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Data Science

Lupe [1]

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.

Team
  • Tobias Eichinger [2]
  • Katerina Katsarou [3]
  • Boris Lorbeer [4]
  • Bianca Lüders [5]
  • Philip Raschke [6]
  • Dr. [7]  [8]Sandro Rodriguez Garzon [9]
  • Friedhelm Victor [10]

Projects

  • eBiz [11]
  • Indoor Analytics [12]
  • SPECIAL [13]
  • STEAM [14]
  • Street Smart Retail [15]

Publications

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Hagemann, T. and Katsarou, K. (2020). A Systematic Review on Anomaly Detection for Cloud Computing Environments [23]. 3rd Artificial Intelligence and Cloud Computing Conference (AICCC 2020). ACM, 83-96.


Hatzinikolaou D., Katsarou K. (2019). An Account of Principal Components Analysis and Some Cautions on Using the Correct Formulas and the Correct Procedures in SPSS [24]. International Journal of Statistics & Applications. Scientific & Academic Publishing Company, 160-169.


Katsarou, K. and Ounoughi, C. and Mouakher, A. and Nicolle, C. (2020). STCMS: A Smart Thermal Comfort Monitor For Senior People [25]. 28th IEEE International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE 2020). IEEE (accepted for publication).


Katsarou, K. and Shekhawat, D. S. (2019). CRD-SentEnse: Cross-domain Sentiment Analysis using an Ensemble Model [26]. Proceedings of The 11th International ACM Conference on Management of Digital EcoSystems(MEDES 2019). ACM (accepted for publication).


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 [27]. A-BIRCH: Automatic Threshold Estimation for the BIRCH Clustering Algorithm. Springer, 169–178.


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 [28]. A-BIRCH: Automatic Threshold Estimation for the BIRCH Clustering Algorithm. Springer, 169–178.


Lorbeer, B. and Deutsch, T. and Ruppel, P. and Küpper, A. (2019). Anomaly Detection with HMM Gauge Likelihood Analysis [29]. 2019 IEEE Fifth International Conference on Big Data Computing Service and Applications (BigDataService), 1–8.


Lorbeer, B. and Kosareva, A. and Deva, B. and Softić, D. and Ruppel, P. and Küpper, A. (2017). Variations on the Clustering Algorithm BIRCH [30]. Big Data Research. Elsevier.


Lüders, B. and Ruppel, P. (2018). Macroscopic Patterns in Sparse Location Data: Identifying Mobility Prototypes [31]. Proceedings of the 4th IEEE International Conference on Big Data Service and Applications (BigDataService 2018)


Motta, M. and Hagemann, T. and Fischer, S. and Assion, F. (2021). EvolMusic: Towards Musical Adversarial Examples for Black-Box Attacks on Speech-To-Text [32]. 2021 Genetic and Evolutionary Computation Conference Companion (GECCO ’21 Companion), accepted for publication


Rodriguez Garzon, S. and Arbuzin, D. and Küpper, A. (2017). Geofence Index: A Performance Estimator for the Reliability of Proactive Location-Based Services [33]. 2017 18th IEEE International Conference on Mobile Data Management (MDM), 1-10.


Rodriguez Garzon, S. and Louis, B. (2020). Context Flow Graphs: Situation Modeling for Rule-based Proactive Context-aware Systems [34]. IEEE Access, 1-22.


Romiti, M. and Victor, F. and Moreno-Sanchez, P. and Nordholt, P. S. and Haslhofer, B. and Maffei, M. (2021). Cross-Layer Deanonymization Methods in the Lightning Protocol [35]. Financial Cryptography and Data Security. Springer International Publishing.


Victor, F. and Hagemann, T. (2019). Cryptocurrency Pump and Dump Schemes: Quantification and Detection [36]. 2019 IEEE International Conference on Data Mining Workshops (ICDMW), 244–251.


Victor, F. (2020). Address Clustering Heuristics for Ethereum [37]. Financial Cryptography and Data Security. Springer International Publishing, 617–633.


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