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Chair of Data Science and Data Engineering

Chair of Data Science and Data Engineering

New Professorship at Bonn-Aachen International Center for Information Technology

Prof. Dr. Emmanuel Müller Prof. Dr. Emmanuel Müller
Chair of Data Science and Data Engineering
Bonn-Aachen International Center for Information Technology

E-Mail: mueller(at)
Phone: +49 228 7369-300 (b-it)
Fax: +49 228 7369-301 (b-it)

b-it building, Room 2.123
Endenicher Allee 19C
D-53115 Bonn

Research and Teaching Overview

Our research covers knowledge discovery, data mining, big data systems, and data exploration for high dimensional, graph, time series, and stream data. The chair is leading and contributing to several open-source initiatives enabling repeatability and comparability for the research community. We have organized several tutorials and workshops at major data mining, database, and machine learning conferences, and edited a special issue for the Machine Learning Journal. In the past few years, we have initiated and coordinated various education programmes for “Data Science” and “Data Engineering”: One on the level of university education (M.Sc. programme), two graduate schools (PhD programmes) and multiple executive education programmes for industry.

Research Activities

Knowledge Discovery and Data Mining

Knowledge discovery and data mining, as part of many scientific and industrial applications, 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. On the one side, we investigate efficient algorithms, which scale with size and complexity of the data. Moreover, on the other side, our algorithms generate verifiable knowledge for human users.

Data Mining Topics Algorithms for Big Data Analytics

Our research addresses theoretic challenges in correlation analysis, representation learning, (un-)supervised feature selection, cluster and outlier detection as well as practical challenges in efficient computation of these models in large and complex datasets. The development of novel techniques for complex data spaces (e.g. graph structures, time series, data streams, or high dimensional data) is a particular challenge in this area. We overcome information loss and scalability challenges of traditional data mining techniques that assume homogeneous data and enable big data analytics on heterogeneous datasets. Our chair investigates algorithms for the selection of relevant attributes in high dimensional data, correlations in time series data, change in multivariate data streams, and similarity structures in graph data.

Verifiable Knowledge Discovery for Human Users

Our research aims at an easy to understand presentation of data analytics results. We represent intrinsic dependencies between different information sources for human users. Our research includes exploring the automatic extraction of dependencies and pattern descriptions, which is a significant research contribution for many applications where patterns have to be verified by the users. Human users require such descriptions of potential reasons for each of the detected patterns. Hence, we have proposed verifiable descriptions for learned representations, unexpected patterns, user-driven data exploration, and semi-automated data profiling.

Teaching Activities

Big Data Analytics Data Science Education

In our lectures, we cover fundamental concepts in the field of Big Data Analytics for students in B.Sc./M.Sc. Computer Science, Computational Science, and Data Science programmes. Techniques for the analysis of large and complex datasets have a significant impact in many industrial and scientific applications. In science, industry, and society, in general, there is the necessity of understanding complex data by extracting valuable patterns from a multitude of datasets. In our courses, we introduce the systematic processing of large data volumes as a precondition for both human data understanding and automatic data analysis. We teach fundamental data analytics techniques applicable to different domains in science and industry.

Lectures, Labs, and Seminars

We provide basic lectures, lab courses, and practice-oriented projects as introductory courses:

  • Big Data Analytics
  • Fundamentals in Statistics and Linear Algebra
  • Fundamentals in Data Structures and Scalable Algorithms
  • Big Data Analytics Lab (incl. annual Data-Mining-Cup competition)
  • Projects on selected machine learning topics (e.g. “Predictive Diagnostics”, “Graph Exploration”, …)

We provide a selected set of advanced lectures and research seminars for specialization in data science and engineering:

  • Advanced Data Mining Paradigms for Complex Data
  • Graph Mining and Exploration
  • Indexing Structures for Efficient Database Access
  • Data Science and Engineering in Industry and Sciences
  • Smart Representations for Big Data Analytics
  • Data Engineering Research Labs (e.g. “Exploration of Complex Networks”, “Representation Learning for Predictive Maintenance”, …)

INTEGER Teaching Concept

In our Data Science and Data Engineering Labs, we supervise students w.r.t. open research challenges. Courses reflect our research focus on formal problem settings and scalable algorithmic solutions. As result of these lab courses we aim at publication and presentation of results at international conferences, i.e. students will participate in the entire research process! We have named this course style “INTEGER”. INTEGER provides students the opportunity to participate in research. As part of lab courses, we supervise Bachelor and Master students w.r.t. open research challenges, development of novel solutions, publication of results, and let the student’s present their work at international conferences. With INTEGER students have successfully participated in the entire research process and gained enthusiasm for research.

Data Science Education

Selected Publications

All of our publications are listed online [DBLP Bibliography][ACM Digital Library][Google Scholar]

  • Anton Tsitsulin, Davide Mottin, Panagiotis Karras, Alex Bronstein, Emmanuel Müller:
    NetLSD: Hearing the Shape of a Graph
    Proc. 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2018)
  • Erik Scharwächter, Fabian Geier, Lukas Faber, Emmanuel Müller:
    Low redundancy estimation of correlation matrices for time series using triangular bounds
    Proc. 22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2018)
  • Anton Tsitsulin, Davide Mottin, Panagiotis Karras, Emmanuel Müller:
    VERSE: Versatile Graph Embeddings from Similarity Measures
    Proc. 27th International Conference on World Wide Web (WWW 2018)
  • Arvind Shekar Kumar, Tom Bocklisch, Patricia Iglesias Sanchez, Christoph Strähle, Emmanuel Müller:
    Multi-Feature Interactions and Redundancy for Feature Ranking in Mixed Data.
    Proc. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2017)
  • Davide Mottin and Emmanuel Müller:
    Graph Exploration: From Users to Large Graphs
    Proc. ACM International Conference on Management of Data (SIGMOD 2017)
  • Erik Scharwächter, Emmanuel Müller, Jonathan Donges, Marwan Hassani, Thomas Seidl:
    Detecting Change Processes in Dynamic Networks by Frequent Graph Evolution Rule Mining
    Proc. IEEE International Conference on Data Mining (ICDM 2016)
  • Fabian Keller, Emmanuel Müller, Klemens Böhm:
    Estimating mutual information on data streams.
    (Best Paper Award) Proc. 27th International Conference on Scientific and Statistical Database Management (SSDBM 2015)
  • Thibault Sellam, Emmanuel Müller, Martin L. Kersten:
    Semi-Automated Exploration of Data Warehouses.
    Proc. 24th ACM Conference on Information and Knowledge Management (CIKM 2015)
  • Bryan Perozzi, Leman Akoglu, Patricia Iglesias Sánchez, Emmanuel Müller:
    Focused clustering and outlier detection in large attributed graphs.
    Proc. 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2014)
  • Hoang Vu Nguyen, Emmanuel Müller, Klemens Böhm:
    A Near-Linear Time Subspace Search Scheme for Unsupervised Selection of Correlated Features.
    In Big Data Research Journal (1) 2014
  • Patricia Iglesias Sánchez, Emmanuel Müller, Fabian Laforet, Fabian Keller, Klemens Böhm:
    Statistical Selection of Congruent Subspaces for Mining Attributed Graphs.
    Proc. IEEE 29th International Conference on Data Mining (ICDM 2013)
  • Fabian Keller, Emmanuel Müller, Klemens Böhm:
    HiCS: High Contrast Subspaces for Density-Based Outlier Ranking.
    Proc. IEEE 28th International Conference on Data Engineering (ICDE 2012)
  • Emmanuel Müller, Matthias Schiffer, Thomas Seidl:
    Statistical selection of relevant subspace projections for outlier ranking.
    Proc. IEEE 27th International Conference on Data Engineering (ICDE 2011)
  • Emmanuel Müller, Stephan Günnemann, Ira Assent, Thomas Seidl:
    Evaluating Clustering in Subspace Projections of High Dimensional Data.
    Proc. 35th International Conference on Very Large Data Bases (VLDB 2009)