@article{Grimmer2018b,
title = {Exploids: Host-basierte Angriffserkennung auf Linux-VMs},
author = {Martin Grimmer and Jörn Hoffmann and Martin Max Röhling},
editor = {Computec Media GmbH},
url = {https://www.linux-magazin.de/ausgaben/2018/03/intrusion-detection/},
year = {2018},
date = {2018-03-01},
journal = {Linux Magazin},
volume = {03/2018},
abstract = {Forscher untersuchen, wie sich Hackerangriffe schnell erkennen und erfolgreich abwehren lassen und wie Admins Spuren sichern können, die später bei einer juristischen Ahndung helfen. Trotzdem soll die Privatsphäre der Anwender aber geschützt bleiben.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{Hirmer2017,
title = {The First Data Science Challenge at BTW 2017},
author = {Pascal Hirmer and Tim Waizenegger and Ghareeb Falazi and Majd Abdo and Yuliya Volga and Alexander Askinadze and Matthias Liebeck and Stefan Conrad and Tobias Hildebrandt and Conrad Indiono and Stefanie Rinderle-Ma and Martin Grimmer and Matthias Kricke and Eric Peukert},
url = {https://doi.org/10.1007/s13222-017-0263-8},
doi = {10.1007/s13222-017-0263-8},
issn = {1610-1995},
year = {2017},
date = {2017-11-01},
journal = {Datenbank-Spektrum},
volume = {17},
number = {3},
pages = {207--222},
abstract = {The 17th Conference on Database Systems for Business, Technology, and Web (BTW2017) of the German Informatics Society (GI) took place in March 2017 at the University of Stuttgart in Germany. A Data Science Challenge was organized for the first time at a BTW conference by the University of Stuttgart and Sponsor IBM. We challenged the participants to solve a data analysis task within one month and present their results at the BTW. In this article, we give an overview of the organizational process surrounding the Challenge, and introduce the task that the participants had to solve. In the subsequent sections, the final four competitor groups describe their approaches and results.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{Kricke2017b,
title = {Preserving Recomputability of Results from Big Data Transformation Workflows},
author = {Matthias Kricke and Martin Grimmer and Michael Schmeißer},
url = {https://doi.org/10.1007/s13222-017-0265-6},
doi = {10.1007/s13222-017-0265-6},
issn = {1610-1995},
year = {2017},
date = {2017-11-01},
journal = {Datenbank-Spektrum},
volume = {17},
number = {3},
pages = {245--253},
abstract = {The ability to recompute results from raw data at any time is important for data-driven companies to ensure data stability and to selectively incorporate new data into an already delivered data product. However, data transformation processes are heterogeneous and it is possible that manual work of domain experts is part of the process to create a deliverable data product. Domain experts and their work are expensive and time consuming, a recomputation process needs the ability of automatically adding former human interactions. It becomes even more challenging when external systems are used or data changes over time. In this paper, we propose a system architecture which ensures recomputability of results from big data transformation workflows on internal and external systems by using distributed key-value data stores. Furthermore, the system architecture will contain the possibility of incorporating human interactions of former data transformation processes. We will describe how our approach significantly relieves external systems and at the same time increases the performance of the big data transformation workflows.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}