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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.

Arbeitsgruppe

9

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.

Akademien

ab dem 5. Semester und Doktoranden

|

20. August bis 2. September 2017

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