Archived project

AEGIS - Advanced Big Data Value Chain for Public Safety and Personal Security

Goal: AEGIS is an EC H2020 Innovation Action, aiming at creating an interlinked “Public Safety and Personal Security” Data Value Chain, and at delivering a novel platform for big data curation, integration, analysis and intelligence sharing. AEGIS will help EU companies to adopt a more data-driven mentality, extending and/or modifying their individual data solutions and offering more advanced data services (e.g. data cleansing, data integration, semantic data linking) while at the same time attaching value to their datasets and introducing novel business models for the data sharing economy.

Methods: Semantics, Data Analysis, Big Data, Open Data, data analytics, linked data

Date: 1 January 2017

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Project log

Christian Kaiser
added a research item
Ein Fahrzeug ist ein „Hochleistungscomputer auf vier Rädern“ und mit einer heterogenen Sensorik ausgestattet. Die von dieser Sensorik ermöglichte Sammlung von Fahrzeuglebenszyklusdaten erlaubt die Entwicklung völlig neuer Produkte, Services und Geschäftsmodelle. In den USA hat sich in diesem Kontext in Analogie zur Quantified-Self-Bewegung eine lebendige Quantified-Car-Startup-Szene etabliert, welche mit enormen Risikokapitalsummen von teilweise mehr als 20 Millionen USD ausgestattet ist. Diese Entwicklungen zeigen sehr deutlich, wie hoch der Marktwert eines Digitalen Ökosystems für Quantified Car durch Investoren eingeschätzt wird. Vor diesem Hintergrund liefert dieser Beitrag eine Einführung in das Phänomen Quantified Car und analysiert die Geschäftsmodelle der drei Startups Automatic, Mojio und Dash. Eine wesentliche Erkenntnis aus dieser Analyse besteht darin, dass sich die verfolgten Anwendungsszenarien, Dienste und Basistechnologien der Startups durchaus überdecken. Der Beitrag schließt mit einer kurzen Diskussion über den zunehmenden Wettbewerb zwischen der IKT-Industrie und den etablierten Automotive-Branchengrößen über die Vorherrschaft im Aufbau eines Digitalen Ökosystems für Quantified Car.
Christian Kaiser
added a research item
In the age of digital technology cars will have to exceed their former functionality as a tool for transportation to survive as status symbols. One feasible approach is to provide valuable digital services based on car sensor data which currently is used for the sake of driving only. Hence the 'quantified self phenomenon' can be transferred to modern cars-becoming 'quantified cars'. The paper provides insights into the quantified car phenomenon and explores the approaches of car manufacturers and tech start-ups on their journey to develop novel digital services and sustainable business models. At the moment, cars are an ideal playground for innovative US tech start-ups backed with risk capital to establish new ecosystems following the examples of Google and Facebook. In contrast to that, especially German-speaking car manufacturers have been rather reluctant to reap the value of 'their' car operation data in delivering successful digital services to stakeholders. However, two recent reports from 'Verband der Automobilindustrie' (VDA) – the German automotive industry association – suggest that Original Equipment Manufacturers (OEMs) have to hold a stronger position in the future and may limit the capabilities of third parties to freely access car data. If implemented as described in the VDA reports, then the battle for a car data-service-ecosystem will progress to the next round.
Fenareti Lampathaki
added an update
 
Sotirios Koussouris
added a project goal
AEGIS is an EC H2020 Innovation Action, aiming at creating an interlinked “Public Safety and Personal Security” Data Value Chain, and at delivering a novel platform for big data curation, integration, analysis and intelligence sharing. AEGIS will help EU companies to adopt a more data-driven mentality, extending and/or modifying their individual data solutions and offering more advanced data services (e.g. data cleansing, data integration, semantic data linking) while at the same time attaching value to their datasets and introducing novel business models for the data sharing economy.