Dr.-Ing. Dominik Riemer

Bereichsleiter

Werdegang

Dominik Riemer studierte Informationswirtschaft am Karlsruher Institut für Technologie (KIT). Von 2011 bis 2016 arbeitete er als Wissenschaftlicher Mitarbeiter und später als Projektleiter am FZI bei Prof. Dr. Studer im Forschungsbereich IPE mit dem Schwerpunkt Complex Event Processing (CEP). Anschließend war er von Januar 2017 bis März 2019 Abteilungsleiter "Wissensmanagement" am FZI. Seit Oktober 2018 verantwortet er gemeinsam mit Dr.-Ing. Fabian Rigoll den Forschungsbereich "Information Process Engineering".

Dominik Riemer ist Experte für technische Fragestellungen bzgl. ereignisgesteuerter Architekturen, verteilter Systeme und Datenstromverarbeitung. Im Rahmen seiner Tätigkeit arbeitet er als Projektmitarbeiter, Projektleiter und wissenschaftlicher Koordinator in zahlreichen öffentlichen Forschungsprojekten (z.B. ProaSense (EU), ALERT (EU), PostBot-E (BMWi), PartSense (BMBF), WEKOVI (BMVI)) und berät Großunternehmen wie Bosch und BASF sowie KMU bei der Umsetzung innovativer Lösungen für Echtzeitverarbeitung und IoT in Domänen wie Mobilität, Produktion und Logistik.

2016 promovierte Dominik Riemer am Karlsruher Institut für Technologie (KIT) summa cum laude zum Thema "Methods and Tools for Management of Distributed Event Processing Applications". Die Arbeit wurde ausgezeichnet mit dem Wissenschaftspreis der KIT-Fakultät für Wirtschaftswissenschaften.

Seine derzeitigen Forschungsinteressen liegen im Bereich des intelligenten Datenmanagements für Stream Processing und Fog Computing, dabei insb. der Zugänglichmachung komplexer echtzeitfähiger und hochskalierbarer Analytics-Verfahren und -Anwendungen für Fachanwender.

 

Forschungsinteressen:

• Stream Processing 
• Fog Computing
• Self-Service Big Data Analytics / Guided Analytics
• Distributed Systems

Forschungsprojekte:

• Apache StreamPipes: A self-service (Industrial) IoT toolbox to enable non-technical users to connect, analyze and explore IoT data streams (https://streampipes.apache.org) 
• Lieferbot-E (BMWi Elektromobilität): Automatisierte Ver- und Entsorgung städtischer Quartiere durch autonome Elektrofahrzeuge
• OCROSS (BMVI mFUND): Open Data Crowd Sensing Service für die einfache Fusion annotierter und schwarmbasierter Massendaten
• WEKOVI (BMVI mFUND): Werkzeuge für die einfache Erstellung komplexer Vergleichsindizes
• ProveIT (BMWi Zukunftsfähige Logistiknetzwerke): Echtzeitbasierte Störungserkennung und -behebung in Logistikprozessen 
• ProaSense (EU FP7): The Proactive Sensing Enterprise, Datengetriebene proaktive Erkennung von Störungen in Produktionsprozessen
• ALERT (EU FP7): Echtzeitkoordination und Kollaboration von Entwicklern in Softwareprojekten 
• ReFLEX (EU FP7 SME): Ad-hoc Prozessadaptionen im Logistikumfeld
• PartSense (DE KMU-Innovativ): Anwendungen und Technologien für Participatory Sensing 

Publikationen

Zeitungs- oder Zeitschriftenartikel (2)

1. A disruption management system for automotive inbound networks: concepts and challengesInfoDetails

   Meyer, Anne and Sejdovic, Suad and Glock, Katharina and Bender, Matthias and Kleiner, Natalja and Riemer, Dominik, 2018

   Production processes in the automotive industry are highly dependent on reliable inbound logistics processes, because in lean production systems delays or mistakes often result in expensive interruptions of production processes. However, transport processes are always subject to unavoidable disturbances, delays, or mistakes. The goal of the research project ProveIT is to provide an IT system improving the transparency by monitoring transport processes in real-time: deviations from the transport plans are identified predictively, and classified dynamically as disruptions if they have negative impacts on the subsequent processes. If a disruption occurs, the operations managers are provided with mitigation actions automatically generated by escalation-based online optimization algorithms. In this contribution, we introduce the use cases, the architecture and main concepts of the ProveIT disruption management system, and report on challenges faced during field experiments with our application partners, Bosch, ZF, and Geis.

2. Fogsy: Towards Holistic Industrial AI Management in Fog and Edge EnvironmentsDetails

   Wiener, Patrick and Zehnder, Philipp and Heyden, Marco and Philipp, Patrick and Riemer, Dominik

Konferenzbeitrag (17)

1. Managing Geo-Distributed Stream Processing Pipelines for the IIoT with StreamPipes Edge ExtensionsDetails

   Wiener, Patrick and Zehnder, Philipp and Riemer, Dominik, Association for Computing Machinery, 2020

2. StreamPipes Connect: Semantics-Based Edge Adapters for the IIoTDetails

   Zehnder, Philipp and Wiener, Patrick and Straub, Tim and Riemer, Dominik, 2020

3. Towards Context-Aware and Dynamic Management of Stream Processing Pipelines for Fog ComputingInfoDetails

   P. Wiener and P. Zehnder and D. Riemer, 2019

   Newly arising IoT-driven use cases often require low-latency anaiytics to derive time-sensitive actions, where a centralized cloud approach is not applicable. An emerging computing paradigm, referred to as fog computing, shifts the focus away from the central cloud by offloading specific computational parts of analytical stream processing pipelines (SPP) towards the edge of the network, thus leveraging existing resources close to where data is generated. However, in scenarios of mobile edge nodes, the inherent context changes need to be incorporated in the underlying fog cluster management, thus accounting for the dynamics by relocating certain processing elements of these SPP. This paper presents our initial work on a conceptual architecture for context-aware and dynamic management of SPP in the fog. We provide preliminary results, showing the general feasibility of relocating processing elements according to changes in the geolocation.

4. Using Virtual Events for Edge-based Data Stream Reduction in Distributed Publish/Subscribe SystemsInfoDetails

   P. Zehnder and P. Wiener and D. Riemer, 2019

   Distributed publish/subscribe systems are an enabling technology for Industrial Internet of Things applications. While the number of sensors increases, network bandwidth becomes a bottleneck. Existing solutions typically aim to reduce network load either by pre-processing events directly on the edge or by aggregating events into larger batches. However, these approaches are rather static and do not adequately account for the application requirements of subscribers or the actual values of sensor measurements. This paper introduces methods for publish/subscribe systems to dynamically adapt payloads of events at runtime based on i) different data reduction and transformation strategies, ii) a wrapper solution around existing message brokers and iii) a semantics-based event schema registry. Consumers are able to subscribe to various quality levels and receive virtual events, that are reconstructed directly at the subscriber based on knowledge from the semantic model and dynamic decision rules. Our evaluation shows that the concept of virtual events can reduce the network load between publishers, the message broker and subscribers compared to multiple investigated compression techniques.

5. Representing Industrial Data Streams in Digital Twins using Semantic LabelingDetails

   P. Zehnder and D. Riemer, 2018

6. Feeding the Digital Twin: Basics, Models and Lessons Learned from Building an IoT Analytics Toolbox (Invited Talk)Details

   D. Riemer, 2018

7. Towards automatic infrastructure provisioning for highly dynamic streaming applicationsDetails

   P. Zehnder and D. Riemer, 2017

8. Modeling self-service machine-learning agents for distributed stream processingDetails

   P. Zehnder and D. Riemer, 2017

9. Considering Human Factors in the Development of Situation-Aware CEP Applications: New Direction PaperDetails

   Sejdovic, Suad and Euting, Sven and Riemer, Dominik and Sure-Vetter, York, ACM, 2017

10. StreamPipes: Solving the DEBS Challenge with Semantic Stream Processing PipelinesDetails

    Riemer, Dominik and Kaulfersch, Florian and Hutmacher, Robin and Stojanovic, Ljiljana, 2015

11. SEPP: Semantics-Based Management of Fast Data StreamsDetails

    Riemer, Dominik and Stojanovic, Ljiljana and Stojanovic, Nenad, 2014

12. Demo: a system for dynamic real-time personal fitness monitoringDetails

    Stojanovic, Nenad D and Riemer, Domink and Xu, Yongchun, 2013

13. Demo: ALERT-real-time coordination in open source software developmentDetails

    Riemer, Dominik and Stojanovic, Ljiljana and Stojanovic, Nenad, 2013

14. A methodology for designing events and patterns in fast data processingDetails

    Riemer, Dominik and Stojanovic, Nenad and Stojanovic, Ljiljana, 2013

15. An event-driven system for business awareness management in the logistics domainDetails

    Magoutas, Babis and Riemer, Dominik and Apostolou, Dimitris and Ma, Jun and Mentzas, Gregoris and Stojanovic, Nenad, 2013

16. ALERT: semantic event-driven collaborative platform for software developmentDetails

    Stojanovic, Ljiljana and Sen, Sinan and Ma, Jun and Riemer, Dominik, 2012

17. Using complex event processing for modeling semantic requests in real-time social media monitoringDetails

    Riemer, Dominik and Stojanovic, Ljiljana and Stojanovic, Nenad, 2012

Thesis (1)

1. Methods and tools for management of distributed event processing applicationsDetails

   Riemer, Dominik, 2016

Export Suchergebnis .bib

Kontakt

Telefon: +49 721 9654-724
E-Mail: riemer@dont-want-spam.fzi.de

Zurück