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This time Julius Blum GmbH ( will host the event. This is a joint meetup with 4ländereck Data Science (4leds)(; you can RSVP on either meetup page.


17:00 - 17:15: Welcome (Klaus Wendel, Blum)

17:15 - 18:00: Data Science at Blum: Process Optimization with Data Analytics (Kathrin Plankensteiner, Blum and FH Vorarlberg)

18:00 - 18:15: break

18:15 - 19:00: Graphs and graph algorithms with Neo4j and Cypher (Iryna Feuerstein, Prodyna)

19:00 - 20:00: Drinks, Food and Networking

Talks and speakers:

# Data Science at Blum: Process Optimization with Data Analytics

In manufacturing industries, data analytics can be used for several and heterogeneous tasks, e.g. for product design, quality evaluation, production control & optimization, demand forecasting. In this talk, Kathi Plankensteiner discusses selected data analytics projects. She talks about key business requirements, applied statistical & probabilistic models as well as about identified challenges during data modeling. Furthermore, she gives an outlook for corresponding prospective implementations for business and process optimization.

Kathi Plankensteiner studied technical mathematics and data analysis at the University of Klagenfurt, Austria. She received her PhD. in applied statistics in 2015. During her master and PhD. studies, she worked for KAI (Competence Center for Automotive and Industrial Electronics), which is a funded research center of Infineon Technologies Austria AG. Now she works as a Data Scientist at Julius Blum GmbH and as a research assistant at FH Vorarlberg. Her field of research includes reliability testing and analyzing, lifetime modeling, regression analysis, computational statistics, multivariate data analysis, statistical inference, machine learning, and stochastic process modeling.

# Graphs and graph algorithms with Neo4j and Cypher

Graph data processing and analysis is a perfect complement to the common statistical methods of data analysis. In this talk, we will give a brief introduction to the graph database Neo4j and its query language Cypher. The advantages of the graph algorithms will be explained and typical use cases (network analysis, semantic search, linked data) presented. Furthermore, we will explore where a graph database can amend the existing machine learning tools in solving data science problems.

Iryna Feuerstein is a passionate graphista and co-organizer of NRW graph databases Meetup. She works as IT consultant and software engineer at PRODYNA AG. Her specialization is data engineering with the main focus on topics like linked data, graphs and graph databases. She gained the theoretical background for graph theory during her studies in Mathematics, which she accomplished last year. Additionally, she possess already several years of experience with graph databases in big projects in industry. In her free time, she enjoys engaging in child education in IT too.