Automating Industrial Event Stream Analytics: Methods, Models, and Tools

Industrial event streams are an important cornerstone of Industrial Internet of Things (IIoT) applications. For instance, in the manufacturing domain, such streams are typically produced by distributed industrial assets at high frequency on the shop floor. To add business value and extract the full potential of the data (e.g. through predictive quality assessment or maintenance), industrial event stream analytics is an essential building block. One major challenge is the distribution of required technical and domain knowledge across several roles, which makes the realization of analytics projects time-consuming and error-prone. For instance, accessing industrial data sources requires a high level of technical skills due to a large heterogeneity of protocols and formats. To reduce the technical overhead of current approaches, several problems must be addressed. The goal is to enable so-called "citizen technologists" to evaluate event streams through a self-service approach. This requires new methods and models that cover the entire data analytics cycle. In this thesis, the research question is answered, how citizen technologists can be facilitated to independently perform industrial event stream analytics. The first step is to investigate how the technical complexity of modeling and connecting industrial data sources can be reduced. Subsequently, it is analyzed how the event streams can be automatically adapted (directly at the edge), to meet the requirements of data consumers and the infrastructure. Finally, this thesis examines how machine learning models for industrial event streams can be trained in an automated way to evaluate previously integrated data. The main research contributions of this work are: 1. A semantics-based adapter model to describe industrial data sources and to automatically generate adapter instances on edge nodes. 2. An extension for publish-subscribe systems that dynamically reduces event streams while considering requirements of downstream algorithms. 3. A novel AutoML approach to enable citizen data scientists to train and deploy supervised ML models for industrial event streams. The developed approaches are fully implemented in various high-quality software artifacts. These have been integrated into a large open-source project, which enables rapid adoption of the novel concepts into real-world environments. For the evaluation, two user studies to investigate the usability, as well as performance and accuracy tests of the individual components were performed

Cloud based processing of real time sensor-data streams

The aim of the project is to design an architecture for real time sensor data streaming, management and live visualisation over the web to contribute to existing research in the field of the Web of Things. The project will investigate the infrastructure between streaming hardware and website. Typical hardware would be programmable, equipped with sensors or the ability to connect up sensors and a possibility to communicate over the internet, e.g. an Arduino board, Raspberry Pi or also a smartphone. This project focuses on the universality and manageability of the system to allow many users to deploy the sensing data over a web portal and consume the data on their own websites or allow others to integrate the data into third party websites. The requirements for a universal and manageable streaming service will be developed by investigation of existing systems and analysis of use cases in different application areas. The operational capability of the architecture was investigated by implementing key features and experiments. The project identified the strength and weaknesses of the architecture and investigated the feasibility of the concept. A basic prototype was developed and tested. The feasibility of several parts of the system was proved by implementations and tests. Visualisation possibilities, security and processes were investigated. System requirements for different application areas were defined. System limits were investigated such as the relation between the streamed amount of sensor values and visualisation update rate, the accuracy of the system with high visualisation update rates and the limits of creating streaming instances as a bottleneck. The experiments and tests defined the limits and gave statements about the system performance, security and feasibility

Cloud based processing of real time sensor-data streams

The aim of the project is to design an architecture for real time sensor data streaming, management and live visualisation over the web to contribute to existing research in the field of the Web of Things. The project will investigate the infrastructure between streaming hardware and website. Typical hardware would be programmable, equipped with sensors or the ability to connect up sensors and a possibility to communicate over the internet, e.g. an Arduino board, Raspberry Pi or also a smartphone. This project focuses on the universality and manageability of the system to allow many users to deploy the sensing data over a web portal and consume the data on their own websites or allow others to integrate the data into third party websites. The requirements for a universal and manageable streaming service will be developed by investigation of existing systems and analysis of use cases in different application areas. The operational capability of the architecture was investigated by implementing key features and experiments. The project identified the strength and weaknesses of the architecture and investigated the feasibility of the concept. A basic prototype was developed and tested. The feasibility of several parts of the system was proved by implementations and tests. Visualisation possibilities, security and processes were investigated. System requirements for different application areas were defined. System limits were investigated such as the relation between the streamed amount of sensor values and visualisation update rate, the accuracy of the system with high visualisation update rates and the limits of creating streaming instances as a bottleneck. The experiments and tests defined the limits and gave statements about the system performance, security and feasibility

Proceedings of the 9th international conference on disability, virtual reality and associated technologies (ICDVRAT 2012)

The proceedings of the conferenc

CACIC 2015 : XXI Congreso Argentino de Ciencias de la Computación. Libro de actas

Actas del XXI Congreso Argentino de Ciencias de la Computación (CACIC 2015), realizado en Sede UNNOBA Junín, del 5 al 9 de octubre de 2015.Red de Universidades con Carreras en Informática (RedUNCI

XXIII Edición del Workshop de Investigadores en Ciencias de la Computación : Libro de actas

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Actas del XVIII Workshop de Investigadores en Ciencias de la Computación (WICC 2016), realizado en la Universidad Nacional de Entre Ríos, el 14 y 15 de abril de 2016.Red de Universidades con Carreras en Informática (RedUNCI