Distributed Systems

Bachelor Projects

Transfer Learning for Short Term Load Forecasting

Supervisor: Dilek Dustegor.
Status: available.
Date: 24/01/2023.
Accurate short term load forecasting is essential in modern energy systems. This has become possible in recent years with the application of multiple machine learning and deep learning models, provided that significant training data is available. New buildings do not have historical data to feed a model to predict their energy consumption. To counteract the lack of historical data, the literature reports Transfer Learning (TL) as a potential solution. TL consists in developing models on existing source domain (e.g. different building), then the pre-trained models are used on a the target domain (i.e. new building). In this project, the student will develop, optimize and compare deep learning based transfer learning models for energy consumption forecasting, utilizing historical consumption data from the Enernoc dataset.

  1. Yassine Himeur, Mariam Elnour, Fodil Fadli, Nader Meskin, Ioan Petri, Yacine Rezgui, Faycal Bensaali, Abbes Amira, "Next-generation energy systems for sustainable smart cities: Roles of transfer learning," Sustainable Cities and Society 85 (2022) 104059, https://doi.org/10.1016/j.scs.2022.104059
  2. Y.-K. Juan, P. Gao, and J. Wang, "A hybrid decision support system for sustainable office building renovation and energy performance improvement," Energy and Buildings 42-3 (2010), https://doi.org/10.1016/j.enbuild.2009.09.006
  3. Y. Ahn and B. S. Kim, "Prediction of building power consumption using transfer learning based reference building and simulation dataset," Energy and Buildings 258 (2022), https://doi.org/10.1016/j.enbuild.2021.111717

Leakage Detection in Water Network

Supervisor: Dilek Dustegor.
Status: available.
Date: 24/01/2023.
Leaks in water distribution networks (WDNs) are one of the main reasons for water loss during transportation. Considering water scarcity, combined with a growing population worldwide, it is an urgent humanitarian need to minimize water losses. Lately, some attempts have been made to use data-driven and machine learning techniques for leakage localization. But capabilities and limitations of these methods are not clearly understood. In this project, the student will develop, optimize, and compare several machine learning models for leak detection purposes in a water network.

References:

  1. Marcos Quiñones-Grueiro, Marlon Ares Milián, Maibeth Sánchez Rivero, Antônio J. Silva Neto, Orestes Llanes-Santiago, "Robust leak localization in water distribution networks using computational intelligence," Neurocomputing 438 (2021) 195–208, https://doi.org/10.1016/j.neucom.2020.04.159
  2. Chan-Wook Lee and Do-Guen Yoo, "Development of Leakage Detection Model and Its Application for Water Distribution Networks Using RNN-LSTM," Sustainability 2021, 13, 9262, https://doi.org/10.3390/su13169262
  3. Jie Zhang, Xiaoping Yang and Juan Li, "Leak localization of water supply network based on temporal convolutional network," Meas. Sci. Technol. 33 (2022) 125302 (8pp), https://doi.org/10.1088/1361-6501/ac8ca5
  4. Zahra Fereidooni, Hooman Tahayori, Ali Bahadori‑Jahromi, "A hybrid model‑based method for leak detection in large scale water distribution networks," Journal of Ambient Intelligence and Humanized Computing (2021) 12:1613–1629, https://doi.org/10.1007/s12652-020-02233-2

Node masking in Graph Neural Networks

Supervisor: Huy Truong.
Status: available.
Date: 22/01/2023.
Working with data in the real world often leads to missing information problems which can negatively affect the performance of deep learning models. However, in proper ways, it can boost the expressiveness of Graph Neural Network (GNN) models in node representation learning through a technique known as Node Masking. In particular, it hides arbitrary nodal features in a graph and instructs the GNN to recover the missing parts. The student can explore diverse masking strategies, such as zero masking, random node replacement, mean-neighbor substitution, shared learnable embedding, and nodal permutation. These options above should be compared and evaluated in a graph reconstruction task that applies to a water distribution network. This study will focus on finding a generative technique that effectively enhances the performance of GNN models in semi-supervised transductive learning.

References:

  1. Hou, Zhenyu, Xiao Liu, Yuxiao Dong, Chunjie Wang, and Jie Tang. "GraphMAE: Self-Supervised Masked Graph Autoencoders." arXiv preprint arXiv:2205.10803(2022).
  2. Abboud, Ralph, Ismail Ilkan Ceylan, Martin Grohe, and Thomas Lukasiewicz. "The surprising power of graph neural networks with random node initialization." arXiv preprint arXiv:2010.01179 (2020).
  3. Hajgató, Gergely, Bálint Gyires-Tóth, and György Paál. "Reconstructing nodal pressures in water distribution systems with graph neural networks." arXiv preprint arXiv:2104.13619 (2021).
  4. He, Kaiming, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick. "Masked autoencoders are scalable vision learners." In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 16000-16009. 2022.

Graph Neural Networks for Human Activity Recognition

Supervisor: Andrés Tello.
Status: available.
Date: 22/01/2023.
Graph Neural Networks (GNNs) are a proven approach for learning complex relationships in data whose underlying structure can be modeled as a graph. The aim of this work is to perform an empirical evaluation of GNNs for Human Activity Recognition. Data collected from smartphones and wearables while people perform different activities (e.g. sitting, standing, walking) will be used to evalute the GNNs ability to recognize human activities. The assumption is that the signals collected from smartphones and wearables have underlying structural relationships that can uniquely characterize each activity. Then, GNNs can learn to categorize the graphs associated to Human Activities into classes. The students are expected to propose a GNN-based classification model taking into account different strategies for modeling sensor data as a graph and evaluate the performance of the proposed methods.

References:

  1. Mohamed, A., Lejarza, F., Cahail, S., Claudel, C., & Thomaz, E. (2022, March). HAR-GCNN: Deep Graph CNNs for Human Activity Recognition From Highly Unlabeled Mobile Sensor Data. In 2022 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops) (pp. 335-340). IEEE.
  2. Yan, Y., Liao, T., Zhao, J., Wang, J., Ma, L., Lv, W., ... & Wang, L. (2022). Deep transfer learning with graph neural network for sensor-based human activity recognition. arXiv preprint arXiv:2203.07910.
  3. Nian, A., Zhu, X., Xu, X., Huang, X., Wang, F., & Zhao, Y. (2022, August). HGCNN: Deep Graph Convolutional Network for Sensor-Based Human Activity Recognition. In 2022 8th International Conference on Big Data and Information Analytics (BigDIA) (pp. 422-427). IEEE.
  4. W. Huang, L. Zhang, W. Gao, F. Min and J. He, "Shallow Convolutional Neural Networks for Human Activity Recognition Using Wearable Sensors," in IEEE Transactions on Instrumentation and Measurement, vol. 70, pp. 1-11, 2021, Art no. 2510811, doi: 10.1109/TIM.2021.3091990.

Data Streams Scheduling and Routing with In-network Computing

Supervisor: Bochra Boughzala.
Status: available.
Date: 22/01/2023.
In the context of industrial Internet, automation and sensing devices are continuously monitoring and controlling the environment and exchanging time-sensitive data over a shared network infrastructure. The industrial communication relies on the PubSub messaging paradigm where a mix of real-time and non-real time data streams are presenting conflicting requirements in terms of the Quality of Service (QoS). Typically high reliability and low latency are required for real-time data streams, while best effort delivery can be used for non-real time services. In this project, our goal is to leverage Software-Defined Networking (SDN) and in-network computing, and use the network programmability in the switches to devise efficient methods for routing and scheduling algorithms of the mixed data streams. The data streams scheduling aims at minimizing the queueing delays while the routing is for selecting the best path to avoid congestion links.

References:

  1. Silva, Luis, Paulo Pedreiras, Pedro Fonseca, and Luis Almeida. "On the adequacy of SDN and TSN for Industry 4.0." In 2019 IEEE 22nd International Symposium on Real-Time Distributed Computing (ISORC), pp. 43-51. IEEE, 2019.
  2. Bhowmik, Sukanya, Muhammad Adnan Tariq, Boris Koldehofe, Frank Dürr, Thomas Kohler, and Kurt Rothermel. "High performance publish/subscribe middleware in software-defined networks." IEEE/ACM Transactions on Networking 25, no. 3 (2016): 1501-1516.
  3. Bosshart, Pat, Dan Daly, Glen Gibb, Martin Izzard, Nick McKeown, Jennifer Rexford, Cole Schlesinger et al. "P4: Programming protocol-independent packet processors." ACM SIGCOMM Computer Communication Review 44, no. 3 (2014): 87-95.

In-Network Complex Event Detection with Programmable Data Planes

Supervisor: Bochra Boughzala.
Status: available.
Date: 22/01/2023.
Complex Event Processing (CEP) is an essential service of industrial Internet through which meaningful situations can be detected from the basic communication messages of the IoT devices. The reliable and timely detection of complex events is crucial for these industrial applications. In this project, we aim at leveraging Software-Defined Networking (SDN) and in-network computing, and use the network programmability in the switches to devise a solution for line-rate in-network complex event detection. The goal is to design an SDN-based PubSub oriented service for the reliable detection of complex patterns and events through the configuration of filters in the switches using programmable data planes and specifically the P4 programming language.

References:

  1. Kohler, Thomas, Ruben Mayer, Frank Dürr, Marius Maaß, Sukanya Bhowmik, and Kurt Rothermel. "P4CEP: Towards in-network complex event processing." In Proceedings of the 2018 Morning Workshop on In-Network Computing, pp. 33-38. 2018.
  2. Vestin, Jonathan, Andreas Kassler, and Johan Åkerberg. "FastReact: In-network control and caching for industrial control networks using programmable data planes." In 2018 IEEE 23rd International Conference on Emerging Technologies and Factory Automation (ETFA), vol. 1, pp. 219-226. IEEE, 2018.
  3. Mayer, Ruben, Boris Koldehofe, and Kurt Rothermel. "Predictable low-latency event detection with parallel complex event processing." IEEE Internet of Things Journal 2, no. 4 (2015): 274-286.

Applications of Trusted Execution Environments (TEE) in IoT

Supervisor: Majid Lotfian Delouee.
Status: available.
Date: 18/01/2023.
Employing a Trusted Execution Environment (TEE) is considered a way of protecting the security and privacy of data. In other words, a trusted execution environment is a secure and isolated environment that prevents unauthorized access or modification of applications and data while they are in use. In this research project, students are asked to elaborate on the main features as well as the pros and cons of TEEs. In addition, it is expected to perform research on the applications of TEEs in the Internet of things environments and how can they provide an acceptable level of privacy for application users. Finally, students are expected to propose novel ideas to employ TEE in the Internet of Things environment (e.g., at the edge layer) to preserve privacy efficiently.

  1. Jauernig, P., Sadeghi, A.R. and Stapf, E., 2020. Trusted execution environments: properties, applications, and challenges. IEEE Security & Privacy, 18(2), pp.56-60.
  2. Valadares, D.C.G., Will, N.C., Caminha, J., Perkusich, M.B., Perkusich, A. and Gorgônio, K.C., 2021. Systematic literature review on the use of trusted execution environments to protect cloud/fog-based Internet of Things applications. IEEE Access, 9, pp.80953-80969.
  3. Will, N.C., 2022, April. A Privacy-Preserving Data Aggregation Scheme for Fog/Cloud-Enhanced IoT Applications Using a Trusted Execution Environment. In 2022 IEEE International Systems Conference (SysCon) (pp. 1-5). IEEE.

How Complex Event Processing Can Benefit from Federated Learning

Supervisor: Majid Lotfian Delouee.
Status: available.
Date: 18/01/2023.
Complex event processing system (CEP) is a paradigm to analyze input streams (e.g., IoT sensory data) to generate high-level information in real-time. To achieve a higher quality of results, a CEP middleware requires as much as possible data while data owners are not willing to deliver them due to privacy reasons. Federated Learning (FL) is one of the machine learning approaches which allows data owners to train learning models locally and send the models instead of raw sensed data. This ensures a higher level of preserving privacy while improving the quality of results. In this research project, students are asked to elaborate on the main components and pros and cons of both CEP and FL. Finally, students are expected to discuss and propose novel ideas to show the possibilities of improving the performance of CEP systems using the FL paradigm.

  1. Banabilah, S., Aloqaily, M., Alsayed, E., Malik, N. and Jararweh, Y., 2022. Federated learning review: Fundamentals, enabling technologies, and future applications. Information Processing & Management, 59(6), p.103061.
  2. Dayarathna, M. and Perera, S., 2018. Recent advancements in event processing. ACM Computing Surveys (CSUR), 51(2), pp.1-36.
  3. Roldán, J., Boubeta-Puig, J., Martínez, J.L. and Ortiz, G., 2020. Integrating complex event processing and machine learning: An intelligent architecture for detecting IoT security attacks. Expert Systems with Applications, 149, p.113251.

In-network Packet Classification Using Finite State Automata

Supervisor: Saad Saleh.
Status: available.
Date: 20/01/2023.
The current generation of in-network devices heavily build on Ternary content addressable memories (TCAMs) with huge power consumption and large space requirements. To counter the shortcomings of traditional TCAM-based architectures, this project aims to develop and analyze finite state automata for in-network packet classification. The state machines would be designed in context to the response provided by Cognitive materials, called Memristors, built at the University of Groningen. Memristors can provide a range of states with varying input-output response and can deploy state machines with less energy consumption. The project would focus on analytical analysis and network simulations for state-based packet classification mechanisms.

  1. Pontarelli, Salvatore, et al. "FlowBlaze: Stateful Packet Processing in Hardware." 16th USENIX Symposium on Networked Systems Design and Implementation (NSDI 19). 2019.
  2. Baboescu, Florin, Sumeet Singh, and George Varghese. "Packet classification for core routers: Is there an alternative to CAMs?." IEEE INFOCOM 2003. Twenty-second Annual Joint Conference of the IEEE Computer and Communications Societies (IEEE Cat. No. 03CH37428). Vol. 1. IEEE, 2003.
  3. Zhang, Xiaoquan, et al. "A survey on stateful data plane in software defined networks." Computer Networks 184 (2021): 107597.

Modeling and Analysis of Network Traffic Flows

Supervisor: Saad Saleh.
Status: available.
Date: 20/01/2023.
With the growing user base, the Internet has been proliferated by a range of traffic categories requiring different Quality of Service (QoS) guarantees in terms of delay, throughout and jitter for the end applications. Consequently, the underlying network transport mechanisms, like congestion control and flow control, are facing challenges in providing satisfactory QoS to the end applications. In this project, we aim to analyze the requirements of various network traffic flows for providing satisfactory QoS to various network traffic categories. The project would model and analyze the traffic flows using simulations and propose novel techniques for enhancing the network performance.

  1. A. Iqbal, U. Javed, S. Saleh, J. Kim, J. S. Alowibdi and M. U. Ilyas, "Analytical Modeling of End-to-End Delay in OpenFlow Based Networks," in IEEE Access, vol. 5, pp. 6859-6871, 2017.
  2. Al-Turjman, Fadi. "Information-centric framework for the Internet of Things (IoT): Traffic modeling & optimization." Future Generation Computer Systems 80 (2018): 63-75.
  3. Arun, Venkat, Mohammad Alizadeh, and Hari Balakrishnan. "Starvation in end-to-end congestion control." Proceedings of the ACM SIGCOMM 2022 Conference. 2022.

Accelerating Big Data Analytics using Heterogeneous Resources.

Supervisor: Boris Koldehofe, Pratyush Agnihotri (External).
Status: unavailable.
Date: 31/08/2022.
With the proliferation of the Internet of Things (IoT) devices, it is estimated that the data volume created by IoT connections is projected to grow as well. Processing this data can be helpful to various application domains such as online gaming, fraud detection, etc. to infer timely meaningful information to provide faster and better services to end-users. However, it has become challenging to accelerate the performance to meet the quality of service (QoS) demands of end-users with a higher and varying workload. In such a scenario, hardware accelerators can be extremely useful to meet QoS demands. As a part of this research project, students aim to analyze existing related literature related to hardware acceleration, followed by formalizing the problem, modeling the solution, and implementing the proof of concept or prototype. In addition, there are required to perform an evaluation of the proposed solution in comparison to the existing approaches.

Modeling and analysis of process models using Colored Petri nets

Supervisor: Heerko Groefsema.
Status: Available.
Date: 31/10/2022.
Petri nets are mathematical models that can be used to model distributed systems. In our research, we use XML-based Place/Transition nets to obtain verifiable models of business processes. To increase expressivity, we are interested in support for Colored Petri nets. In this project, the student will investigate Colored Petri nets, design and implement Colored Petri nets using the Petri Net Markup Language, and implement its semantics.

  1. Kurt Jensen and Lars M. Kristensen. 2015. Colored Petri nets: a graphical language for formal modeling and validation of concurrent systems. Commun. ACM 58, 6 (June 2015), 61–70.
  2. Hillah, Lom M., et al. "A primer on the Petri Net Markup Language and ISO/IEC 15909-2." Petri Net Newsletter 76 (2009): 9-28.
  3. BPMPetriNet package

The BPMN token game

Supervisor: Heerko Groefsema.
Status: Available.
Date: 31/10/2022.
The Business Process Model and Notation (BPMN) is the de facto industry standard for modeling and executing business processes. In research, however, it is common to model business processes using Petri nets, which use a token game for its execution semantics. In this project, the student is asked to bridge this gap by designing and implementing the BPMN standard and its semantics as such a token game.

  1. OMG. Business process model and notation (BPMN) version 2.0, 2011.
  2. Remco M. Dijkman, Marlon Dumas, Chun Ouyang, Semantics and analysis of business process models in BPMN, Information and Software Technology, Volume 50, Issue 12, 2008, Pages 1281-1294.
  3. BPMPetriNet package

Verification of Security and Privacy concepts in BPMN Choreography diagrams

Supervisor: Heerko Groefsema.
Status: Available.
Date: 31/10/2022.
Where process models define the flow of activities of participants, choreographies describe interactions between participants. Within such interactions, the security and privacy related concepts of separation of duties and division of knowledge are important. The former specifies that no one person has the privileges to misuse the system, either by error or fraudulent behavior, while the latter defines the absence of total knowledge within a single person, such that the knowledge can not be abused. The problem is, how do we specify such concepts and what kind of model is required to verify these concepts? In this project we ask the student to devise an approach to formally specify and verify these concepts given a BPMN Choreography Diagram.

  1. OMG. Business process model and notation (BPMN) version 2.0, 2011.
  2. Pullonen, Pille & Matulevičius, Raimundas & Bogdanov, Dan. (2017). PE-BPMN: Privacy-Enhanced Business Process Model and Notation. 40-56.
  3. BPMVerification package

Obtaining Alignments from Transition Graphs

Supervisor: Heerko Groefsema.
Status: Available.
Date: 31/10/2022.
The practice of checking conformance of business process models has revolutionized the industry through the amount of insight it creates into the process flows of businesses. Conformance checking entails matching an event log (which details events of past executions) against a business process model (which details the prescribed process flow) through a so called alignment. Any deviation from the prescribed process flow is detected and reported. Generally, alignments are obtained by matching the so called token replay of process models (e.g., Petri nets) against events in logs. Our Transition Graphs are also obtained from token replays, but offer further insight into parallel executions than regular Reachability Graphs. As a result, we are interested in the applicability of obtaining alignments using Transition Graphs, especially when matched against event logs that include lifecycle events and thus offer parallel execution data. In this project we ask the student to implement and evaluate the applicability of such an approach.

  1. H. Groefsema, N.R.T.P. van Beest, and M. Aiello (2016) A Formal Model for Compliance Verification of Service Compositions. IEEE Transactions on Service Computing.
  2. Carmona, Josep, et al. "Conformance checking." Switzerland: Springer.[Google Scholar] (2018).
  3. BPMVerification package

Compliance and conformance checking logs in Zeebe

Supervisor: Heerko Groefsema.
Status: Available.
Date: 31/10/2022.
The practice of checking conformance of business process models has revolutionized the industry through the amount of insight it creates into the process flows of businesses. Conformance checking entails matching an event log (which details events of past executions) against a business process model (which details the prescribed process flow). As a result, logging has become extremely important for any business process execution engine, such as Zeebe: the business process execution engine of the open-source Camunda framework. For our research we are interested in detailed event logs of process executions, that include information such as the lifecycle state of tasks, and output to common event log formats. In this project we ask the student to assess current event logs, assess the logging capabilities of Zeebe, and implement a package featuring detailed extended and customizable logging and live event hooks.

  1. Zeebe: Distributed Workflow Engine for Microservices Orchestration
  2. "IEEE Standard for eXtensible Event Stream (XES) for Achieving Interoperability in Event Logs and Event Streams," in IEEE Std 1849-2016 , vol., no., pp.1-50, 11 Nov. 2016
  3. Hompes, B. F. A. "Artifact Lifecycle Extension."

Deep Learning over graph structured data.

Supervisor: Andrés Tello.
Status: in progress: unavailable.
Date: 31/01/2022.
Deep Learning has been used successfully for solving problems in computer vision, natural language processing and many other tasks where data can be accurately represented in the Euclidean domain. However, there are several problems where data can be naturally modeled as graphs, e.g. social networks, recommendation systems, transportation networks, water networks, among others. In this context, Graph Neural Networks (GNNs) have shown promising results on many prediction tasks on graph-structured datasets. The goal of this project is to evaluate the state-of-the-art approaches for graph representation learning and use GNNs for node classification and link prediction in graph-structured data. The domain for the experiments can be discussed later (Transport Networks, Water Networks, Product Recommendations).

Conception and Analysis of High Performance Real-Time Stream Processing in the context of the DEBS grand challenge problem.

Supervisor: Boris Koldehofe, Pratyush Agnihotri (External).
Status: in progress: unavailable.
Date: 31/01/2022.
In this research project the engaged students (1-3 students, groups are possible) focus on a current grand challenge problem (DEBS 2022 Grand Challenge: https://2022.debs.org/call-for-grand-challenge-solutions/) in which researchers compete world-wide to improve the performance of distributed stream processing systems. As part of this research project students aim to analyze existing distributed stream processing solutions and technologies, understand and propose a solution for minimizing performance gaps and design an appropriate performance study. Motivated students are encouraged to participate in cooperation with the supervisors on the grand challenge competition.

Design and Performance Analysis of RDMA-based Distributed Complex Event Processing.

Supervisor: Bochra Boughzala.
Status: unavailable.
Date: 31/01/2022.
To achieve a higher level of elasticity and scalability modern applications of Complex-Event Processing (CEP) are executed in distributed manner. When DCEP (Distributed CEP) employs operator parallelization along with multithreading where each operator instance is executed by a thread attached to a core in a multicore CPU architecture, the degree of parallelization remains limited to the number of cores available for the CEP application. In this project, the student will explore the feasibility of a novel deployment method for DCEP relying on heterogenous and disaggregated hardware. The proposed DCEP deployment solution must take advantage of novel in-network programmable devices combined with traditional compute nodes. The goal in this project is to leverage RDMA for maintaining shared state among truly distributed operator instances.

Total ordering of Events using In-Network Computing in the context of Complex-Event Processing.

Supervisor: Bochra Boughzala.
Status: unavailable.
Date: 31/01/2022.
With Complex-Event Processing (CEP), operator instances are executing continuously queries on the incoming event stream so that when there is a match to the query, a new composite event is created and fed to the CEP application, e.g., a traffic jam situation in a smart city. The reliable detection of a situation of interest in real-time event streams requires the in-order delivery of events. However, the unpredictable and dynamic nature of data center networks where links can go down and routing paths change unexpectedly, events can be forwarded through different paths and arrive in out-of-order manner to the CEP operator. In this project, the goal is to use programmable packet scheduling to restore the total order of events in a DCEP system. The student will devise an in-line network function that will deliver a total order of events at line rate leveraging novel programmable packet processing hardware.

Software Verification for Data Processing Pipeline

Supervisor: Mostafa Hadadian.
Status: in progress (unavailable).
Date: 10/01/2022.
Software verification ensures that specific software components or subsystems meet their design requirements. It is done at the design time. On the other hand, data processing pipelines are composed of several data processing elements that are connected together, i.e. an output of one element is an input for another one, to produce the expected result. This project aims to use software verification techniques to verify the design of a data processing pipeline.

Automated Planning of Data Processing Pipeline

Supervisor: Mostafa Hadadian.
Status: in progress (unavailable).
Date: 10/01/2022.
Planning is the reasoning side of acting. It is an abstract, explicit deliberation process that chooses and organizes actions by anticipating their expected outcomes. This deliberation aims at achieving some pre-stated objectives. Automated planning is an area of artificial intelligence (AI) that studies this deliberation process computationally. This project aims to use these automated planning techniques to create a system that automatically designs data processing pipelines which are consisted of several building blocks working together to produce the expected result.


Have your own project suggestions?

We are available to supervise projects on various aspects of distributed systems, in particular involving

  • Service-Oriented and Cloud Computing
  • Pervasive Computing and Smart Environments
  • Network Centric Real-time Analytics
  • Energy Distribution Infrastructures
  • Adaptive Communication Middleware

If you have an idea of a specific project or would like to work generally in a specific area, please let us know about it and we can then narrow the project down.

Please feel free to contact us to discuss specific topics and options.