Prof. Dr. Emmanuel Müller
Knowledge Discovery and Data Mining, Hasso-Plattner-Institut, Universität Potsdam
Studierende aller Fächer, die Herausforderungen im eigenen Feld haben. Ange-
strebt wird der Austausch von interdisziplinären Ideen und Methoden.
Big Data Analytics
Data is ubiquitous in industrial, scientific and government processes. Data is collected as
observation of natural phenomena, data is created from simulation models and experiments,
data is used for verification of complex processes, and data is used to guide strategic decisi-
ons. Overall, data has a significant influence on the quality of our daily life. However, lack of
advanced data mining technology is in clear contrast to the value of data in many application
domains. Furthermore, data mining does not end with the execution of algorithms. With data
mining algorithms, resulting in the discovery of unknown, novel, and unexpected patterns,
one should aim at assisting humans in their daily decision making.
With big data analytics we enable an alternative data-driven process for decision makers by
focusing on the extensive exploitation of available data. With computer science research we
focus on the open challenges in data understanding: We study new data mining concepts
that assist humans in their daily decision making. We develop novel algorithms for sensor da-
ta streams, high dimensional databases, graph data, and other heterogeneous data resour-
ces. In all these research fields we focus on the inclusion of the human domain expertise into
our data mining methods. This allows humans to steer this data analysis process to novel
data-driven hypothesis and a comprehensive understanding of their data.
The working group will cover basic technologies in big data analytics. Such methodologies
are of major interest for scientists in different research fields and widely applicable to indus
trial settings. The working group will give an introduction to the knowledge discovery pro-
cess, efficient data mining algorithms, open source data mining tools, as well as interactive
data exploration. The working group is open for challenges that students have in their own
field. We envision an exchange of interdisciplinary ideas and methods for this course.
ab dem 5. Semester und Doktoranden
20. August bis 2. September 2017