Presentations (SR 348, Informatik-Gebäude 50.34)

• Wednesday, 26th June 2019, 13:30 - 15:00: 

  • P1 Raoul Schwagmeier: Verification Methods with Probabilistic Logics
  • P2 Kuan Yang: Reduction of Process Models for Enabling Verification of Large Processes

• Thursday 27th June 2019, 13:30 - 15:30:

  • P3 Samrat Bista: SMT-based Process Verification
  • P4 Fangyi Xu: Process verification with Data
  • S2 Luyao Zhao: Evaluation Framework for Data-centric Process Management

• Wednesday 3rd July 2019, 14:00 - 15:15:

  • S5 Moritz Renftle: Taxonomy of Use Cases for Word Embedding Models
  • P5 Rusheel Iyer: Process Management in the Cloud

• Thursday 4th July 2019, 13:30 - 15:30:

  • S6 Markus Mohr: Meta-Learning Approaches for Clustering
  • S7 Alexis Bernhard: Generating Training Examples in Meta-Learning 

Deadlines

• Report structure and literature list: 21.05. 2019
• 1st Version of the complete presentation slides: 2 weeks before presentation
• Report: 31.08.2018

Templates

for report: Springer LNCS Format (Latex or Word Downloads), see http://www.springer.com/computer/lncs?SGWID=0-164-6-793341-0

for presentations: no required standard, a possible powerpoint template 

Slides of the Kick-Off meeting at 30th April 2019.

Slides of the Tutorial "How to present" (12 June 2019)

Topics of the DBIS Research Group of Prof. Klemens Böhm are Databases, Information Systems and Data Science. In this seminar, we will focus on the subtopics Workflow Management Systems (WfMS), Privacy of Database Systems and Meta-Learning.

- WfMS are important to model and automate processes in a variety of application domains, such as energy markets and auctions. Current trends in research concern all phases of the lifecycle of workflows. They are driven by innovations like cloud infrastructures, improved flexibility and adaptability of processes, enhanced influence of data on workflows, and process mining and verification techniques to achieve higher quality of business processes.

- Privacy of database queries: Nowadays, it is common to store and query databases that contain private data. Important examples are location or energy consumption databases that contain information on, e.g., the daily habits of the data owners. In this seminar, we focus on querying such databases in a privacy-preserving way. To this end, we study the popular concept of differential privacy.

- In machine learning (ML), the field of meta-learning has received growing attention in the last few years. Meta-learning tries to predict how well a ML model will perform on a particular data set, without training the ML model. It uses characteristics of data sets as meta-features and ML performance measures like accuracy as meta-target. Thus, one can apply standard ML approaches like classification, regression, or instance-based methods on such meta-data sets.

In this seminar, topics are recent research on workflow modeling and verification, privacy of querying databases and meta-learning.

In the seminar the language is English. We will organize the presentations in 3 blocks.