@article{KammererPryssHoppenstedtetal.2020,
  author    = {Kammerer, Klaus and Pryss, R{\"u}diger and Hoppenstedt, Burkhard and Sommer, Kevin and Reichert, Manfred},
  title     = {Process-driven and flow-based processing of industrial sensor data},
  series = {Sensors},
  volume    = {20},
  journal   = {Sensors},
  number    = {18},
  issn      = {1424-8220},
  doi       = {10.3390/s20185245},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-213089},
  year      = {2020},
  abstract  = {For machine manufacturing companies, besides the production of high quality and reliable machines, requirements have emerged to maintain machine-related aspects through digital services. The development of such services in the field of the Industrial Internet of Things (IIoT) is dealing with solutions such as effective condition monitoring and predictive maintenance. However, appropriate data sources are needed on which digital services can be technically based. As many powerful and cheap sensors have been introduced over the last years, their integration into complex machines is promising for developing digital services for various scenarios. It is apparent that for components handling recorded data of these sensors they must usually deal with large amounts of data. In particular, the labeling of raw sensor data must be furthered by a technical solution. To deal with these data handling challenges in a generic way, a sensor processing pipeline (SPP) was developed, which provides effective methods to capture, process, store, and visualize raw sensor data based on a processing chain. Based on the example of a machine manufacturing company, the SPP approach is presented in this work. For the company involved, the approach has revealed promising results.},
  language  = {en}
}
@article{KammererHoppenstedtPryssetal.2019,
  author    = {Kammerer, Klaus and Hoppenstedt, Burkhard and Pryss, R{\"u}diger and St{\"o}kler, Steffen and Allgaier, Johannes and Reichert, Manfred},
  title     = {Anomaly Detections for Manufacturing Systems Based on Sensor Data—Insights into Two Challenging Real-World Production Settings},
  series = {Sensors},
  volume    = {19},
  journal   = {Sensors},
  number    = {24},
  issn      = {1424-8220},
  doi       = {10.3390/s19245370},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-193885},
  pages     = {5370},
  year      = {2019},
  abstract  = {o build, run, and maintain reliable manufacturing machines, the condition of their components has to be continuously monitored. When following a fine-grained monitoring of these machines, challenges emerge pertaining to the (1) feeding procedure of large amounts of sensor data to downstream processing components and the (2) meaningful analysis of the produced data. Regarding the latter aspect, manifold purposes are addressed by practitioners and researchers. Two analyses of real-world datasets that were generated in production settings are discussed in this paper. More specifically, the analyses had the goals (1) to detect sensor data anomalies for further analyses of a pharma packaging scenario and (2) to predict unfavorable temperature values of a 3D printing machine environment. Based on the results of the analyses, it will be shown that a proper management of machines and their components in industrial manufacturing environments can be efficiently supported by the detection of anomalies. The latter shall help to support the technical evangelists of the production companies more properly.},
  language  = {en}
}
@article{KammererGoesterReichertetal.2021,
  author    = {Kammerer, Klaus and G{\"o}ster, Manuel and Reichert, Manfred and Pryss, R{\"u}diger},
  title     = {Ambalytics: a scalable and distributed system architecture concept for bibliometric network analyses},
  series = {Future Internet},
  volume    = {13},
  journal   = {Future Internet},
  number    = {8},
  issn      = {1999-5903},
  doi       = {10.3390/fi13080203},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-244916},
  year      = {2021},
  abstract  = {A deep understanding about a field of research is valuable for academic researchers. In addition to technical knowledge, this includes knowledge about subareas, open research questions, and social communities (networks) of individuals and organizations within a given field. With bibliometric analyses, researchers can acquire quantitatively valuable knowledge about a research area by using bibliographic information on academic publications provided by bibliographic data providers. Bibliometric analyses include the calculation of bibliometric networks to describe affiliations or similarities of bibliometric entities (e.g., authors) and group them into clusters representing subareas or communities. Calculating and visualizing bibliometric networks is a nontrivial and time-consuming data science task that requires highly skilled individuals. In addition to domain knowledge, researchers must often provide statistical knowledge and programming skills or use software tools having limited functionality and usability. In this paper, we present the ambalytics bibliometric platform, which reduces the complexity of bibliometric network analysis and the visualization of results. It accompanies users through the process of bibliometric analysis and eliminates the need for individuals to have programming skills and statistical knowledge, while preserving advanced functionality, such as algorithm parameterization, for experts. As a proof-of-concept, and as an example of bibliometric analyses outcomes, the calculation of research fronts networks based on a hybrid similarity approach is shown. Being designed to scale, ambalytics makes use of distributed systems concepts and technologies. It is based on the microservice architecture concept and uses the Kubernetes framework for orchestration. This paper presents the initial building block of a comprehensive bibliometric analysis platform called ambalytics, which aims at a high usability for users as well as scalability.},
  language  = {en}
}