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An analytical process is provided, which analyzes the recent GPS traces of a vehicle and assigns to it a probability of having a crash in the next mid-term future (typically, one month ahead).  The process is based on the analysis of historical data of several vehicles in terms of mobility (

A tool for analyzing the GPS traces of a vehicle to identify the places where a user stops for significant durations, and to aggregate them into locations of interest for the user. Each location is enriched with statistical information about frequency of stops.

Trajectory clustering is an important operation of knowledge discovery from mobility data. Especially nowadays, the need for performing advanced analytic operations over massively produced data, such as mobility traces, in efficient and scalable ways is imperative.

The automotive after sales market is 44bn € in Germany alone. Our spare part recommendation engine allows car repair shops to deliver value with less human labor (increasing efficiency), allowing for higher value activities.

Ciclogreen has developed an analytics and prediction tool that, given the historical data of the traffic flow at a given point in the city, will simulate behaviors that could generate congestion or flow of traffic, public transport, and pedestrians.

The key concept proposed in the DEEP Hybrid DataCloud project is the need to support intensive computing techniques that require specialized HPC hardware, like GPUs or low-latency interconnects, to explore very large datasets.

Current trends towards the use of big data technologies in the context of smart cities suggest the need for developing novel software development ecosystems upon which advanced mobility functionalities can be developed.

AGORA is the B2B data platform broker of Atos to connect Data Providers and Data Consumers, facilitating the access, acquisition and trade-off of Connected Vehicle and Smart Home data under the standardized data model (CIDM).

Big data technologies are nowadays being integrated in systems that are required to process a vast amount of information from geographically distributed data sources.

Key motivation of Cross-CPP project is to give cross-sectorial industries access to the great spectrum of sensor data coming from products from various industrial sectors (vehicles, smart home devices, etc.).