FOKUS researchers are awarded for their work on big data
News from Dec. 17, 2013
On the 27th of November Nils Montenegro was awarded for his achievement in his bachelor thesis “Big Data analysis in case of financial data” at the panel “Forum Junge Spitzenforscher”. Moreover Forooz Shahbazi Avarvand got an award for her proposal “Applying and Improving Subspace Methods and Techniques for Big Data dimension Reduction” which deals with the categorization of vast data amounts. At the annual panel which is hosted by “Stiftung Industrieforschung” and “Humboldt-Innovation GmbH” researchers get the opportunity to present novel and applicable results to business representatives.
The guiding theme during this year’s panel was research on Big Data which deals with the efficient analysis of huge data amounts with regard to specific criteria. During the last couple of months there has been a focus on Big Data in the media due to its usage in the analysis of intelligence data. Nevertheless there are numerous fields of application in telecommunication, for insurance companies and the financial institutions. Thus Nils Montenegro dealt with the analysis of historic equity prices and tested different kinds of trading strategies which could have been pursued with Big Data back then. Supervised by researchers from the Fraunhofer FOKUS he dealt with methods for the recognition of certain patterns and different kinds of MapReduce-algorithms in order to figure out significant trends quickly.
Forooz Shahbazi Avarvand is working on her dissertation about signal processing at Fraunhofer FOKUS at the moment. She was able to transfer her knowledge from acoustic signal processing and electroencephalography (EEG) which is a method for measuring current flows within the neurons of the brain to the field of Big Data. She presented a new approach for categorizing totally unstructured data as it occurs in written texts. The major innovation was to take a look at the data set with respect to a reduced number of aspects before sorting the data with the help of subspace methods. The innovation’s benefit opens up opportunities within numerous applications of Big Data.