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Data Science

Lupe

Data in today’s business landscape are created and stored at exponentially large scales. Therefore, the need to improve business operations through data-driven decisions has emerged as an important objective for many growing companies. The field of data science addresses those needs by combining computer science, engineering, mathematics, statistics, and predictive modeling to generate analytical insights about data from a variety of sources.   Data often require a great amount of cleaning and pre-processing; so many research topics are also directed toward identifying different solutions for parallelized and distributed computing and data storage: from the big players in this market like Apache Hadoop and Spark through to CUDA. The field of data science interfaces with a variety of other disciplines, and by utilizing new computational technologies together with statistics and predictive modeling we strive to provide unique analytical insights from data at large scales.

Data Science research projects at SNET currently investigate data from the automotive, energy, and mobile communications domains. As such, we are often involved with the processing and analysis of geospatial data with both structured and unstructured formats.  Our goal is to discover the statistical relationships buried deep within data, and to use that knowledge as the framework for prototype development.

Publications

Katsarou, K. and Shekhawat, D. S. (2019). CRD-SentEnse: Cross-domain Sentiment Analysis using an Ensemble Model. Proceedings of The 11th International ACM Conference on Management of Digital EcoSystems(MEDES 2019). ACM (accepted for publication).


Eichinger, T. and Beierle, F. and Papke, R. and Rebscher, L. and Tran, H. and Trzeciak, M. (2019). On Gossip-based Information Dissemination in Pervasive Recommender Systems. ACM RecSys 2019. ACM (in press).


Beierle, F. and Tran, V.T. and Allemand, M. and Neff, P. and Schlee, W. and Probst, T. and Zimmermann, J. and Pryss, R. (2019). What data are smartphone users willing to share with researchers? Designing and evaluating a privacy model for mobile data collection apps. Journal of Ambient Intelligence and Humanized Computing. Springer.


Dinh Tuan, H. and Beierle, F. and Rodriguez Garzon, S. (2019). MAIA: A Microservices-based Architecture for Industrial Data Analytics. IEEE International Conference on Industrial Cyber-Physical Systems (accepted for publication)


Lorbeer, B. and Deutsch, T. and Ruppel, P. and Küpper, A. (2019). Anomaly Detection with HMM Gauge Likelihood Analysis. BDS2019 accepted for publication.


Eichinger, T. and Beierle, F. and Khan, S. U. and Middelanis, R. and Sekar, V. and Tabibzadeh, S. (2019). affinity: A System for Latent User Similarity Comparison on Texting Data. IEEE-ICC (to appear)


Zielinski, E. and Schulz-Zander, J. and Ruckstuhl, H. and Artemenko, A. and Ramirez, A. and Zeiger, F. and Mormul, M. and Hetzelt, F. and Beierle, F. and Klaus, H. and Zimmermann, M. and Schellenberger, C. (2019). Secure Real-time Communication and Computing Infrastructure for Industry 4.0 – Challenges and Opportunities. Proceedings 2019 Advanced Communication Networks for Industrial Applications (AIComNets). IEEE (to appear).


Victor, F. and Lüders, B. (2019). Measuring Ethereum-based ERC20 Token Networks. International Conference on Financial Cryptography and Data Security, tbd.


Beierle, F. and Aizawa, A. and Collins, A. and Beel, J. (2019). Choice Overload and Recommendation Effectiveness in Related-Article Recommendations. Analyzing the Sowiport Digital Library. International Journal on Digital Libraries. Springer.


Eichinger, T. and Winter, S. (2019). Regularly varying functions, generalized contents, and the spectrum of fractal strings. Contemporary Mathematics. American Mathematical Society, 63-94.


Lüders, B. and Ruppel, P. (2018). Macroscopic Patterns in Sparse Location Data: Identifying Mobility Prototypes. Proceedings of the 4th IEEE International Conference on Big Data Service and Applications (BigDataService 2018)


Lorbeer, B. and Kosareva, A. and Deva, B. and Softić, D. and Ruppel, P. and Küpper, A. (2017). Variations on the Clustering Algorithm BIRCH. Big Data Research. Elsevier.


Eichinger, T. (2017). The Corpus Replication Task. Proceedings of the 2017 International Conference on Computational Science & Computational Intelligence


Deva, B. and Raschke, P and Rodriguez Garzon, S. and Küpper, A. (2017). STEAM: A Platform for Scalable Spatiotemporal Analytics. 8th International Conference on Ambient Systems, Networks and Technologies, ANT 2017, 731-736.


Victor, F. and Rodriguez Garzon, S. and and Küpper, A. (2017). Smartphone-collected Mobile Network Events for Mobility Modeling. 14th IEEE International Conference on Ubiquitous Intelligence and Computing (UIC 2017)


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