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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

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.


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.


Beierle, F. and Aizawa, A. and Beel, J. (2017). Exploring Choice Overload in Related-Article Recommendations in Digital Libraries. Proceedings of the 5th International Workshop on Bibliometric-enhanced Information Retrieval (BIR2017). CEUR-WS, 51–61.


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.


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)


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).


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)


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


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


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).


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


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.


Lorbeer, B. and Kosareva, A. and Deva, B. and Softić, Dvzenan and Ruppel, P. and Küpper, Axel (2016). A-BIRCH: Automatic Threshold Estimation for the BIRCH Clustering Algorithm. A-BIRCH: Automatic Threshold Estimation for the BIRCH Clustering Algorithm. Springer, 169–178.


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)


Rodriguez Garzon, S. and Arbuzin, D. and Küpper, A. (2017). Geofence Index: A Performance Estimator for the Reliability of Proactive Location-based Services. Proceedings of the 18th IEEE International Conference on Mobile Data Management (IEEE MDM 2017). IEEE.


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