Dr.-Ing. Florian Schmidt

Distributed Intelligence Systems
for Industry 4.0, smart factories, and precision medicine

I am a Senior Researcher (PostDoc) at the DOS-group TU Berlin.
I recieved my PhD for "Anomaly detection in cloud computing environments" at the TU Berlin in 2020. I studied Computer Science at the TU Berlin (M.Sc.) and Leibniz Universität Hannover (B.Sc.).
Awards/Certifications: Forum Junge Spitzenforscher Award for innovative AI 2020, Hochschuldidaktisches Zertifikat 2019
Publications: Google Scholar
Main tech stack: Java & Python, Docker, Kubernetes - Github
Social networks: ResearchGate, LinkedIn, Instagram, Youtube
Organizations I worked at: UKM, Zalando, ICCAS , LUH, ISAH
Industrial projects I worked for: ATOS, BT, Huawei, Deutsche Telekom, Siemens, KSB
I am open for contract work. Areas: IoT sensor data analysis, Digitalization/Industry 4.0, and Edge/Cloud-service architectures.
Contact: florian.schmidt@tu-berlin.de

Latest News and Projects

Distributed Intelligence

Industrial examples and research for automated, cognitive intelligence assistance for the Industy 4.0 and medical field. This research area combines AI-based analysis pipelines with distributed edge cloud continuum deployments. Click "View" for more interesting details.

comming soon

Anomaly Detection for Data Streams

Projects and Algorithms tackling the task of detecting abnormal behaviour within a high frequent data stream using unsupervised techniques.

Towards in-situ anomaly detection for edge clouds

Invited talk for the Sensor AI workshop 2020.

ATOS IT-challenge 2020 submission

Anomaly detection demonstration of out unsupervised online technique IFTM applied to cloud computing anomalies.

Unsupervsied Anomaly Detection - IFTM

Telecommunication system providers move their IP multimedia subsystems to virtualized services in the cloud. For such systems, dedicated hardware solutions provided a reliability of 99.999% in the past. Although virtualization offers more cost efficient usage of such services, it comes with higher complexity for providing reliable running software components due to the fragile computation stack. In order to hide the impact of such problematic behaviors, automatic mechanisms may help to detect degraded state anomalies in order to execute remediation actions. This work introduces IFTM as a framework for unsupervised anomaly detection in a distributed environment based on real-time monitoring data. The proposed approach consists of two key concepts using an automatic identity function and threshold learning to distinguish between normal and abnormal system behaviors. The evaluation is performed on a testbed running an open source implementation of the IP multimedia subsystem (Clearwater) executed on a replicated Openstack cloud environment. Results show the applicability of IFTM with high detection rates (98%) and low number of false alarms.
IEEE Services 2018

Anomaly Detection for the Smart Factory

Cooperation project with Siemens aiming to integrate anomaly detection algorithms in to the industrial factories.

Anomaly Detection for Water Pumps

Cooperation project with KSB aiming to provide anomaly detection forecastings for water pumps for predictive maintenance.

Self-Healing Cloud

Within this cooperation project, we aim to create an immune system for computers. It should detect automatically anomalies, consider the root cause and select the appropriate counter actions to remediate the anomaly. Novel algorithms and testbeds are created together with our project partners Huawei Technologies and Deutsche Telekom.
Digital Education

Information about digital tools for visualization and simulating complex concepts for students.

Invited Talk at Tag der Lehre

I presented at the TU Berlin the newest developments of SysprogInteract. More information here: https://www.zewk.tu-berlin.de/v_menue/wissenschaftliche_weiterbildung/lehren_und_lernen/tag_der_lehre

SysprogInteract Extended Visualizations

We added further visual animations for memory placement strategies and resource usage.

SysprogInteract CPU-scheduling simulations

First CPU-scheduling visualization component for SysprogInteract. Please feel free to checkout the video and github repository.

TU Wimi+

TU Wimi+ is part of an BMBF project aiming to increase the quality of education. It consists of several smaller projects, which create new didactical media for including those into the education process. For the undergraduate computer science course Systemprogrammierung, we develop a novel simulation framework providing an interactive computer system simulation. Project link from ZEWK and CIT.
Advanced Visualizations

Visualization is crutial for human understanding of complex data and analytics results. Next, you can see novel visualization techniques.

Interactive Decision Tree Visualization

Visualizing high dimensional decision tree models and data sets in the plane. Please feel free to checkout the video and github repository.

ViMEC Interactive Application for Micro-Cluster Visualizations

Digitalization increases the opportunity to collect vast amounts of data in a large scale manner. In order to handle the information overload, data mining techniques like online clustering are performed. A lot of online clusterers are based on the concept of micro-clusters in order to represent the given data stream. Based on its definition, micro-clusters can be represented as an n-sphere. Online clustering algorithms like BIRCH or DenStream use different strategies for maintaining the micro-clusters in evolving time series, but using the same underlying key concept storing a summarized version of the data stream in their models. We propose ViMEC, an application for multidimensional micro-cluster visualization, giving the user the opportunity to gain understanding of the internal behaviour of the clustering model. For a given time frame, ViMEC gives the user three different types of visualizations presenting different levels of details: Overview, Pair-view and Detail-view. These views combine not only a summary and detail representations for the different dimensions, but also aim to show different relations between dimensions. Preliminary results show, that large data sets with up to 20,000 data points can be visualized within less than 20 seconds.
Please feel free to checkout the video and paper.

Medical Decision Tree Visualization

Information overload in the medical domain is both visible in the increased number of publications as well as in the volume of patient data. In order to cope with this problem, we propose a novel framework combining patient’s health records with medical knowledge, which is based on medical algorithms from frequently used guidelines. The framework uses new types of animation and layout algorithms for visualizing knowledge models in health records. At the Münster University Hospital the framework is already in prototypical use for education and communication purposes.
Please feel free to checkout the video and paper.


Java is not so nice when it comes to data visualizations. Javascript is instead very powerful for both creating visualizations, but also for creating advanced animations. JavaD3 provides a simple Java API to create visualizations like Timeseries plots, Histograms etc. In future, we are going to extend this library to create also animations. Checkout the github repository.
Big-Data Analytics

More information are provided about an EIT-Digital project MCloudDaaS and graph analytics.

EIT-Digital project MCloudDaaS

Multi-Cloud Data Analytics as a Service (MCloudDaaS) combines the benefits of Big Data Analytics and Multi-Cloud Systems. Many companies struggle to find a convenient and affordable data analytic solution. Most cloud providers does not provide an easy to use cloud system Furthermore, clients have concerns like the lack of security, which leads to a mistrust against cloud services. In order to overcome such barriers, MCloudDaaS provides innovative functionalities with regards to security, scalability and fault-tolerance. In addition, MCloudDaaS avoids a vendor lock-in of the customers, who would be in a traditional cloud system economically dependent of the provided DaaS solutions and therefore increasing costs when moving to a different cloud provider. To increase the trust of the customer, high security standards will be integrated. Moreover, MCloudDaaS provides rich management functionalities, predictions of performance and management for scalability. As a result, this leads to CAPEX reduction, because customers can switch to a cheaper provider.
Project partners: Atos, BT, CNR, TUB

Graph Processing with Grafungi

The simple graph library. It contains an embedded graph database, webservice for multi entity graphs and bayesian networks. Checkout the github repository.


Ferrer, A.J., Becker, S., Schmidt, F., Thamsen, L. and Kao, O., 2021. Towards a Cognitive Compute Continuum: An Architecture for Ad-Hoc Self-Managed Swarms. arXiv preprint arXiv:2103.06026. (accepted at Cloud2Things 2021)

Bogatinovski, J., Nedelkoski, S., Acker, A., Schmidt, F., Wittkopp, T., Becker, S., Cardoso, J. and Kao, O., 2021. Artificial Intelligence for IT Operations (AIOPS) Workshop White Paper. arXiv preprint arXiv:2101.06054.

Becker, Sören, Schmidt, Florian, Gulenko, Anton, Acker, Acker and Kao, Odej, 2020, December. Towards AIOps in Edge Computing Environments. In 2020 IEEE International Conference on Big Data (Big Data) (pp. 3470-3475). IEEE.

Styp-Rekowski, Kevin, Schmidt, Florian and Kao, Odej, 2020, December. Optimizing Convergence for Iterative Learning of ARIMA for Stationary Time Series. In 2020 IEEE International Conference on Big Data (Big Data) (pp. 2217-2222). IEEE.

Gulenko, A., Acker, A., Schmidt, F., Becker, S. and Kao, O., 2020, August. Bitflow: An In Situ Stream Processing Framework. In 2020 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C) (pp. 182-187). IEEE.

Börner, A., Hübers, H.W., Kao, O., Schmidt, F., Becker, S., Denzler, J., Matolin, D., Haber, D., Lucia, S., Samek, W. and Triebel, R., 2020. Sensor Artificial Intelligence and its Application to Space Systems--A White Paper. arXiv preprint arXiv:2006.08368.

Anton Gulenko, Odej Kao, Florian Schmidt. Anomaly Detection and Levels of Automation for AI-Supported System Administration. Information Management and Big Data. SIMBig 2019. Communications in Computer and Information Science, vol 1070. Springer.

René Wetzig, Anton Gulenko, Florian Schmidt. Unsupervised Anomaly Alerting for IoT-Gateway Monitoring using Adaptive Thresholds and Half-Space Trees. IEEE International Conference on Internet of Things: Systems, Management and Security. IEEE, 2019.

Daniel Thilo Schroeder, Kevin Styp-Rekowski, Florian Schmidt, Alexander Acker and Odej Kao. Graph-based Feature Selection Filter Utilizing Maximal Cliques. In 2019 IEEE International Conference on Social Networks Analysis, Management and Security (SNAMS).

Florian Schmidt, Johannes Ohlemacher, Vincent Hennig. Case Study: Visualizing Computer System Programming Concepts for Education. SEFI 2019.

Florian Schmidt, Franz-Josef Schmitt, Laura Boeger, Arno Wilhelm-Weidner, and Nicole Torjus. Digital Teaching and Learning Projects in Engineering Education at Technische Universität Berlin. ASEE 2019.

Florian Schmidt, Florian Suri-Payer, Anton Gulenko, Marcel Wallschläger, Alexander Acker, and Odej Kao. Unsupervised Anomaly Event Detection for Cloud Monitoring using Online Arima. 11th IEEE/ACM International Conference on Utility and Cloud Computing (UCC 2018). IEEE, 2018.

Alexander Acker, Florian Schmidt, Anton Gulenko, and Odej Kao. Online Density Grid Pattern Analysis to Classify Anomalies in Cloud and NFV Systems. IEEE International Conference on Cloud Computing Technology and Science (CloudCom). IEEE, 2018.

Florian Schmidt, Florian Suri-Payer, Anton Gulenko, Marcel Wallschläger, Alexander Acker, and Odej Kao. Unsupervised Anomaly Event Detection for VNF Service Monitoring using Multivariate Online Arima. IEEE International Conference on Cloud Computing Technology and Science (CloudCom). IEEE, 2018.

Marcel Wallschläger, Anton Gulenko, Florian Schmidt, Alexander Acker and Odej Kao. Anomaly Detection for Black Box Services in Edge Clouds Using Packet Size Distribution. 2018 IEEE 7th International Conference on Cloud Networking (CloudNet). IEEE, 2018.

Dora Szücs and Florian Schmidt. Decision Tree Visualization for High-dimensional Numerical Data. In 2018 IEEE International Conference on Social Networks Analysis, Management and Security (SNAMS). IEEE, 2018.

Florian Schmidt, Anton Gulenko, Marcel Wallschläger, Alexander Acker, Vincent Kennig, Feng Liu, and Odej Kao. IFTM - unsupervised anomaly detection for virtualized network function services. In 2018 IEEE International Conference on Web Services (ICWS). IEEE, 2018.

Anton Gulenko, Florian Schmidt, Alexander Acker, Marcel Wallschläger, Feng Liu, and Odej Kao. Detecting anomalous behavior of black-box services modeled with distance-based online clustering. In 2018 IEEE International Conference on Cloud Computing (CLOUD). IEEE, 2018.

Florian Schmidt and Yannick Ehrenfeld. ViMEC: Interactive Application for Micro-Cluster Visualizations. In EuroVis 2018 - Posters. The Eurographics Association, 2018.

Marcel Wallschläger, Anton Gulenko, Florian Schmidt, Odej Kao, and Feng Liu. Automated anomaly detection in virtualized services using deep packet inspection. Procedia Computer Science, 110:510-515, 2017.

Alexander Acker, Florian Schmidt, Anton Gulenko, Reinhard Kietzmann, and Odej Kao. Patient-individual morphological anomaly detection in multi-lead electrocardiography data streams. In 2017 IEEE International Conference on Big Data (Big Data), pages 3841-3846. IEEE, 2017.

Leon L Seifert, Anna Husing-Kabar, Florian Schmidt, Hauke Heinzow, and Hartmut H Schmidt. Evaluation of potential risk factors and the role of medication regimen in the development of hepatic encephalopathy after transjugular intrahepatic portosystemic shunting in cirrhotic patients. In HEPATOLOGY, volume 66, pages 277A-277A. WILEY 111 RIVER ST, HOBOKEN 07030-5774, NJ USA, 2017.

Jannis Koch, Lauritz Thamsen, Florian Schmidt, and Odej Kao. Smipe: Estimating the progress of recurring iterative distributed data ows. In 2017 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT), pages 156-163. IEEE, 2017.

Lauritz Thamsen, Benjamin Rabier, Florian Schmidt, Thomas Renner, and Odej Kao. Scheduling recurring distributed data ow jobs based on resource utilization and interference. In 2017 IEEE International Congress on Big Data (BigData Congress), pages 145-152. IEEE, 2017.

Florian Schmidt, Vincent Hennig, Sarah Köhler, Marcel Wallschläger, Anton Gulenko, Hartmut Schmidt, and Odej Kao. Novel framework combining health records with medical algorithms. In Proceedings of the 11th EAI International Conference on Pervasive Computing Technologies for Healthcare, pages 480-481. ACM, 2017.

Anton Gulenko, Marcel Wallschläger, Florian Schmidt, Odej Kao, and Feng Liu. A system architecture for real-time anomaly detection in large-scale nfv systems. Procedia Computer Science, 94:491-496, 2016.

Anton Gulenko, Marcel Wallschläger, Florian Schmidt, Odej Kao, and Feng Liu. Evaluating machine learning algorithms for anomaly detection in clouds. In 2016 IEEE International Conference on Big Data (Big Data), pages 2716-2721. IEEE, 2016.

Lauritz Thamsen, Ilya Verbitskiy, Florian Schmidt, Thomas Renner, and Odej Kao. Selecting resources for distributed data ow systems according to runtime targets. In 2016 IEEE 35th International Performance Computing and Communications Conference (IPCCC), pages 1-8. IEEE, 2016.

Matthias Becker, Florian Schmidt, and Helena Szczerbicka. Applicability of bio-inspired and graph-theoretic algorithms for the design of complex fault-tolerant graphs. In 2013 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pages 2730-2734. IEEE, 2013.

Community Activities

Speaker for invited talks Workshop Speaker at DevOpsCon 2021
Stadtbibliothek Charlottenburg, Berlin (Corona Warnapp, 5G) 2020
SensorAI 2020
Lunch für gute Lehre 2019
Tag der Lehre 2018, 2017
University committees Beirat mindSET (Erasmus +) 2019-2021
Beirat Strategie der Wissenschaftlichen Weiterbildung TU Berlin 2019
Program committees AIOPs 2020 (organizer)
IDSTA 2020
DSEA 2019


SS20 Systemprogrammierung with more than 800 students - Digital Semester
WS19/20 Master Project Distributed Systems
SS19 Systemprogrammierung with more than 700 students
WS18/19 Master Project Distributed Systems
SS18 Systemprogrammierung with more than 800 students
WS17/18 Master Project Distributed Systems
SS17 Systemprogrammierung with more than 700 students
WS16/17 Bachelor Project Distributed Systems
SS16 Seminar Distributed Systems (Master & Bachelor)

Supervised Master Theses

Supervised Bachelor Theses
Visualization of Micro-Clusters in Big Data Streams
Kerem Dede, 2020
Animierte Visualisierung von Real-time Zeitreihendaten in Java
Jan Behrens, 2019
Design and development of a real-time data stream aggregation machanism for visulization
Martin Kachev, 2019
Animierte Visualisierung von Micro Clustern in hochfrequenten Zeitreihendaten
Laura Oppermann, 2019
Interaktive Visualisierung verteilter Datenströme
Florian Teich, 2019
Streaming Half-Space Trees: Ein Machine-Learning-Algorithmus zur Anomalieerkennung in hochfrequenten Datenströmen mit Anwendungsbeispiel für AIOps
René Wetzig, 2019
Interaktive Simulationen von Prozessverhalten in Computersystemen für die Lehre
Johannes Ohlemacher, 2019
Automatische Bewertung von Praxisaufgaben in der Systemprogrammierung
Borys Fernando Bustos Galarza, 2018
An Online Microclustering Algorithm for Tissue Segmentation in Contrast Enhanced Ultrasound Videos
Fabian Hofmann, 2018
Unsupervised Anomaly Detection using Iterative ARIMA
Florian Suri, 2018
Bildstabilisierungsverfahren von Ultraschallvideos
Tim Spankowski, 2018
Simulation und Visualisierung von Algorithmen für die Systemprogrammierung
Clemens Gläser, 2018
Interaktive Visualisierung von Entscheidungsbäumen bei höherdimensionalen Daten
Dora Szücs, 2018
Interaktive Visualisierung multidimensionaler Echtzeit-Cluster ovn Sensordaten
Yannik Ehrenfeld, 2017
Analysis for Tissue Recognition in Contrast Enhanced Ultrasound Videos
Sebastian Petrausch, 2017
Nutzerdefinierte Visualisierung von Trackingdaten für mobile Geräte
Christian Hoffmann, 2017
Interaktive Visualisierung graphenbasierter Entscheidungsmodelle für mobile Geräte
Vincent Hennig, 2017
Visualisierung probabilistischer Netze als Empfehlungssystem für die Medizin
Clara Simon, 2017
Webbasierte Visualisierung medizinischer Entscheidungsalgorithmen
Sarah Köhler, 2017
Big Data Graph Processing Dataflow Management
David Grigorijan, 2016
Interaktive Graph-basierte visuelle Darstellung von Big Data
Scott Viet Phong Nguyen, 2016