Inhalt des Dokuments

[1]- © Service-centric
Networking
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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Year [20]
Journal [21]
Victor, F. and Ruppel, P. and Kuepper, A. (2021).
A Taxonomy for Distributed Ledger Analytics [22]. IEEE
Computer. IEEE, 30-38.
Beierle, F. and Probst, T. and Allemand, M. and
Zimmermann, J. and Pryss, R. and Neff, P. and Schlee, W. and Stieger,
S. and Budimir, S. (2020). Frequency and duration of daily smartphone
usage in relation to personality traits [23]. Digital
Psychology. facultas, 20–28.
Katsarou, K. and Ounoughi, C. and Mouakher, A.
and Nicolle, C. (2020). STCMS: A Smart Thermal Comfort Monitor For
Senior People [24]. 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 [25]. 4th International Conference
on Cloud and Big Data Computing(ICCBDC 2020). ACM, 68-75.
Grunert, K. (2020). Overview of JavaScript
Engines for Resource-Constrained Microcontrollers [26]. 5th
International Conference on Smart and Sustainable Technologies
2020(SpliTech 2020), 1-7.
Fischer, S. and Katsarou, K. and Holschke, O.
(2020). DEEPFLOW: Toawards Network-Wide Ingress Traffic Prediction
Using Machine Learning At Large Scale [27]. 7th International
Symposium on Networks, Computers and Communications(ISNCC 2020).
IEEE.
Victor, F. (2020). Address Clustering Heuristics
for Ethereum [28]. Financial Cryptography and Data Security.
Springer International Publishing, 617–633.
Müller, M. and Ostern, N. and Rosemann, M.
(2020). Silver Bullet for all Trust Issues? Blockchain-based Trust
Patterns for Collaborative Business Processes [29]. 18th Int.
Conference on Business Process Management (BPM 2020)
Müller, M. and S. Rodriguez Garzon. S. and M.
Rosemann, M. and Küpper, A. (2020). Towards Trust-aware Collaborative
Business Processes: An Approach to Identify Uncertainty [30].
IEEE Internet Computing, 1-1.
Dinh Tuan, H. and Mora Martinez, M. and Beierle,
F. and Rodriguez Garzon, S. (2020). Development Frameworks for
Microservice-based Applications: Evaluation and Comparison (accepted
for publication [31]. 2020 European Symposium on Software
Engineering (ESSE)
Rodriguez Garzon, S. and Louis, B. (2020).
Context Flow Graphs: Situation Modeling for Rule-based Proactive
Context-aware Systems [32]. IEEE Access, 1-22.
Hagemann, T. and Katsarou, K. (2020). A
Systematic Review on Anomaly Detection for Cloud Computing
Environments [33]. 3rd Artificial Intelligence and Cloud Computing
Conference (ISNCC 2020). ACM (accepted for publication).
Beierle, F. and Tran, V.T. and Allemand, M. and
Neff, P. and Schlee, W. and Probst, T. and Zimmermann, J. and Pryss,
R. (2020). What data are smartphone users willing to share with
researchers? Designing and evaluating a privacy model for mobile data
collection apps [34]. Journal of Ambient Intelligence and
Humanized Computing. Springer, 2277–2289.
Beierle, F. and Aizawa, A. and Collins, A. and
Beel, J. (2020). Choice Overload and Recommendation Effectiveness in
Related-Article Recommendations. Analyzing the Sowiport Digital
Library [35]. International Journal on Digital Libraries.
Springer, 231–246.
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.
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[40]
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Ernst-Reuter-Platz 7
10587 Berlin, Germany
Phone: +49 30 8353 58811
Fax: +49 30 8353 58409
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