The e-ROSA project seeks to build a shared vision of a future sustainable e-infrastructure for research and education in agriculture in order to promote Open Science in this field and as such contribute to addressing related societal challenges. In order to achieve this goal, e-ROSA’s first objective is to bring together the relevant scientific communities and stakeholders and engage them in the process of coelaboration of an ambitious, practical roadmap that provides the basis for the design and implementation of such an e-infrastructure in the years to come.
This website highlights the results of a bibliometric analysis conducted at a global scale in order to identify key scientists and associated research performing organisations (e.g. public research institutes, universities, Research & Development departments of private companies) that work in the field of agricultural data sources and services. If you have any comment or feedback on the bibliometric study, please use the online form.
You can access and play with the graphs:
- Evolution of the number of publications between 2005 and 2015
- Map of most publishing countries between 2005 and 2015
- Network of country collaborations
- Network of institutional collaborations (+10 publications)
- Network of keywords relating to data - Link
Big Data Stream processing is among the most important computing trends nowadays. The growing interest on Big Data Stream processing comes from the need of many Internet-based applications that generate huge data streams, whose processing can serve to extract useful analytics and inform for decision making systems. For instance, an IoT-based monitoring systems for a supply-chain, can provide real time data analytics for the business delivery performance. The challenges of processing Big Data Streams reside on coping with real-time processing of an unbounded stream of data, that is, the computing system should be able to compute at high throughput to accommodate the high data stream rate generation in input. Clearly, the higher the data stream rate, the higher should be the throughput to achieve consistency of the processing results (e.g. preserving the order of events in the data stream). In this paper we show how to map the data stream processing phases (from data generation to final results) to a software chain architecture, which comprises five main components: sensor, extractor, parser, formatter and outputter. We exemplify the approach using the Yahoo!S4 for processing the Big Data Stream from FlightRadar24 global flight monitoring system.
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