Digitalization has become an important driver of innovation in the automotive industry. For instance, the Quantified Self-movement has recently started spreading to the automotive domain, resulting in the provision of novel digital vehicle services for various stakeholders such as individual drivers and insurance companies. In this direction, a growing number of ICT start-ups from outside Europe have entered the market. Their digital vehicle services are grounded on the availability of vehicle Big Data. Hence, to better understand and capture this ongoing digital transformation, we introduce the Vehicle Data Value Chain (VDVC) as a lightweight model to describe and examine digital vehicle services. Furthermore, we classify current digital vehicle services offered by four start-ups and five car manufacturers by applying the VDVC, thereby identifying commonalities and differences within three crucial steps: data generation, acquisition, and usage. Additionally, we apply the VDVC to describe a digital mobility service provided by a European industry consortium. This exemplary application serves to evaluate the VDVC and show its general applicability in a practical context. We end our paper with a brief conclusion and an outlook on various current activities of standardization organizations, the European Commission and car manufacturers related to the future of vehicle services.

Local regression models are one of the backbones of spatial analytics. Computational scalability of such models can be resolved with a distributed implementation which requires truly local modelling as communication between components is limited or absent at some stages. The use of such models in a streaming context provide further restrictions. A calibration procedure has to be truly incremental, with constant memory and processing time for any sample in a stream. This paper explores a spatially distributed incremental local regression model satisfying these requirements and providing similar functionality in terms of interpretability and modelling accuracy as the widely-used geographically weighted regression. Our experiments were run on a conventional mid-range 8-core server. In the largest scale experiment we processed a stream of 157 million spatially referenced samples simulating power consumption readings taken every 2 hours in a period of 5 months from about 87000 households in a European country. The software implementation we developed for the evaluation and performance analysis is made available as an open source project.

Nonblocking collective communication operations are currently being considered for inclusion into the MPI standard and are an area of active research. The benefits of such operations are documented by several recent publications, but so far, research concentrates on InfiniBand clusters. This paper describes an implementation of nonblocking collectives for clusters with the Scalable Coherent Interface (SCI) interconnect. We use synthetic and application kernel benchmarks to show that with nonblocking functions for collective communication performance enhancements can be achieved on SCI systems. Our results indicate that for the implementation of these nonblocking collectives data transfer methods other than those usually used for the blocking version should be considered to realize such improvements.

An extensive analysis based on the use of different morphological filters for the classification of very-high resolution panchromatic images is presented. Feature selection on high-dimensional input space is performed using recursive feature elimination, a support vector machines specific method performing backward elimination based on margin-estimation criterion. Experimental results on an eight-classes image of Las Vegas (USA) confirmed the effectiveness of the analysis pointing out the relevancy of the most contributing morphological features which resulted in high classification accuracy using panchromatic imagery.

The discovery of enhanced magnetoresistance and oscillatory interlayer exchange coupling in transition metal multilayers just over a decade ago has enabled the development of new classes of magnetically engineered magnetic thin-film materials suitable for advanced magnetic sensors and magnetic random access memories. Magnetic sensors based on spin-valve giant magnetoresistive (GMR) sandwiches with artificial antiferromagnetic reference layers have resulted in enormous increases in the storage capacity of magnetic hard disk drives. The unique properties of magnetic tunnel junction (MTJ) devices has led to the development of an advanced high performance nonvolatile magnet random access memory with density approaching that of dynamic random-access memory (RAM) and read-write speeds comparable to static RAM. Both GMR and MTJ devices are examples of spintronic materials in which the flow of spin-polarized electrons is manipulated by controlling, via magnetic fields, the orientation of magnetic moments in inhomogeneous magnetic thin film systems. More complex devices, including three-terminal hot electron magnetic tunnel transistors, suggest that there are many other applications of spintronic materials.