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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

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.


Rodriguez Garzon, S. and Arbuzin, D. and Küpper, A. (2017). Geofence Index: A Performance Estimator for the Reliability of Proactive Location-Based Services. 2017 18th IEEE International Conference on Mobile Data Management (MDM), 1-10.


S. Rodriguez Garzon and M. Elbehery and B. Deva and A. Küpper (2016). Reliable Geofencing: Assisted Configuration of Proactive Location-based Services. 2016 IEEE International Conference on Mobile Services (MS), 204-207.


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.


Katsarou, K. and Ounoughi, C. and Mouakher, A. and Nicolle, C. (2020). TComf-Predict: A New Tthermal Comfort Prediction Fframework For Elderly People. 28th IEEE International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE 2020). IEEE (accepted for publication).


Hagemann, T. and Katsarou, K. (2020). Reconstruction-based anomaly detection for the cloud: A comparison on the Yahoo!Webscope S5 dataset. 4th International Conference on Cloud and Big Data Computing(ICCBDC 2020). ACM (accepted for publication).


Victor, F. and Hagemann, T. (2019). Cryptocurrency Pump and Dump Schemes: Quantification and Detection. 2019 IEEE International Conference on Data Mining Workshops (ICDMW), tbd.


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).


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. Financial Cryptography and Data Security. Springer International Publishing, 113–129.


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)


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


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