In recent years, we have seen a dramatic increase in the use of data-centric distributed systems such as global grid infrastructures, sensor networks, network monitoring, and vari...
Time is an important data dimension with distinct characteristics that is common across many application domains. This demands specialized methods in order to support proper analy...
Wolfgang Aigner, Alessio Bertone, Silvia Miksch, C...
We present a probabilistic model-based framework for distributed learning that takes into account privacy restrictions and is applicable to scenarios where the different sites ha...
Abstract Current, data-driven applications have become more dynamic in nature, with the need to respond to events generated from distributed sources or to react to information extr...
Wide-area sensor infrastructures, remote sensors, RFIDs, and wireless sensor networks yield massive volumes of disparate, dynamic, and geographically distributed data. As such sen...
Ranga Raju Vatsavai, Olufemi A. Omitaomu, Joao Gam...