professor Getov’s direct contribution to the component based MPJ development methodology and ETSI standards adopted and used by ActiveEon throughout the 13 years of its existence. His work has reduced substantially the return of investment cycle of complex distributed applications’ [e-i].

Impact 3. Social Impact

Beyond the significant economic impact Vladimir Getov has made with his contributions, Buhler explains that ‘[t]he technical advancements he [Getov] has invented and co-invented are the basis for many Web-scale applications we have at our fingertips today’, creating a ‘resulting impact on the society’ [b]. IBM specify three key social impacts of their cognitive technology in lengthy case studies featuring real-world usage of the IBM Watson supercomputer that would not exist without Getov’s research [d-ii to d-iv]. The areas of interventions listed below provide typical examples:

• Transforming social services by providing ‘easier-to-access, more personalized services [that] can help individuals at risk better manage their own well-being, getting the right support when they need it’ [d-ii]. A specific use case is ‘Aspiranet, which currently serves 22,000 youth and families across California, [and] is using natural language inquiry of unstructured data to help youth transition from foster home care to living on their own’; their CEO confirms ‘cognitive technology helps free up caseworkers’ time, enabling them to focus on what matters most, human connection’ [d-ii]. This is a significant impact given that in the ‘United States, social worker turnover is as high as 90 percent per year, with heavy workloads, including paperwork, as a major contributing factor’ [d-ii].

• Providing environmental protections: IBM gives the example of their work with the Beijing Environmental Protection Bureau to reduce air pollution in China (the cause of more than one million premature deaths per year) via the 2014 Green Horizons initiative, which ‘addresses these challenges by using advanced machine learning to identify smaller sections of the city that are at risk. Along with trade-off analyses, this enables more targeted mitigation actions, such as shutting down targeted industries, while minimizing socioeconomic disruptions’ [d-iii].

• Enhancing public safety: IBM gives the example of how they used Watson to anticipate and respond to the Oct 2015 Hurricane Patricia (one of the strongest storms ever recorded): ‘weather prediction models based on artificial intelligence (AI) provided advanced warning to an IBM production center in Guadalajara. The system had analyzed huge volumes of disparate information, including weather data, social feeds and news reports to get a comprehensive view of the storm's trajectory. […] Early warning gave site officials the crucial time they needed to act’ and they ‘opted to evacuate the site as a precautionary measure’ [d-iv].

Another example of social impacts that have been created through the adoption of Getov’s innovations is found in the work of ActiveEon. As mentioned earlier, ActiveEon has used Getov’s component based MPJ development methodology in their ProActive Workflows & Scheduling software which incorporates dynamic and autonomous workload allocation to an elastic pool of resources using multi-language software components within the HPC cloud-based workflows [e-i] [e-ii, p.30]. Among a range of case studies demonstrating usage of this software across sectors including mining, satellite technology, visa processing, and solvency solutions [e-iii], is its use for microbiome analytics orchestration [e-iv, p.18-20]. The enabling of microbiome analytics orchestration at this scale is of significant benefit to the biomedical field as microbiome innovation encompasses pharmaceutical applications such as personalised medicine and nutrition, identification of the impact of drugs on the gut microbiome, and identification of bacterial biomarkers associated with dysbiosis (microbial imbalance) [e-iv, p.8].

Changing practice at Transport for London (TfL)

By 2009 it had become apparent that CCTV recordings in London were too often of limited usefulness in the investigation of crime. A key reason for this lack of usefulness was that CCTV system providers to TfL set their cameras in a non-standardised manner, resulting in the CCTV cameras on board its London buses producing visual imagery of varying quality. It was revealed by the Metropolitan Police Service (MPS) that, despite the high expenditure on CCTV in London (£200m across 10 years), “for every 1,000 cameras in London, less than one crime is solved per year” [a-i].

Thus, with the participation of the TfL and the funding of a PhD studentship by the Home Office’s Scientific Development Branch (HOSDB), Triantaphillidou and her colleagues undertook an applied research project aimed at addressing CCTV quality in this context, for the first time, resulting in the standardisation of CCTV recording on London buses.

On the basis of their investigation, the team produced a set of standards that would ensure facial recognition from CCTV recordings of bus passengers within the variety of sitting/standing scenarios and lighting conditions relevant to London Buses. It also established a methodology to derive such standards, as described in output [1]. TfL implemented the Westminster’s team recommendations for CCTV recorded video in all London buses in 2014. Andrew Hyman, the Analytics Research and Data Outreach Manager at TfL, confirms that the research team’s “ findings were used to set minimum CCTV quality levels for the CCTV fitted to all new buses. This was implemented through the ITT Bus Build Specification, which sets out the bus build standards for all new buses as part of the requirements for all bus route tenders. This is mandatory” [a-ii]. This change in practice is such that, as of April 2020, the TfL have approximately 9,000 buses in the fleet that have been fitted with CCTV cameras on the basis of these specifications [a-iii].

The significance of this impact on the relevant TfL London bus practices is that human faces can now be consistently, successfully, identified from CCTV footage across all of the TfL buses, allowing it to function as a vital tool in the investigation of crime. Writing in 2018, Hyman notes that “[g]ood CCTV significantly increases the chances of detection” and that the TfL’s “Bus CCTV is currently recognized as some of the best quality CCTV covering the public domain, which is one reason why the request for it continues to rise annually” [a-ii]. This increase in demand is confirmed by the MPS who provided the following information [a-iv], which encompasses all bus operators that must use the TfL specifications:

The increase in MPS demand for access to TfL bus CCTV footage demonstrates the increasing usefulness of this high-quality imagery in police investigations. While neither the MPS nor the Crown Prosecution Service record data on the number of successful prosecutions that use CCTV within its evidence [a-iv, a-v], several high-profile cases demonstrate the usefulness of Triantaphillidou’s optimised recording standards for obtaining convictions.

Example 1: Demonstrating the effectiveness of the CCTV specifications at night time, a video recording played a vital role in the conviction of four teenagers for their homophobic attack on a lesbian couple in July 2019, which made global headlines following the release of the image of the bloodied women. The judge referred to the CCTV video several times in her judgement, stating that it proved that the suspects “quite clearly targeted this couple” [a-vi].

Example 2: The quality of the CCTV imagery, as tested under wide-ranging conditions, was such that the police were able to trace a suspect who stamped a person to death on a bus in March 2018. The camera that was positioned to film the exit door of the bus caught the suspect pressing his hand against the glass (see image on the right [a-vi]), allowing the police to lift finger prints from it, leading to his identification, arrest, and eventual conviction [a-vi].

As such, the impact on TfL practice regarding their CCTV recording has also had a positive impact on the organisations

seeking to solve crime and prosecute criminals by enabling them to pursue these aims in an effective manner.

Changing practice at an imaging start-up

A secondary impact of the above engagement relates to the upskilling of a researcher who would contribute methodologies and material from the TfL project to the development a UK SME (small or medium-sized enterprise), which in turn led to its purchase by Apple Inc.

Dr Anastasia Tsifouti, a then BSc graduate of Westminster and HOSDB employee , undertook the aforementioned HOSBD-sponsored PhD studentship at Westminster under the supervision of Dr Triantaphillidou and aided in the successful enhancement of TfL CCTV, as described above. In Spring 2018 Dr Tsifouti joined Spectral Edge, a SME that focusses on improving pictures and videos on mass market devices on a pixel-level, via embeddable technology that can be implemented purely in software or in silicon and through techniques incorporating machine learning in real time [b-i].

Spectral Edge benefited from the methodologies and data obtained from the TfL project, which were used to enhance the company’s approach to computational photography. This occurred both through the skills and knowledge Tsifouti brought to the company from the Westminster project, and Triantaphillidou’s three-day consultancy on the image quality framework of Spectral Edge’s SE Fusion technology in January 2019 [b-ii]. Subsequently, the TfL work featured in Spectral Edge’s 2019 white paper: RGB+IR Real Time Fusion for the Security and Surveillance Industry [b-iii]. The pre-2014 bus CCTV images analysed by the Westminster team feature on page 12 of the white paper, which also communicates how Tsifouti’s skills in optimising underexposed, overexposed, and mixed illumination image rendering were used in the development of their flagship SE Fusion technology, which “ensures minimal loss of resolution, while delivering improved contrast, dynamic range and signal-to-noise ratio” within its image capture process [b-iii, p.12].

In November 2019, Apple Inc. acquired the SME, with Bloomberg suggesting that “Spectral Edge’s technology could contribute to the AI [Artificial Intelligence] Apple already uses in its Camera app by continuing to improve the quality of photos in low-light environments” [b-iv]. The specific ways in which the SpectralEdge innovations have been incorporated into Apple devices cannot be confirmed due to issues of high confidentiality. Nonetheless, following this acquisition the skills base of SpectralEdge staff, which has been positively impacted by Westminster researchers, has fed into their role within Apple’s Cambridge-based Camera and Photos Team, which “provides innovative algorithms and image processing solutions for all of Apples world-class devices which include the most successful imaging product, iPhone as well as other camera related systems” [b-v]. Dr Tsifouti, now an Apple Inc. employee, attests to the importance of Dr Triantaphillidou to her development within this unique field: “Image quality is an important field in the development of any technology or investigation that relates to imaging. It is an interdisciplinary field, not very well understood by non image quality scientists. In addition, there are not many image quality scientists in the UK. Dr Triantaphillidou has been a great help to my development, throughout my career, as an image quality scientist. Even very recently, in 2019, I requested her consultation on image quality metrics, when I was working for a startup (SpectralEdge) that was sold to Apple” [b-vi].

Changing practice at Huawei

In their collaboration with Huawei Technologies Co Ltd, China, the Westminster researchers proposed and implemented a successful verification methodology that connected image quality prediction models with customer experience rating methods. Success in this area is confirmed by output [6] and testimony which specifies two specific changes of practice that have resulted in the impact of creating efficiency and cost-savings within the R&D process of Huawei’s Camera Testing Team: [Text removed for publication] [c-i] [c-ii].

The optimisation of camera image quality on the basis of robust predictions of customer preference, as achieved via the intervention of Triantaphillidou and her team, is of key significance for Huawei’s commercial strategy in this area. This is because their ever-increasing market share in the smartphone sector is driven by their constant technological improvements to their phone cameras. Huawei is second to Samsung in regard to global market share and is the leader of the China market at 42% / 97.8 million shipments in 2019, a 66% increase on the previous year [c-iii]. As The Verge explains, this growth is in spite of the smartphone market “experiencing its own form of recession” in recent years, with Huawei being an “exception” because they have “consistently made huge strides between every device release. The company has invested heavily in its camera hardware, which has paid off with terrific performance (currently unmatched in low light) and has stirred smartphone owners to hit the ‘upgrade’ button” [c-iv]. As such, Prof Triantaphillidou and her fellow University of Westminster researchers are impacting upon Huawei’s continued commercial expansion in the smartphone market by ensuring they remain the key innovators in the area of camera phone hardware.