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Paper on Adaptive Sampling and Filtering for IoT accepted at DEBS 2020

A paper by data management systems researchers in the Database Systems and Information Management (DIMA) Group at TU Berlin and the Intelligent Analytics for Massive Data (IAM) Group at DFKI (German Research Center for Artificial Intelligence) has been accepted for presentation at the 14th ACM International Conference on Distributed and Event-Based Systems (DEBS 2020), 13. - 17. July 2020 in Montreal, Canada.

The paper "A Survey of Adaptive Sampling and Filtering Algorithms for the Internet of Things", authored by D. Giouroukis et al. gathers representative, state-of-the-art algorithms to address scalability challenges in real-time and distributed sensor systems. To gather data timely and efficiently, the authors focus on two techniques, namely adaptive sampling and adaptive filtering. The paper outlines current research challenges for the IoT, future research directions, and aims to support researchers in their decision making process when designing distributed sensor systems.

References:

[1] DFKI Intelligent Analytics for Massive Data Group, https://bit.ly/2LKoY4Y.
[2] DEBS 2020, https://2020.debs.org/
[3] "A Survey of Adaptive Sampling and Filtering Algorithms for the Internet of Things," Dimitrios Giouroukis, Alexander Dadiani, Jonas Traub, Steffen Zeuch, Volker Markl, https://bit.ly/3gEAZa6

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