Abstract

In many industry applications, larger and larger amounts of data become available, allowing to gain deeper insights, to generate more accurate forecats, to optimize and automate processes, and to thereby create significant value. Often the data is not static, but arrives continuously in large data streams, which ideally are processed and leveraged in real-time. This talk will present a modular and flexible platform for real-time big data stream processing, complex event detection, data science and machine learning with an easy-to-use visual process design user interface, seamlessly integrating the most relevant big data and stream processing frameworks (Hadoop, Spark, Spark Streaming, Kafka, Flink, etc.) within one unifying platform and user interface, based on the widely used data science platform RapidMiner. This talk will also provide an overview of applications of this framework across many industries like machine failure prediction and prevention and predictive maintenance in industrial production in the manufacturing industry, criticial event detection, prediction and prevention in the chemical indutry, product quality prediction and optimization as well as energy consumption and cost reduction in the steel and metal industry, data-driven process optimization in the food and beverage industry, various use cases in the automotive and aviation industry, maritime data analysis to detect complex events like piracy or illegal fishing and to avoid collisions, drug effectiveness prediction for cancer drug development and biomedical research, financial time series analysis and forecasting for the investment industry. The latter three use cases are addresed in the European reseach projects INFORE, which will also be shortly introduced.

Biography

Ralf Klinkenberg, founder and head of research at RapidMiner, is a data-driven entrepreneur with more than 30 years of experience in machine learning and advanced data analytics research, software development, consulting, and applications in the automotive, aviation, chemical, finance, healthcare, insurance, internet, manufacturing, pharmaceutical, retail, software, and telecom industries. He holds Master of Science degrees in computer science with focus on artificial intelligence, machine learning, and predictive analytics from Technical University of Dortmund, Germany, and Missouri University of Science and Technology (MST), Rolla, MO, USA. In 2001 he initiated the open source data mining software project RapidMiner and in 2007 he founded the predictive analytics software company RapidMiner with Dr. Ingo Mierswa. In 2008 he won the European Open Source Business Award and 2016 he was awarded the European Data Innovator Award. In 2017 the German government invited him to the steering committee of the “Plattform Lernende Systeme”, an initiative of the German government to promote the use of machine learning and artificial intelligence in industry and society, which he serves since then. In 2018 and 2020 he advised the German government in the formulation of its artificial intelligence strategy. The "Who’s Who in Data Science & Machine Learning 2021" by Onalytica named RapidMiner as one of the top 3 data science platform providers world-wide and Ralf Klinkenberg as one of the leading experts and influencers for data science and machine learning. Ralf Klinkenberg is co-organizer of the Industrial Data Science (IDS) conference series. He is passionate about learning in humans and machines as well as about how to leverage data to make organization more data-driven, more agile, more efficient and effective, and more successful using data mining and machine learning, both from a business and a technical perspective. Today RapidMiner has 770,000+ registered users in 150+ countries world-wide and is one of the most widely used predictive analytics platforms world-wide. In the last seven years, Analysts of Forrester and of Gartner repeatedly named RapidMiner as a leader and visionary among the machine learning and data science platform providers.