@article{electronics11233942,

title = {Evaluation of the Flourish Dashboard for Context-Aware Fault Diagnosis in Industry 4.0 Smart Factories},

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

editor = {Kawa Nazemi and Egils Ginters and Michael Bazant},

url = {https://www.mdpi.com/2079-9292/11/23/3942},

doi = {10.3390/electronics11233942},

issn = {2079-9292},

year  = {2022},

date = {2022-11-01},

urldate = {2022-01-01},

journal = {Electronics},

volume = {11},

number = {23},

abstract = {Cyber-physical systems become more complex, therewith production lines become more complex in the smart factory. Every employed system produces high amounts of data with unknown dependencies and relationships, making incident reasoning difficult. Context-aware fault diagnosis can unveil such relationships on different levels. A fault diagnosis application becomes context-aware when the current production situation is used in the reasoning process. We have already published TAOISM, a visual analytics model defining the context-aware fault diagnosis process for the Industry 4.0 domain. In this article, we propose the Flourish dashboard for context-aware fault diagnosis. The eponymous visualization Flourish is a first implementation of a context-displaying visualization for context-aware fault diagnosis in an Industry 4.0 setting. We conducted a questionnaire and interview-based bilingual evaluation with two user groups based on contextual faults recorded in a production-equal smart factory. Both groups provided qualitative feedback after using the Flourish dashboard. We positively evaluate the Flourish dashboard as an essential part of the context-aware fault diagnosis and discuss our findings, open gaps, and future research directions.},

keywords = {Artificial Intelligence, Case study, Data Analytics, Data Science, Data visualization, Decision Making, Decision Support Systems, Evaluation, smart factory, Smart manufacturing, Visual analytics},

pubstate = {published},

tppubtype = {article}

}

@article{Burkhardt2015b,

title = {Best-practice Piloting of Integrated Social Media Analysis Solution for E-Participation in Cities},

author = {Dirk Burkhardt and Kawa Nazemi and Silvana Tomic and Egils Ginters},

editor = {Jimson Mathew and Ashutosh K. singh},

url = {https://www.sciencedirect.com/science/article/pii/S1877050915038648, Elsevier Science Direct

https://www.sciencedirect.com/science/article/pii/S1877050915038648/pdf?md5=169ec82a0af0b5b8e740685f17683d0a&pid=1-s2.0-S1877050915038648-main.pdf, full text},

doi = {https://doi.org/10.1016/j.procs.2015.12.354},

issn = {1877-0509},

year  = {2015},

date = {2015-01-01},

journal = {Procedia Computer Science},

volume = {77},

pages = {11 - 21},

abstract = {Goal definitions and developments are challenging in large-scale projects, because of the different expertise and skills of the stakeholders. Development often fails its intended goal because of misunderstandings and unclear definitions and descriptions during the planning phase. The paper describes a novel approach to collecting requirements and defining development plans by provisioning a guideline which informs what has to be done, when and in what form. The User Case Requirement Analysis model was applied in the large-scale European project FUPOL during the development of a Social Media Analysis System. Based on this a successful task-based evaluation could be performed that shows the benefit of the model and the software.},

note = {ICTE in regional Development 2015 Valmiera, Latvia},

keywords = {Decision Making, E-Governmant, Evaluation, Information Communication Technologies, Piloting, Policy modeling},

pubstate = {published},

tppubtype = {article}

}