DisDaS: Verteilte Datenströme in dynamischen Umgebungen

• Antragsteller: Professor Dr. Friedhelm Meyer auf der Heide
• Fachliche Zuordnung: Theoretische Informatik
• Förderung: Förderung von 2014 bis 2021
• Projektkennung: Deutsche Forschungsgemeinschaft (DFG) - Projektnummer 254953735

We currently observe rapidly growing interest in large systems of devices, each of which permanently observes data that has - often in real time - to be aggregated to useful information. Examples are (1) information gathered by the smartphones of world-wide distributed users, (2) sensor data gathered by cars, (3) local usage data of links collected by Internet Service Provider, or (4) sensors data gathered in (maybe mobile) sensor networks. The aim is to aggregate useful information from such distributed streams of observed data items. Examples for aggregation are (weighted) average, minimum, maximum of measured temperature. In this project we plan to lay the foundations for the design and analysis of distributed algorithms that continuously compute aggregated information of streams of data which are observed by a multitude of devices. These devices may be mobile, i.e. capable of moving in the plane or in space, and contain both (wireless) communication devices and sensors for observing their environment. The major challenge we have to cope with is that data streams are too big and arrive too fast to be completely stored or processed in real time. Thus we have to find ways to extract useful information from the streams using restricted resources like memory, communication volume and computation time. We plan to develop continuous distributed algorithms in dynamic environments, taking both mobility of the devices and of the observed events into account.

• DFG-Verfahren: Schwerpunktprogramme
• Teilprojekt zu: SPP 1736: Algorithmen für große Datenmengen