Programm Fachgruppe Datenbanksysteme

Day 1

13:00 – 13:30 Uhr
Prof. Dr. Stefan Wrobel (Institute Director Fraunhofer IAIS)

13:30 – 15:00 Uhr
Prof. Dr. Geoff Webb (Monash University)

Time series classification at scale

Time series classification is a fundamental data science problem, providing understanding of dynamic processes as they evolve over time. The recent introduction of ensemble techniques has revolutionised this field, greatly increasing accuracy, but at a cost of increasing already burdensome computational overheads.  I present new time series classification technologies that achieve the same accuracy as recent state-of-the-art developments, but with many orders of magnitude greater efficiency and scalability.  These make time series classification feasible at hitherto unattainable scale.

15:00 – 15:30 Uhr

15:30 – 16:30 Uhr
Joint Research Track

Solving Abstract Reasoning Tasks with Grammatical Evolution
(Raphael Fischer, Matthias Jakobs, Sascha Mücke and Katharina Morik)

Construction of a Corpus for the Evaluation of Textual Case-based Reasoning Architectures
(Andreas Korger and Joachim Baumeister)

Modeling Interdependent Preferences over Incomplete Knowledge Graph Query Answers
(Till Affeldt, Stephan Mennicke and Wolf-Tilo Balke)

16:30 – 18:00 Uhr
Poster Session

Day 2

13:00 – 14:30 Uhr
Prof. Dr. Kristian Kersting (TU Darmstadt)

On Hybrid and Systems AI
Our minds make inferences that appear to go far beyond standard machine learning. Whereas people can learn richer representations and use them for a wider range of learning tasks, machine learning algorithms have been mainly employed in a stand-alone context, constructing a single function from a table of training examples. In this talk, I shall touch upon a view on AI and machine learning, called Systems AI, that can help capturing these human learning aspects by combining different AI and ML models using high-level programming. Since inference remains intractable, existing approaches leverage deep learning for inference. Instead of “just going down the neural road,” I shall argue to also use probabilistic circuits, a deep but tractable architecture for probability distributions. This hybrid approach can speed up inference  as I shall illustrate for unsupervised science understanding, database queries and automating density estimation.


14:30 – 15:30 Uhr
Discovery & Evolution

  • Discovery of Ontologies from Implicit User KnowledgeDiscovery of Ontologies from Implicit User Knowledge
    (David Haller and Richard Lenz)
  • Sense Tree: Discovery of New Word Senses with Graph-based Scoring
    (Jan Ehmüller, Lasse Kohlmeyer, Holly McKee, Daniel Paeschke, Tim Repke, Ralf Krestel and Felix Naumann)
  • Schema Evolution and Reproducibility of Long-term Hydrographic Data Sets at the IOW
    (Tanja Auge, Erik Manthey, Susanne Jürgensmann, Susanne Feistel and Andreas Heuer)

15:30 – 16:00 Uhr

16:00 – 17:00
Query Processing

  • Future Fetch – Towards a ticket-based data access from secondary storage in database systemsFuture Fetch – Towards a ticket-based data access from secondary storage in database systems
    (Demian E. Vöhringer and Klaus Meyer-Wegener)
  • Modeling Interdependent Preferences over Incomplete Knowledge Graph Query Answers
    (Till Affeldt, Stephan Mennicke and Wolf-Tilo Balke)
  • Towards Evolutionary, Domain-Specific Query Classification Based on Policy Rules
    (Peter K. Schwab and Klaus Meyer-Wegener)

17:00 – 18:00 Uhr
Community Meeting

Day 3

13:00 – 14:00 Uhr
Parallel Research Track

14:00 – 15:00 Uhr
Parallel Research Track

15:00 – 15:30 Uhr

15:30 – 16:30 Uhr
Prof. Dr. Thomas Gärtner (TU Wien)

Interactive Machine Learning with Structured Data

In this talk I’ll give an overview of our contributions to what I call interactive machine learning. Often, interaction in Computer Science is interpreted as the interaction of humans with the computer but I intend a broader meaning of the interaction of machine learning algorithms with the real world, including but not restricted to humans. Interactions with humans span a broad range where they can be intentional and guided by the human or they can be guided by the computer such that the human is oblivious of the fact that he is being guided. Another example of an interaction with the real world is the use of machine learning algorithms in cyclic discovery processes such as drug design. Important properties of interactive machine learning algorithms include efficiency, effectiveness, responsiveness, and robustness. In the talk I will show how these can be achieved in a variety of interactive contexts.

16:30 – 17:00 Uhr

Call for Papers

The autumn meeting of the Fachgruppe Datenbanksysteme (GI Special Interest Group on Databases) in cooperation with LWDA 2020 will be held from September 9-11, 2020 at the University of Bonn, Germany. Depending on the situation, the meeting will probably be held as a virtual conference.

Currently, scalable large-scale data management and the efficient processing of big volumes of heterogenous data is an important problem for all areas of database systems ranging from new types of hardware for data management to new algorithms for query processing and data analytics. While the focus on scalability is not new in database research and indutrial development, the new kinds of data, their heterogeneity, and their specific semantics pose a wide variety of new and exciting challenges. The exploration of these challenges will be at the heart for the upcoming FGDB meeting and we welcome a wide range of submissions.    

In summary, for LWDA 2020 we are especially interested in reports on research at the borderline of large-scale data management, Big Data processing, and domain-specific data management solutions coupled with data analytics and machine learning technology. Accordingly, topics of interest include but are not limited to:Solutions for large-scale data management and Big Data

  • Analytics and data processing in data-intensive applications
  • Streaming Data, dataflow engines and distributed data processing
  • Intelligent data analysis techniques, machine learning, deep learning
  • Data analysis with human interaction (e.g., crowdsourcing)
  • Data warehousing, data integration, ETL, and interoperability
  • Domain-specific solutions from all fields, covering business (eCommerce, IoT, industry 4.0, …) and application sciences (geosciences, biomedicine, digital health, physics, …)
  • User interfaces and visualization

Submission guidelines

We solicit submissions under two different models:

  • full research papers (up to 12 pages, peer-reviewed and to be published by LWDA)
  • short papers (4 pages, peer-reviewed and to be published by LWDA) to present visionary ideas, work in progress, projects etc.

For both submission models, authors will have the opportunity to give a presentation at LWDA, and both, full and short papers, will be published in the LWDA proceedings. All submissions should be in English. All papers have to be formatted according to the Springer LNCS guidelines and are to be submitted as PDF files to EasyChair. Please select the track ‘LWDA 2020, FG-DB’.

At least two independent reviewers will review all submissions. The conference proceedings will be published as CEUR Workshop Proceedings and will be indexed by DBLP. All workshop participants have to register for the LWDA 2020 conference.

We further welcome presentations and poster submissions of previously published work such as recent publications at top tier international venues. These will not be reviewed but selected by the PC chairs.

Topics of interest

Submissions from all areas contributing to the development and application of intelligent knowledge and experience management systems are welcome. We explicitly encourage paper submissions that are not mainstream but from communities within mathematics, social sciences or economics in order to obtain a more interdisciplinary view on the subject.

Topics of interest include but are not limited to:

  • New frontiers in Knowledge Management and Experience Knowledge
  • Reasoning approaches, for example case-based reasoning, probabilistic reasoning, logic-based approaches and text-based reasoning
  • Knowledge representation, for example cases, semantic networks, ontologies, context, and logics
  • Knowledge engineering, for example acquisition methods, quality assessment, versioning, maintenance, and visualization
  • Applications of Knowledge Management, for example semantic web applications, linked data, smart companion technologies, diagnosis, configuration, agent-based applications
  • User experience management and usability knowledge
  • Design and evaluation of knowledge systems
  • Practical experiences (“lessons learned”) with IT-aided KM approaches
  • Integration of Knowledge Management and business processes
  • Combination of Knowledge Management with other systems and concepts (e.g. decision support, cloud computing, Big Data)

Official workshop languages are German and English. In particular, contributions can be in English or German but presentations should be in English to ensure that English speaking participants can always follow.

Important dates

  • Submission deadline: June 21, 2020
  • Notification of acceptance: July 13, 2020
  • Camera-ready copy: August 17, 2020
  • LWDA 2020 Conference: September 9-11, 2020

Workshop organisation

  • Wolf-Tilo Balke (TU Braunschweig)
  • Stefan Conrad (Heinrich Heine University Düsseldorf)

Program committee

  • Klemens Böhm (KIT)
  • Ralf Krestel (U Passau)
  • Stephan Mennicke (TU Dresden)
  • Sebastian Michel (TU Kaiserslautern)
  • Felix Naumann (HPI Potsdam)
  • Gunter Saake (U Magdeburg)
  • Kai-Uwe Sattler (TU Illmenau)


Dr. Daniel Trabold

Dr. Daniel Trabold

Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS

Schloss Birlinghoven
53757 Sankt Augustin, Germany

Phone +49 2241 14-2751

Bonn Image: ©travelview – stock.adobe.com