Projekt

PROTEUS

Predictive Analytics and Real-Time Interactive Visualization for Industry

Predictive Analytics and Real-Time Interactive Visualization for Industry

  • Laufzeit:

PROTEUS mission is to investigate and develop ready-to-use scalable online machine learning algorithms and real-time interactive visual analytics to deal with extremely large data sets and data streams.

The foundation for the PROTEUS advances is the use of an optimized implementation of combined batch and streaming processing and building around this later scalable real time processes. The developed algorithms and techniques will form a library to be integrated into an enhanced version of Apache Flink, the EU Big Data platform.

In project PROTEUS, DFKI works together with industrial and academic partners Bournemouth University, treelogic, Trilateral Research&Consulting, Arcelor Mittal and Lambdoop to develop innovative data analytics algorithms to tackle challenges facing predictive and decision support as well as data visualization.

In particular, the project goes beyond the current state-of-art technology by making the following specific original contributions:

  • New strategies for real-time hybrid computation, batch data and data streams.
  • Real-time scalable machine learning for massive, high-velocity and complex data streams analytics.
  • Real-time interactive visual analytics for Big Data.
  • Implementation of the new advances on top of Apache Flink.
  • Real-world industrial validation of the technology developed.

DFKI chairs the project's scientific and technical committee, which assists the steering board and project coordinator, and lead development of a high-throughput, low-latency software architecture for conjoint batch and stream processing. Apache Flink is being extended to serve as basis for novel scalable online machine learning algorithms. DFKI will disseminate and exploit scientific results and engage in the Apache Flink community to attract public awareness to PROTEUS.

The project is funded by the European Union (Horizon 2020, Ref: 687691).

Projekt teilen auf:

Ansprechpartner

Publikationen zum Projekt

Adrian Bartnik, Bonaventura Del Monte, Tilmann Rabl, Volker Markl

In: Datenbanksysteme für Business, Technologie und Web (BTW 2019) Datenbanksysteme für Business, Technologie und Web (BTW 2019). GI-Fachtagungen Fachtagung für Datenbanksysteme für Business, Technologie und Web (BTW) March 4-8 Rostock Germany Gesellschaft für Informatik Bonn 2019.

Zur Publikation
Tilmann Rabl Jeyhun Karimov (Hrsg.)

IEEE International Conference on Data Engineering (ICDE-2018) befindet sich The annual IEEE International Conference on Data Engineering (ICDE) addresses research issues in designing, building, managing, and evaluating advanced data-intensive systems and applications. It is a leading forum for researchers, practitioners, develope April 16-19 Paris France IEEExplore 10/2018.

Zur Publikation
Jonas Traub, Philipp Grulich, Alejandro Rodriguez Cuéllar, Sebastian Breß, Asterios Katsifodimos, Tilmann Rabl, Volker Markl

In: Proceedings of the 34th IEEE International Conference on Data Engineering. IEEE International Conference on Data Engineering (ICDE-2018) April 16-20 Paris France IEEE 2018.

Zur Publikation

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence