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Database Systems and Information Management GroupApproximate Analysis of massive Data streams with modern Hardware

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Approximate Analysis of Massive Data Streams with Modern Hardware


The Software Campus [1] project ADAM addresses the connection between approximated analysis of data streams and the advantages of modern hardware architectures.

In the current era of the internet of things and industry 4.0, the quantity and velocity of data sources available in form of continuous data streams increases drastically. Keeping data analysis up to this increase is essential for current and future data-driven services like fraud detection or predictive maintenance.

However, data stream analysis is usually performed in clusters consisting of standard server hardware. Analyzing more data in these systems means increasing the size of the cluster which, in turn, increases cost for hardware and power consumption. Furthermore, increasing the size of the cluster demands highest scalability from stream processing systems and used algorithms. Thus, next-generation stream processing systems have to be designed for maximum cost-efficiency and scalability.

The goal of the ADAM project is to develop concepts for cost-efficient and scalable stream processing system based on stream summaries and specialized hardware. Using stream summaries allows for trading off accuracy for more scalable and efficient processing. Evaluating and analyzing these summaries using targeted compute architectures (e.g. FPGAs, GPUs) reduces the amount of required general-purpose hardware and can significantly decrease power consumption.


The ADAM project is funded by the Federal Ministry of Education and Research (BMBF) as part of the Software Campus program, and is supported by Huawei Technologies.

[1] Software Campus

Sponsored by the German Federal Ministry of Education and Research (BMBF), Software Campus (SC) is an executive development program aimed at developing tomorrow’s senior IT executives.

The SC program combines scientific leading-edge research with hands-on management practice in an entirely new and innovative concept. It is directed at outstanding computer science doctoral students who are interested in taking over executive management functions in industry. Awardees lead their own research projects in cooperation with industry partners over a one to two year period.

Kickoff des Jahrgangs 2017

Project Duration: 04/2018 - 03/2020

Supervisor: Prof. Dr. Volker Markl

Industry Partner


Funded by



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