@inproceedings{Nazemi2020db,

title = {An Industry 4.0-Ready Visual Analytics Model for Context-Aware Diagnosis in Smart Manufacturing},

author = {Lukas Kaupp and Kawa Nazemi and Bernhard Humm},

doi = {10.1109/IV51561.2020.00064},

isbn = {978-1-7281-9134-8},

year  = {2020},

date = {2020-09-01},

booktitle = {2020 24th International Conference Information Visualisation (IV)},

pages = {350-359},

publisher = {IEEE},

address = {New York, USA},

abstract = {The integrated cyber-physical systems in Smart Manufacturing generate continuously vast amount of data. These complex data are difficult to assess and gather knowledge about the data. Tasks like fault detection and diagnosis are therewith difficult to solve. Visual Analytics mitigates complexity through the combined use of algorithms and visualization methods that allow to perceive information in a more accurate way. Thereby, reasoning relies more and more on the given situation within a smart manufacturing environment, namely the context. Current general Visual Analytics approaches only provide a vague definition of context. We introduce in this paper a model that specifies the context in Visual Analytics for Smart Manufacturing. Additionally, our model bridges the latest advances in research on Smart Manufacturing and Visual Analytics. We combine and summarize methodologies, algorithms and specifications of both vital research fields with our previous findings and fuse them together. As a result, we propose our novel industry 4.0-ready Visual Analytics model for context-aware diagnosis in Smart Manufacturing.},

keywords = {Analytical models, cyber-physical systems, Data Science, Industries, Outlier Detection, Pipelines, Protocols, Reasoning, Smart manufacturing, Task analysis, Visual analytics},

pubstate = {published},

tppubtype = {inproceedings}

}