@inproceedings {INPROC-2022-03,
author = {Marco Spie{\ss} and Peter Reimann and Christian Weber and Bernhard Mitschang},
title = {{Analysis of Incremental Learning andWindowing to handle Combined Dataset Shifts on Binary Classification for Product Failure Prediction}},
booktitle = {Proceedings of the 24th International Conference on Enterprise Information Systems (ICEIS 2022)},
publisher = {SciTePress},
institution = {Universit{\"a}t Stuttgart, Fakult{\"a}t Informatik, Elektrotechnik und Informationstechnik, Germany},
type = {Konferenz-Beitrag},
month = {April},
year = {2022},
keywords = {Binary Classification; Dataset Shift; Incremental Learning; Product Failure Prediction; Windowing.},
language = {Englisch},
cr-category = {H.2.8 Database Applications},
department = {Universit{\"a}t Stuttgart, Institut f{\"u}r Parallele und Verteilte Systeme, Anwendersoftware},
abstract = {Dataset Shifts (DSS) are known to cause poor predictive performance in
supervised machine learning tasks. We present a challenging binary
classification task for a real-world use case of product failure prediction.
The target is to predict whether a product, e. g., a truck may fail during the
warranty period. However, building a satisfactory classifier is difficult,
because the characteristics of underlying training data entail two kinds of
DSS. First, the distribution of product configurations may change over time,
leading to a covariate shift. Second, products gradually fail at different
points in time, so that the labels in training data may change, which may a
concept shift. Further, both DSS show a trade-off relationship, i. e.,
addressing one of them may imply negative impacts on the other one. We discuss
the results of an experimental study to investigate how different approaches to
addressing DSS perform when they are faced with both a covariate and a concept
shift. Thereby, we prove that existing approaches, e. g., incremental learning
and windowing, especially suffer from the trade-off between both DSS.
Nevertheless, we come up with a solution for a data-driven classifier that
yields better results than a baseline solution that does not address DSS.},
url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=INPROC-2022-03&engl=0}
}
@inproceedings {INPROC-2021-10,
author = {Alejandro Villanueva Zacarias and Christian Weber and Peter Reimann and Bernhard Mitschang},
title = {{AssistML: A Concept to Recommend ML Solutions for Predictive Use Cases}},
booktitle = {Proceedings of the 8th IEEE International Conference on Data Science and Advanced Analytics (DSAA 2021)},
address = {Porto, Portugal},
publisher = {IEEE},
institution = {Universit{\"a}t Stuttgart, Fakult{\"a}t Informatik, Elektrotechnik und Informationstechnik, Germany},
type = {Konferenz-Beitrag},
month = {Oktober},
year = {2021},
keywords = {Recommender Systems; Machine Learning; Meta Learning},
language = {Englisch},
cr-category = {H.2.8 Database Applications},
department = {Universit{\"a}t Stuttgart, Institut f{\"u}r Parallele und Verteilte Systeme, Anwendersoftware},
abstract = {The adoption of machine learning (ML) in organizations is characterized by the
use of multiple ML software components. Citizen data scientists face practical
requirements when building ML systems, which go beyond the known challenges of
ML, e. g., data engineering or parameter optimization. They are expected to
quickly identify ML system options that strike a suitable trade-off across
multiple performance criteria. These options also need to be understandable for
non-technical users. Addressing these practical requirements represents a
problem for citizen data scientists with limited ML experience. This calls for
a method to help them identify suitable ML software combinations. Related work,
e. g., AutoML systems, are not responsive enough or cannot balance different
performance criteria. In this paper, we introduce AssistML, a novel concept to
recommend ML solutions, i. e., software systems with ML models, for predictive
use cases. AssistML uses metadata of existing ML solutions to quickly identify
and explain options for a new use case. We implement the approach and evaluate
it with two exemplary use cases. Results show that AssistML proposes ML
solutions that are in line with users{\^a}€™ performance preferences in seconds.},
url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=INPROC-2021-10&engl=0}
}
@inproceedings {INPROC-2020-56,
author = {Christian Weber and Peter Reimann},
title = {{MMP - A Platform to Manage Machine Learning Models in Industry 4.0 Environments}},
booktitle = {Proceedings of the IEEE 24th International Enterprise Distributed Object Computing Workshop (EDOCW)},
address = {Eindhoven, The Netherlands},
publisher = {IEEE},
institution = {Universit{\"a}t Stuttgart, Fakult{\"a}t Informatik, Elektrotechnik und Informationstechnik, Germany},
type = {Demonstration},
month = {Juli},
year = {2020},
keywords = {Model Management; Machine Learning; Collaborative Data Science},
language = {Englisch},
cr-category = {H.3.4 Information Storage and Retrieval Systems and Software},
department = {Universit{\"a}t Stuttgart, Institut f{\"u}r Parallele und Verteilte Systeme, Anwendersoftware},
abstract = {In manufacturing environments, machine learning models are being built for
several use cases, such as predictive maintenance and product quality control.
In this context, the various manufacturing processes, machines, and product
variants make it necessary to create and use lots of different machine learning
models. This calls for a software system that is able to manage all these
diverse machine learning models and associated metadata. However, current model
management systems do not associate models with business and domain context to
provide non-expert users with tailored functions for model search and
discovery. Moreover, none of the existing systems provides a comprehensive
overview of all models within an organization. In our demonstration, we present
the MMP, our model management platform that addresses these issues. The MMP
provides a model metadata extractor, a model registry, and a context manager to
store model metadata in a central metadata store. On top of this, the MMP
provides frontend components that offer the above-mentioned functionalities. In
our demonstration, we show two scenarios for model management in Industry 4.0
environments that illustrate the novel functionalities of the MMP. We
demonstrate to the audience how the platform and its metadata, linking models
to their business and domain context, help non-expert users to search and
discover models. Furthermore, we show how to use MMP's powerful visualizations
for model reporting, such as a dashboard and a model landscape view.},
url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=INPROC-2020-56&engl=0}
}
@inproceedings {INPROC-2020-19,
author = {Christian Weber and Pascal Hirmer and Peter Reimann},
title = {{A Model Management Platform for Industry 4.0 - Enabling Management of Machine Learning Models in Manufacturing Environments}},
booktitle = {Proceedings of the 23rd International Conference on Business Information Systems (BIS)},
editor = {Witold Abramowicz and Rainer Alt and Gary Klein and Adrian Paschke and Kurt Sandkuhl},
publisher = {Springer International Publishing},
institution = {Universit{\"a}t Stuttgart, Fakult{\"a}t Informatik, Elektrotechnik und Informationstechnik, Germany},
series = {Lecture Notes in Business Information Processing},
type = {Konferenz-Beitrag},
month = {November},
year = {2020},
issn = {1865-1348},
keywords = {Model Management; Machine Learning; Metadata Tracking},
language = {Englisch},
cr-category = {H.3.4 Information Storage and Retrieval Systems and Software},
department = {Universit{\"a}t Stuttgart, Institut f{\"u}r Parallele und Verteilte Systeme, Anwendersoftware},
abstract = {Industry 4.0 use cases such as predictive maintenance and product quality
control make it necessary to create, use and maintain a multitude of di erent
machine learning models. In this setting, model management systems help to
organize models. However, concepts for model management systems currently focus
on data scientists, but do not support non-expert users such as domain experts
and business analysts. Thus, it is dicult for them to reuse existing models
for their use cases. In this paper, we address these challenges and present an
architecture, a metadata schema and a corresponding model management platform.},
url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=INPROC-2020-19&engl=0}
}
@inproceedings {INPROC-2020-03,
author = {Christoph Stach and Corinna Giebler and Manuela Wagner and Christian Weber and Bernhard Mitschang},
title = {{AMNESIA: A Technical Solution towards GDPR-compliant Machine Learning}},
booktitle = {Proceedings of the 6th International Conference on Information Systems Security and Privacy (ICISSP 2020)},
editor = {Steven Furnell and Paolo Mori and Edgar Weippl and Olivier Camp},
address = {Valletta, Malta},
publisher = {SciTePress},
institution = {Universit{\"a}t Stuttgart, Fakult{\"a}t Informatik, Elektrotechnik und Informationstechnik, Germany},
pages = {1--12},
type = {Konferenz-Beitrag},
month = {Februar},
year = {2020},
keywords = {Machine Learning; Data Protection; Privacy Zones; Access Control; Model Management; Provenance; GDPR},
language = {Englisch},
cr-category = {K.4.1 Computers and Society Public Policy Issues,
I.5.1 Pattern Recognition Models},
contact = {Senden Sie eine E-Mail an Christoph.Stach@ipvs.uni-stuttgart.de},
department = {Universit{\"a}t Stuttgart, Institut f{\"u}r Parallele und Verteilte Systeme, Anwendersoftware},
abstract = {Machine Learning (ML) applications are becoming increasingly valuable due to
the rise of IoT technologies. That is, sensors continuously gather data from
different domains and make them available to ML for learning its models. This
provides profound insights into the data and enables predictions about future
trends. While ML has many advantages, it also represents an immense privacy
risk. Data protection regulations such as the GDPR address such privacy
concerns, but practical solutions for the technical enforcement of these laws
are also required. Therefore, we introduce AMNESIA, a privacy-aware machine
learning model provisioning platform. AMNESIA is a holistic approach covering
all stages from data acquisition to model provisioning. This enables to control
which application may use which data for ML as well as to make models ``forget''
certain knowledge.},
url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=INPROC-2020-03&engl=0}
}
@inproceedings {INPROC-2019-10,
author = {Christian Weber and Pascal Hirmer and Peter Reimann and Holger Schwarz},
title = {{A New Process Model for the Comprehensive Management of Machine Learning Models}},
booktitle = {Proceedings of the 21st International Conference on Enterprise Information Systems (ICEIS); Heraklion, Crete, Greece, May 3-5, 2019},
editor = {Joaquim Filipe and Michal Smialek and Alexander Brodsky and Slimane Hammoudi},
publisher = {SciTePress},
institution = {Universit{\"a}t Stuttgart, Fakult{\"a}t Informatik, Elektrotechnik und Informationstechnik, Germany},
pages = {415--422},
type = {Konferenz-Beitrag},
month = {Mai},
year = {2019},
isbn = {978-989-758-372-8},
doi = {10.5220/0007725304150422},
keywords = {Model Management; Machine Learning; Analytics Process},
language = {Englisch},
cr-category = {I.2 Artificial Intelligence},
department = {Universit{\"a}t Stuttgart, Institut f{\"u}r Parallele und Verteilte Systeme, Anwendersoftware},
abstract = {The management of machine learning models is an extremely challenging task.
Hundreds of prototypical models are being built and just a few are mature
enough to be deployed into operational enterprise information systems. The
lifecycle of a model includes an experimental phase in which a model is
planned, built and tested. After that, the model enters the operational phase
that includes deploying, using, and retiring it. The experimental phase is well
known through established process models like CRISP-DM or KDD. However, these
models do not detail on the interaction between the experimental and the
operational phase of machine learning models. In this paper, we provide a new
process model to show the interaction points of the experimental and
operational phase of a machine learning model. For each step of our process, we
discuss according functions which are relevant to managing machine learning
models.},
url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=INPROC-2019-10&engl=0}
}
@inproceedings {INPROC-2018-23,
author = {Dominik Brenner and Christian Weber and Juergen Lenz and Engelbert Westk{\"a}mper},
title = {{Total Tool Cost of Ownership Indicator for Holistical Evaluations of Improvement Measures within the Cutting Tool Life Cycle}},
booktitle = {51st CIRP Conference on Manufacturing Systems (CIRP CMS), Stockholm, Sweden, May 16-18, 2018},
editor = {Lihui Wang and Torsten Kjellberg and Xi Vincent Wang and Wei Ji},
publisher = {Elsevier},
institution = {Universit{\"a}t Stuttgart, Fakult{\"a}t Informatik, Elektrotechnik und Informationstechnik, Germany},
series = {Procedia CIRP},
volume = {72},
pages = {1404--1409},
type = {Konferenz-Beitrag},
month = {Mai},
year = {2018},
doi = {https://doi.org/10.1016/j.procir.2018.03.164},
issn = {2212-8271},
keywords = {Cutting Tool Life Cycle; Total Cost of Ownership; Manufacturing; Data Integration},
language = {Englisch},
cr-category = {H.3.0 Information Storage and Retrieval General,
H.4.0 Information Systems Applications General,
J.1 Administration Data Processing},
ee = {http://www.sciencedirect.com/science/article/pii/S2212827118303226},
department = {Universit{\"a}t Stuttgart, Institut f{\"u}r Parallele und Verteilte Systeme, Anwendersoftware},
abstract = {},
url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=INPROC-2018-23&engl=0}
}
@inproceedings {INPROC-2017-38,
author = {Christian Weber and Jan K{\"o}nigsberger and Laura Kassner and Bernhard Mitschang},
title = {{M2DDM – A Maturity Model for Data-Driven Manufacturing}},
booktitle = {Manufacturing Systems 4.0 – Proceedings of the 50th CIRP Conference on Manufacturing Systems (CIRP CMS); Taichung, Taiwan, May 3-5, 2017},
editor = {Mitchell M. Tseng and Hung-Yin Tsai and Yue Wang},
publisher = {Elsevier},
institution = {Universit{\"a}t Stuttgart, Fakult{\"a}t Informatik, Elektrotechnik und Informationstechnik, Germany},
series = {Procedia CIRP},
volume = {63},
pages = {173--178},
type = {Konferenz-Beitrag},
month = {Juli},
year = {2017},
doi = {https://doi.org/10.1016/j.procir.2017.03.309},
issn = {2212-8271},
keywords = {Maturity Model; Industrie 4.0; Industrial Internet; Reference Architectures; Digital Twin; Edge Analytics},
language = {Englisch},
cr-category = {H.1.0 Information Systems Models and Principles General,
H.4.0 Information Systems Applications General},
ee = {http://www.sciencedirect.com/science/article/pii/S2212827117304973},
department = {Universit{\"a}t Stuttgart, Institut f{\"u}r Parallele und Verteilte Systeme, Anwendersoftware},
abstract = {},
url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=INPROC-2017-38&engl=0}
}
@article {ART-2023-04,
author = {Alejandro Gabriel Villanueva Zacarias and Peter Reimann and Christian Weber and Bernhard Mitschang},
title = {{AssistML: An Approach to Manage, Recommend and Reuse ML Solutions}},
journal = {International Journal of Data Science and Analytics (JDSA)},
publisher = {Springer Nature},
type = {Artikel in Zeitschrift},
month = {Juli},
year = {2023},
keywords = {Meta-learning; Machine learning; AutoML; Metadata; Recommender systems},
language = {Englisch},
cr-category = {H.2.8 Database Applications},
department = {Universit{\"a}t Stuttgart, Institut f{\"u}r Parallele und Verteilte Systeme, Anwendersoftware},
abstract = {The adoption of machine learning (ML) in organizations is characterized by the
use of multiple ML software components. When building ML systems out of these
software components, citizen data scientists face practical requirements which
go beyond the known challenges of ML, e.g., data engineering or parameter
optimization. They are expected to quickly identify ML system options that
strike a suitable trade-off across multiple performance criteria. These options
also need to be understandable for non-technical users. Addressing these
practical requirements represents a problem for citizen data scientists with
limited ML experience. This calls for a concept to help them identify suitable
ML software combinations. Related work, e.g., AutoML systems, are not
responsive enough or cannot balance different performance criteria. This paper
explains how AssistML, a novel concept to recommend ML solutions, i.e.,
software systems with ML models, can be used as an alternative for predictive
use cases. Our concept collects and preprocesses metadata of existing ML
solutions to quickly identify the ML solutions that can be reused in a new use
case. We implement AssistML and evaluate it with two exemplary use cases.
Results show that AssistML can recommend ML solutions in line with users{\^a}€™
performance preferences in seconds. Compared to AutoML, AssistML offers citizen
data scientists simpler, intuitively explained ML solutions in considerably
less time. Moreover, these solutions perform similarly or even better than
AutoML models.},
url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=ART-2023-04&engl=0}
}
@article {ART-2018-06,
author = {Christian Weber and Matthias Wieland and Peter Reimann},
title = {{Konzepte zur Datenverarbeitung in Referenzarchitekturen f{\"u}r Industrie 4.0: Konsequenzen bei der Umsetzung einer IT-Architektur}},
journal = {Datenbank-Spektrum},
publisher = {Springer Berlin Heidelberg},
volume = {18},
number = {1},
pages = {39--50},
type = {Artikel in Zeitschrift},
month = {M{\"a}rz},
year = {2018},
issn = {1610-1995},
doi = {10.1007/s13222-018-0275-z},
keywords = {Industrie 4.0; Referenzarchitektur; Datenverarbeitung; RAMI4.0; IIRA},
language = {Deutsch},
cr-category = {H.4.0 Information Systems Applications General,
J.2 Physical Sciences and Engineering},
ee = {https://link.springer.com/article/10.1007/s13222-018-0275-z},
contact = {Senden Sie eine E-Mail an christian.weber@gsame.uni-stuttgart.de},
department = {Universit{\"a}t Stuttgart, Institut f{\"u}r Parallele und Verteilte Systeme, Anwendersoftware},
abstract = {F{\"u}r produzierende Unternehmen stellt die effiziente Verarbeitung gro{\ss}er
Datenmengen eine Herausforderung dar. Die Auswahl der richtigen
Architekturkonzepte f{\"u}r IT-L{\"o}sungen zur Datenverarbeitung spielt dabei eine
wichtige Rolle. Um die IT an den Herausforderungen von Industrie 4.0
auszurichten, stehen Unternehmen verschiedene Referenzarchitekturen
internationaler Gremien zur Verf{\"u}gung. Die Hauptbeitr{\"a}ge dieses Artikels haben
das Ziel, (i) einen {\"U}berblick {\"u}ber die wichtigsten Referenzarchitekturen f{\"u}r
Industrie 4.0 (I4.0) zu geben und (ii) diese unter dem Aspekt der
Datenverarbeitung zu untersuchen. Dazu werden die Referenzarchitekturen anhand
von Datenverarbeitungsanforderungen f{\"u}r I4.0 betrachtet. Die Untersuchung
zeigt, dass die I4.0-Referenzarchitekturen jeweils einen Teilbereich der
Anforderungen abdecken und sich die Konzepte gegenseitig erg{\"a}nzen. (iii) Darauf
aufbauend werden aus den Datenverarbeitungsanforderungen technische
Konsequenzen abgeleitet und Architekturkonzepte f{\"u}r die Realisierung einer
IT-Architektur f{\"u}r die Datenverarbeitung vorgestellt. Dadurch wird es
IT-Architekten erm{\"o}glicht, einen Vergleich der Referenzarchitekturen
hinsichtlich projektbezogener Anforderungen an die Datenverarbeitung
vorzunehmen sowie geeignete Architekturentscheidungen zu treffen.},
url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=ART-2018-06&engl=0}
}
@article {ART-2017-07,
author = {Christian Weber and Jan K{\"o}nigsberger},
title = {{Industrie 4.0: Aktuelle Entwicklungen f{\"u}r Analytics - Teil 2: Vergleich und Bewertung von Industrie 4.0-Referenzarchitekturen}},
journal = {wt Werkstattstechnik online},
publisher = {Springer-VDI-Verlag},
volume = {107},
number = {6},
pages = {405--409},
type = {Artikel in Zeitschrift},
month = {Juni},
year = {2017},
keywords = {IT Architecture; Analytics; Edge Analytics; Big Data; Smart Manufacturing; Industrie 4.0; Industrial Internet},
language = {Deutsch},
cr-category = {H.4.0 Information Systems Applications General,
J.2 Physical Sciences and Engineering,
J.6 Computer-Aided Engineering},
ee = {http://www.werkstattstechnik.de/wt/currentarticle.php?data[article_id]=87256},
department = {Universit{\"a}t Stuttgart, Institut f{\"u}r Parallele und Verteilte Systeme, Anwendersoftware},
abstract = {Die Verarbeitung gro{\ss}er Datenmengen sowie die Erkenntnis, dass Datenanalysen
eine hohe Relevanz haben, sind in den produzierenden Unternehmen angekommen.
Bekannte Anwendungsbeispiele sind Digital Mock-Up in der Produktentwicklung
oder Prozessoptimierung durch Predictive Maintenance. Die in letzter Zeit
entwickelten Referenzarchitekturen in diesen breitgef{\"a}cherten Themenfeldern
betrachten dementsprechend verschiedene Aspekte in unterschiedlichen
Auspr{\"a}gungen. Dieser aus zwei Teilen bestehende Beitrag rekapituliert und
bewertet diese Entwicklungen, um Unternehmen bei der Umsetzung ihrer eigenen
individuellen Architektur Hilfestellung zu geben. Im ersten Teil des Beitrags
(Ausgabe 3-2017: wt Werkstattstechnik online) wurden aktuelle
Referenzarchitekturen mit ihren Architekturbausteinen im Bereich Industrie 4.0
vorgestellt. In diesem zweiten Teil werden nun die Referenzarchitekturen unter
dem Gesichtspunkt der Themenfelder Analytics sowie Datenmanagement untersucht
und bewertet.},
url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=ART-2017-07&engl=0}
}
@article {ART-2017-02,
author = {Christian Weber and Jan K{\"o}nigsberger},
title = {{Industrie 4.0: Aktuelle Entwicklungen f{\"u}r Analytics - Teil 1: Analytics und Datenmanagement in Industrie 4.0-Referenzarchitekturen}},
journal = {wt Werkstattstechnik online},
address = {D{\"u}sseldorf},
publisher = {Springer-VDI-Verlag},
volume = {107},
number = {3},
pages = {113--117},
type = {Artikel in Zeitschrift},
month = {M{\"a}rz},
year = {2017},
keywords = {IT Architecture; Analytics; Edge Analytics; Big Data; Smart Manufacturing; Industrie 4.0; Industrial Internet},
language = {Deutsch},
cr-category = {H.4.0 Information Systems Applications General,
J.2 Physical Sciences and Engineering,
J.6 Computer-Aided Engineering},
ee = {http://www.werkstattstechnik.de/wt/currentarticle.php?data[article_id]=87256},
department = {Universit{\"a}t Stuttgart, Institut f{\"u}r Parallele und Verteilte Systeme, Anwendersoftware},
abstract = {Die Verarbeitung gro{\ss}er Datenmengen sowie die hohe Relevanz von Datenanalysen
sind in den produzierenden Unternehmen mittlerweile angekommen. Bekannte
Anwendungsbeispiele sind Digital Mock-Up in der Produktentwicklung oder
Prozessoptimierung durch Predictive Maintenance. Die in letzter Zeit
entwickelten Referenzarchitekturen in diesen breitgef{\"a}cherten Themenfeldern
betrachten dementsprechend verschiedene Aspekte in unterschiedlichen
Auspr{\"a}gungen. Dieser aus zwei Teilen bestehende Fachbeitrag rekapituliert und
bewertet diese Entwicklungen, um Unternehmen bei der Umsetzung ihrer eigenen
individuellen Architektur Hilfestellung zu geben. Im Teil 1 werden aktuelle
Referenzarchitekturen mit ihren Architekturbausteinen im Bereich Industrie 4.0
vorgestellt. Im zweiten Teil (Ausgabe 6-2017 der wt Werkstattstechnik online)
werden die Referenzarchitekturen unter dem Gesichtspunkt der Themenfelder
Analytics sowie Datenmanagement untersucht und bewertet.},
url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=ART-2017-02&engl=0}
}
@inbook {INBOOK-2017-04,
author = {Laura Kassner and Christoph Gr{\"o}ger and Jan K{\"o}nigsberger and Eva Hoos and Cornelia Kiefer and Christian Weber and Stefan Silcher and Bernhard Mitschang},
title = {{The Stuttgart IT Architecture for Manufacturing}},
series = {Enterprise Information Systems: 18th International Conference, ICEIS 2016, Rome, Italy, April 25--28, 2016, Revised Selected Papers},
publisher = {Springer International Publishing},
series = {Lecture Notes in Business Information Processing},
volume = {291},
pages = {53--80},
type = {Beitrag in Buch},
month = {Juni},
year = {2017},
isbn = {978-3-319-62386-3},
doi = {10.1007/978-3-319-62386-3_3},
language = {Englisch},
cr-category = {H.4.0 Information Systems Applications General,
D.2.12 Software Engineering Interoperability,
J.2 Physical Sciences and Engineering},
ee = {https://link.springer.com/chapter/10.1007/978-3-319-62386-3_3},
department = {Universit{\"a}t Stuttgart, Institut f{\"u}r Parallele und Verteilte Systeme, Anwendersoftware},
abstract = {The global conditions for manufacturing are rapidly changing towards shorter
product life cycles, more complexity and more turbulence. The manufacturing
industry must meet the demands of this shifting environment and the increased
global competition by ensuring high product quality, continuous improvement of
processes and increasingly flexible organization. Technological developments
towards smart manufacturing create big industrial data which needs to be
leveraged for competitive advantages. We present a novel IT architecture for
data-driven manufacturing, the Stuttgart IT Architecture for Manufacturing
(SITAM). It addresses the weaknesses of traditional manufacturing IT by
providing IT systems integration, holistic data analytics and mobile
information provisioning. The SITAM surpasses competing reference architectures
for smart manufacturing because it has a strong focus on analytics and mobile
integration of human workers into the smart production environment and because
it includes concrete recommendations for technologies to implement it, thus
filling a granularity gap between conceptual and case-based architectures. To
illustrate the benefits of the SITAM{\^a}€™s prototypical implementation, we
present an application scenario for value-added services in the automotive
industry.},
url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=INBOOK-2017-04&engl=0}
}