Big data at the service of public safety and security
An EU-funded consortium has developed an innovative solution to improve public safety and personal security by helping organisations, businesses and citizens harness the benefits of big data more easily and efficiently.
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The term big data refers to datasets that are so large, fast-changing or complex that it is difficult for businesses and organisations to process them using traditional methods.
The EU-funded AEGIS project has successfully lowered the barrier to working with such vast amounts of information from diverse sources, across different formats, structures and languages. Its open-source, cloud-based infrastructure opens up many potential use cases and new business models, from helping insurers and policyholders process and even pre-empt claims, to supporting elderly people to maintain their independence for longer.
Public safety and personal security covers many important social and economic areas, including accidents or criminal acts, food safety, homeland security, emergency response, natural disaster management and environmental safety, says project coordinator Yury Glikman of Fraunhofer FOKUS in Germany. This diversity of sectors, domains, languages and data sources, and especially the lack of a common data structure, has been one of the main challenges to developing effective big data services for public safety and security.
Extracting meaning from data
Big data in this context could be sourced from wearable fitness, health or mobile devices. It might come from smart city systems, environmental sensors or smart home platforms. Or it could be extracted from public and private online databases, websites or social media platforms.
Designed to provide configurable, scalable big data infrastructure as a service, the AEGIS system can aggregate and model all these data types and tag the information to extract meaning, supported by an innovative metadata service that processes multiple layers of contextual, structural and syntactic data. A blockchain-based data policy framework ensures data security, integrity, privacy and rights management for a reliable data exchange.
The system is designed to offer users more accurate, evidence-based insights into events past, present and through predictive analytics even provide an indication of what might happen in the future. These insights can be used to support the development of improved decision-support models and generate new business opportunities on top of the big data value chain, focused on real-time data collaboration, knowledge sharing and notification services.
Personalised early warnings
A pilot demonstrator developed with Italian insurance firm HDI Assicurazioni includes applications to provide customers with personalised early warnings for asset protection. For instance, it can alert policyholders to forecasts of severe hailstorms where they live or work, while supporting the insurers data scientists in assessing risks and financial liabilities in a given area.
Another demonstrator uses big data to generate new services for safer driving and safer roads, which could include alerting public infrastructure authorities to poor road maintenance and accident hot spots, in addition to warning drivers of adverse conditions.
Other data integrations via the AEGIS tools would support assisted-living services for the elderly or other vulnerable groups by enabling social or healthcare providers to make use of big data-driven insights.
It could provide added-value services to vulnerable individuals, including proactive and reactive safety solutions, smart notifications and personalised recommendations that can facilitate informed decision-making, either by the individuals themselves or by their care providers. Coupled with smart home systems to maintain comfort, well-being and safety, and wearable health and activity monitoring devices, the solution could prolong independence and quality of life.
The demonstrators were used in the project to evaluate and improve the AEGIS technology, although at the same time they each have business potential on their own, Glikman says. The project partners are continuing to use and improve them.
One partner, Kungliga Tekniska Högskolan in Stockholm, has founded a spin-off company called Logical Clocks that is further developing several of the core components of AEGIS.
A key advantage of the AEGIS solution is that it provides a data analytics and collaborative data-management environment that is accessible via a web browser, eliminating the need for users to have their own high-performance computing infrastructure. As it is an open source solution, any company can install it on a public or private cloud server.
AEGIS enables SMEs or individuals to start working with big data without big investments in infrastructure, independent of the application domain or sector. As a technology, it therefore reduces the barrier to start working with big amounts of data, Glikman says. It is a valuable contribution to the adoption of big-data technologies, although the availability of big data remains a key bottleneck to achieving potentially very high social and economic benefits.