The fourth section gives an overview of German Research Institutes which participate in large scale international research projects, and the final section highlights an organization which
4.2 Collaborative Research Centers
4.2.1 Cybernetic-Cluster – RWTH Aachen University
R&D
Organisation Cybernetic-Cluster IMA/ZLW & IfU | RWTH Aachen University
Dennewartstr. 27 | 52068 Aachen | www.ima-zlw-ifu.rwth-aachen.de/en Director: Univ.-Prof. Dr. rer. nat. Sabina Jeschke
Vice Directors: apl.-Prof. Dr. phil. Ingrid Isenhardt | Dr. rer. nat. Frank Hees +49 241 80 91110 | sabina.jeschke@ima-zlw-ifu.rwth-aachen.de R&D
activities
◼ Intelligent Production: The focus is on methods and procedures to integrate data from automated and virtual production, allowing the AI based prediction of a system’s behaviour and its control. Using a variety of data based techniques like regression, machine learning, natural language processing etc. as well as interactive and explorative visualizations like visual analytics, it is the goal to design, understand, evaluate and optimize new types of highly autonomous production systems. Here, biological inspired learning processes like (un)supervised and reinforcement learning come into play. A prediction of a casting process in automotive production has been developed and is in use.
Other areas of interest are the integration of mobile robotical systems as well as the hybrid cooperation between humans and robots.
◼ Logistics 4.0: The concepts of »production 4.0« can seamlessly be extended towards logistics. During an EU project, a big data algorithm was developed, which is able to automatically plan the optimal and intermodal route for transport logistic service providers under the aspects of time, resources and environment. The algorithm is tuned and built on a large database of transport processes throughout Europe. Other projects are the sensor fusion and data interpretation in the field of automous cars and trucks.
◼ Advanced human-computer interaction: Future HCI in the field of cognitive computing will have to adress the intuitive interaction of humans and technological sytems in all areas.
Taking the external context into account leads to novel methods of context detection and interpretation based on big data analytics. One major component and integral part of these systems is the ability to include human emotions into the interaction. A fully automatic multimodal emotion detection system for the automotive sector has been developed which continuously adapts the user interface of a GPS to the current temper and needs of the driver.
R&D cooperation
◼ Ziekenhuis Oost-Limburg, Leuven, Belgium: Data analytics in emergency medical services
◼ Maastricht University, Netherlands: Joint theses, networked first aid responder systems
◼ MIT Media Lab, Massachusetts Institute of Technology, USA: Joint theses
◼ HKUST, HongKong, China: Cooperation in entrepreneurship Cooperation
with partners in industry
◼ AUDI AG, Ingolstadt, Germany: Data analytics in automotive production
◼ BMW Group, Munich, Germany: Data analytics in automotive production
◼ Novartis AG, Nuremberg, Germany: Data analytics in pharmaceutical production
◼ Daimler AG, Sindelfingen, Germany: Overall study on the future of the working world (partly big data)
◼ Andersch, Frankfurt, Germany: Overall studies on 4.0 in different branches (partly big data)
◼ Interdigital, Wilmingsthon, USA: algorithms and platforms for data integration in the field of smart cities
Additional Information
◼ Award »Digitaler Kopf Deutschlands« for Prof. Jeschke within the German Science Year 2014 »The Digital Society«
◼ Innovation Award 2016 of the region »Münsterland« for the TelliSys consortium
◼ FRP.NRW Award 2011 for the innovative and efficient project coordination of TelliBox
◼ World Champion in the Festo Logistics League, within the RoboCup, 2014 & 2015
4.2.2 Big Data – Small Devices: Collaborative Research Center SFB 876, TU Dortmund University
R&D
Organisation Providing Information by Resource-Constrained Data Analysis | Collaborative Research Center SFB 876
Otto-Hahn-Str. 12 | 44227 Dortmund | http://sfb876.tu-dortmund.de Prof. Dr. Katharina Morik
R&D activities
The goal of SFB 876 is to provide information anytime, anywhere. Cyber-physical systems, the internet of things, scientific experiments – they all output big data. Timely
information helps in emergency situations, e.g., mobile breath spectrometers allow for diagnoses even of unconscious persons, if they still breathe. Portable virus scanners are to detect nano-objects such that a quarantine of groups becomes obsolete, because the infection can be detected for each individual in real-time. Timely information can make our cities smarter: new dynamic microscopic traffic models detect disturbances early on.
In logistics, processes will become more flexible, if containers can communicate using tiny electronic devices. Bringing computation and communication to machines is also a trend in factories, where control takes into account sensor data from manufacturing processes.
Turning big data into smart data requires many steps of data analysis: methods for extracting and selecting features, filtering and cleaning the data, joining heterogeneous sources, aggregating the data, and learning predictions need to scale up. The algorithms are challenged on the one hand by high-throughput data, gigantic data sets like in astrophysics, on the other hand by high dimensions like in genetic data. Resource constraints are given by the relation between the demands of processing the data and the capacity of the computing machinery. The resources are runtime, memory, communication, and energy. Novel machine learning algorithms are optimized with regard to minimal resource consumption. Moreover, learned predictions are applied to program executions in order to save resources.
The collaborative research center SFB876 is an interdisciplinary center comprising 14 projects, 20 professors, and about 50 PhD students or Postdocs.
R&D cooperation
◼ ISAS Leibniz-Institut für Analytische Wissenschaften – ISAS e.V.: Resource optimizing real time analysis of artifactious image sequences for the detection of nano objects
◼ Universitätsklinikum Essen: Feature selection in high dimensional data for risk prognosis in oncology
◼ University of Duisburg Essen: Analysis and Communication for Dynamic Traffic Prognosis
Cooperation with partners in industry
◼ B&S Analytik GmbH, Dortmund: Analysis of Spectrometry Data with Restricted Resources
◼ Deutsche Edelstahl WerkeGmbH, Witten, Data Mining for Quality Control
4.2.3 Cluster Data Engineering and Analytics – Technical University of Munich
R&D
Organisation Technical University of Munich | Department of Informatics
Boltzmannstraße 3 | 85748 Garching bei München | http://www.in.tum.de Representative Chairs:
◼ Chair for Database Systems, Prof. Dr. Alfons Kemper & Prof. Dr. Thomas Neumann
◼ Chair for Decision Sciences and Systems, Prof. Dr. Martin Bichler
◼ Chair for Software Engineering for Business Information Systems (sebis), Prof. Dr. Florian Matthes
R&D activities
◼ The R&D Cluster »Data Engineering and Analytics« of the Department of Informatics bundles various Big Data activities. The following sample selection of chairs illustrates the breadth and diversity of the research:
◼ The Chair for Database Systems performs research on the infrastructure for Big Data, hence enabling Big Data operations. The main focus is on building the main-memory database system HyPer. HyPer is a main-memory-based relational DBMS for mixed OLTP and OLAP workloads. It is a so-called all-in-one New-SQL database system that entirely deviates from classical disk-based DBMS architectures by introducing many innovative ideas including machine code generation for data-centric query processing and multiversion concurrency control, leading to exceptional performance. HyPer’s OLTP throughput is comparable or superior to dedicated transaction processing systems and its OLAP performance matches the best query processing engines — however, HyPer achieves this OLTP and OLAP performance simultaneously on the same database state. Current research focuses on extending HyPer’s functionality beyond OLTP and OLAP processing to exploratory workflows that are deeply integrated into the database kernel by utilizing HyPer’s pioneering compilation infrastructure. Thereby, the »computational database« HyPer serves as the data management as well as the compute infrastructure for Big Data applications.
◼ The Chair for Decision Sciences and Systems focuses on particular use cases and application domains: The analysis and prediction of bid data in the context of ad exchanges and real-time bidding, besides social network analysis based on telecom data in collaboration with CMU and a national telecom (Prof. Krishnan) and also system monitoring and metering, and parameter estimation for automated resource allocation in data centers together with Huawei Ltd.
◼ The Chair for Software Engineering for Business Information Systems is concerned with the adoption of Big Data techniques in enterprises and performs empirical analysis of Big Data adoption in industry (Business Models, Big Data Use Cases, Practical Big Data Experiences, Software Architectures for Big Data). Moreover, the chair explores the application of Big Data techniques, in particular in the service platform monitoring and analytics project (part of TUM LLCM, http://tum-llcm.de/) and in the Lexalyze project where text mining and natural languange processing is performed on large legal document collections, http://www.lexalyze.de)
R&D cooperation
◼ Prof. Dr. Kemper & Prof. Dr. Neumann & Prof. Dr. Peter Boncz, VU Amsterdam & CWI (Guest Professor, Humboldt Prize) & Prof. Dr. Torsten Grust (University of Tübingen)
◼ Prof. Dr. Bichler & Prof. Dr. Ramayya Krishnan (CMU) & Prof. Dr. Jacob Goeree, UT Sydney
◼ Prof. Dr. Matthes & Prof. Dr. Rick Kazman, Software Engineering Institute (SEI, CMU) & Prof.
Dr. Hong-Mei Chen, Shidler College of Business, University of Hawaii at Manoa Cooperation
with partners in industry
◼ Oracle Labs, SAP, Fujitsu, Siemens, Tableau Software
◼ Huawei (collaboration on automated resource allocation in virtualized data centers)
◼ SAP XM (collaboration on real-time bidding)
◼ Government of New South Wales (collaboration on cap-and-trade systems)
◼ Allianz Deutschland, IBM Deutschland (Text Mining, Semantic Annotation of Legal Concepts in Terms and Conditions Using the Analytical Components of IBM Watson Explorer); BMW & Siemens (TUM Living Lab Connected Mobility, Ecosystem Platforms and Connected Mobility), www.tum-llcm.de
The Chair for Database Systems coordinates the new Master of Science program »Data Engineering and Analytics« that was established to train the new generation of Data Science professionals. Moreover, the chair holds best paper awards at BTW 2013, 2015 and ICDE 2014.
Prof. Neumann was awarded with the VLDB 2014 Early Career Innovation Award. Prof Kemper received the GI Fellow award 2015. The Chair for Database Systems organizes the VLDB 2017 International Conference at TUM.
The Chair for Decision Sciences and Systems works on predictive and prescriptive analytics combining data analysis and optimization. Various projects were conducted in the field of marketing analytics including churn prediction, campaign management, and social network analysis in various service industries.
The Chair for Business Information Systems coordinates the TUM Living Lab Connected Mobility project (5 Chairs, 19 PhDs) that includes several topics related to Big Data:
http://www.tum-llcm.de.
Living Lab Connected Mobility project
The German automotive industry faces major challenges through new mobility concepts, digital business models and strong international competitors of digital mobility services.
In support of the digital transformations in the area of smart mobility and smart city the Free State of Bavaria supports the TUM Living Lab Connected Mobility project, which is an interdisciplinary research project, which bundle the fields of informatic and transport research.
The aim of this project is to deliver innovative contributions regarding the design, the architecture, and the scalable realization of an open, vendor independence digital mobility platform. This platform will be developed in a close cooperation with leading companies and will offer small and medium-sized companies a marketplace to develop and operate digital mobility services with substantial lower financial, organization and technical effort with the option of networking. (http://tum-llcm.de/en/)