IEEE-BIG-DATA17_BOSTON

IEEE-BIG-DATA17_BOSTONWe are very pleased to announce that our group got a paper accepted for presentation at IEEE BigData 2017, which will be held on December 11th-14th, 2017, Boston, MA, United States.

 
In recent years, “Big Data” has become a new ubiquitous term. Big Data is transforming science, engineering, medicine, healthcare, finance, business, and ultimately our society itself. The IEEE Big Data conference series started in 2013 has established itself as the top tier research conference in Big Data.
The 2017 IEEE International Conference on Big Data (IEEE Big Data 2017) will provide a leading forum for disseminating the latest results in Big Data Research, Development, and Applications.

Implementing Scalable Structured Machine Learning for Big Data in the SAKE Project” by Simon Bin, Patrick Westphal, Jens Lehmann, and Axel-Cyrille Ngomo Ngonga.

Abstract: Exploration and analysis of large amounts of machine generated data requires innovative approaches. We propose a combination of Semantic Web and Machine Learning to facilitate the analysis. First, data is collected and converted to RDF according to a schema in the Web Ontology Language OWL. Several components can continue working with the data, to interlink, label, augment, or classify. The size of the data poses new challenges to existing solutions, which we solve in this contribution by transitioning from in-memory to database.


Acknowledgments
This work was supported in part by a research grant from the German Ministry for Finances and Energy under the SAKE project (Grant agreement No. 01MD15006E) and by a research grant from the European Union’s Horizon 2020 research and innovation programme under the SLIPO project (Grant agreement No. 731581).


Looking forward to seeing you at IEEE BigData 2017.