In the framework of the 8th Consortium Meeting, which was held from 15th to 17th of June 2016 in Munich, “Large-Scale Distributed Streaming – Methods and Applications” workshop is organized by the FERARI Project in collaboration with Siemens and the LMU Munich.
The goal of this workshop was to bring together researchers and practitioners in the field of large-scale distributed streaming in order to discuss current developments at the interface between research and industrial applications. The aims were to foster an exchange between the research communities as well as to promote recent research results to applications in industry.
|9:00||–||9:30||Michael Mock, Fraunhofer IAIS||Welcome, Overview on the FERARI Project|
|9:30||–||10:00||Daniel Keren, Haifa University||Lightweight Monitoring of Distributed Streams Using Convex Bounding Functions|
|10:00||–||10:30||Minos Garofalakis, TUC||Efficient Analytics over Big, Dynamic, Distributed Data|
|11:00||–||11:20||Moshe Gabel, Technion||Using Stream Mining Techniques for Machine Health Monitoring|
|11:20||–||11:40||Moshe Gabel, Technion||Monitoring Least Squares Models of Distributed Streams|
|11:40||–||12:10||Assaf Schuster Technion||Lazy Detection of Complex Events over Event Streams|
|12:10||–||12:40||Antonios Deligiannakis, TUC||Optimizing Massive-Scale Complex Event Processing|
|14:00||–||14:30||Michael May, Siemens||Industrial Data Analytics @ Siemens|
|14:30||–||15:00||Volker Tresp, Siemens||Machine Learning for Context Sensitive Event Prediction|
|15:30||–||16:00||Filippo M. Tilaro, Manuel G. Berges, CERN||Data Analytics for CERN Control System|
|16:00||–||16:30||Thomas Seidl, LMU||Analyzing even faster data streams|
|16:30||–||17:00||Matthias Schubert, LMU||Monitoring Relational Patterns in Volatile Data Set|
The FERARI project (ferari-project.eu) stands for “Flexible Event Processing for Big Data Architectures” It provides communication efficient in-situ solutions for executing complex tasks on large, distributed data streams, such as efficiently identifying sequences of events over distributed sources with complex relations, or learning and monitoring sophisticated abstract models of the data. The FERARI architecture for large-scale complex event processing (CEP) over massive, distributed data streams is available as open source.