PRAXISforum Big Data Analytics in Process Industry

Conference Programme

PRAXISforum Programme

You can download the programme brochure Here as PDF 


Programme Overview

Tuesday, 9 April 2019
10:00 Check-in and opening of exhibition


Opening and welcome address

Moderation: Thomas Hansmann, CDV Advisory, Germany


Current trends within the Industrial Internet of Things: End to end data processing

  • Connectivity: 5G/TSN
  • Communication: OPC UA
  • Data: Distributed digital shadows
  • Processing: Edge computing
Alexander Willner, Director Industrial Internet of Things (IIoT) Center
Business Unit NGNI, Fraunhofer FOKUS, Germany


Reshaping process industry by using Big Data

  • Big Data - new raw material for process industries
  • Examples of use of big data to increase process efficiency and product quality
  • Examples of use of Big Data for innovative prodcuts and business models

Renata Jovanovic, Managing Director, Accenture GmbH, Germany

12:15 Lunch break, interactive networking and discussion @ "topic tables" in exhibition area


Failure analysis and early detection for large machines using data analytics

  • A failure analysis example from Borealis-Polyolefine
  • An understandable introduction to the involved machine learning algorithms
  • Inspiration to use data analysis in industry

Stefan Pauli, Senior Data Scientist, VTU Engineering Schweiz AG, Switzerland

Herbert Andert, Group-Leader EIC & Automation, VTU Engineering GmbH, Austria


AI in action: A complete and AI-based process automation of text classifications

  • Complete process automation including a daily text mining
  • Business user can easily work/adjust process flow
  • User interface to work with the text mining results

Alexander Timo Buchwald, mayato GmbH, Germany


Data science use cases for the process industry: Data-driven production & supply chain optimizations and forecasting approaches

  • Introduction of data science into the company culture by means of a phase model
  • Data science and AI use case requirements for defining the future IT architecture
  • Real life use case examples relevant to the process industry

Rene Fassbender, CEO & Founder, OmegaLambdaTec GmbH, Germany


Coffee break, interactive networking and discussion @ "topic tables" in exhibition area


From data to knowledge on the transformation and translation

  • Promotion of technology transfer from science to industry in the field of cognitive computing
  • Automated workflows on supercomputers for AI and data analytics. Examples: neuroscience and Earth systems sciences
  • How the new paradigm of modular supercomputing boosts data analytics and AI

Thomas Lippert, Director of the Institute for Advanced Simulation, Head of Jülich Supercomputing Centre, Forschungszentrum Jülich GmbH, Germany


How Big Data will shape the future of intellectual property analytics

  • Emergent technology trends change market competition
  • Why decision making requires a constant monitoring & analysis of millions of data points
  • How artificial intelligence helps to overcome the big data problem

Tim Pohlmann, CEO & Founder, IPlytics GmbH, Germany


Managing the digital transformation

  • What the vision of Industrie 4.0 tells?
  • What do other leading companies?
  • How can I build my company-individual roadmap?

Tobias Harland, Senior Manager Industrial Practice, i4.0MC Industrie 4.0 Maturity Center GmbH, Germany


Data Mining mining data – Let’s put your data to work

  • 20.8B connected things by 2020
  • 1% of industrial data is used
  • Making sense of DATA is hard. Making use of DATA even harder

Sebastian-Friedrich Kowitz, CEO, talpasolutions GmbH, Germany

18:30 Networking Dinner and Discussion @ "Topic Tables" in Exhibition Area


End of first PRAXISforum Day


Wednesday, 10 April 2019

08:30 Re-Opening of Exhibition


Graph technology the enabler for advanced analytics in process industry

  • Graph Technology allows complete transparency of the design process
  • Cross-functional comparison between plant design and plant operation is effortlessly possible
  • Technology significantly improves topics such as predictive maintenance

Sebastian Dörr, Vice President Sales, CONWEAVER GmbH, Germany


Gaining a 360°-view on the business along the whole value chain to make smarter business decision and to thrive innovation

  • The new requirements on the data supply chain
  • How to build a smart data supply chain involving big data and business data with the Logical Data Warehouse (LDW)
  • Breaking down the data silos within the entire organisation
  • Feeding the hunger of IoT and AI applications

Mirko Hardtke, Business Development Manager, Data Virtuality GmbH, Germany


Remote monitoring & optimization for Air Liquide plants

  • Data cleaning
  • Centralized monitoring and optimization
  • Remote control

Moussa Diakhité, Real Time Engineer, Air Liquide France Indstrie, France


Big Data monitoring of chemical batches

  • Turn your batch data to actionable insight
  • Using big data analytics methods to detect and correct product anomalies early
  • Increase the productivity of each asset

Martin Hollender, Researcher, ABB Corporate Research Center, Germany

11:00 Coffee Break, Interactive Networking and Discussion @ "Topic Tables" in Exhibition Area


Model-based solutions for digitalization and automation in the process industry

  • Integration of process data and process knowledge into smart self-learning solutions
  • Smart software, smart sensors and smart platforms
  • Tailored applications for process screening, optimization, scale-up and predictive maintenance
  • Industrial cases and implementations in chemical and pharma sector

Michael Sokolov, COO, Data How, Switzerland


What (not?) to expect from Big Data analysis in process technology

  • Data mining use case: Power plant efficiency
  • Big Data analysis is NOT getting all answers from an automated set of algorithms...
  • Perspective from offline to online data analysis

Martin Weng, Managing Director, aixprocess GmbH, Germany


Make it fun to use modelling and machine learning

  • Are you aware that you most likely are already using some machine learning techniques?
  • Pitfalls you can fall in using traditional methods like ANOVA or least-squares regression
  • How to deal with functional data and other useful machine learning techniques out of the box

Martin Demel, Sr. Systems Engineer JMP, SAS Institute GmbH (JMP Devision), Germany

13:45 Lunch break, interactive networking and discussion @ "topic tables" in exhibition area
14:30 End of PRAXISforum
Become a member