@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}
}
@inproceedings {INPROC-2017-05,
author = {Laura Kassner and Pascal Hirmer and Matthias Wieland and Frank Steimle and Jan K{\"o}nigsberger and Bernhard Mitschang},
title = {{The Social Factory: Connecting People, Machines and Data in Manufacturing for Context-Aware Exception Escalation}},
booktitle = {Proceedings of the 50th Hawaii International Conference on System Sciences},
publisher = {Online},
institution = {Universit{\"a}t Stuttgart, Fakult{\"a}t Informatik, Elektrotechnik und Informationstechnik, Germany},
pages = {1--10},
type = {Konferenz-Beitrag},
month = {Januar},
year = {2017},
isbn = {978-0-9981331-0-2},
keywords = {decision support; internet of things; smart manufacturing; social media; text analytics},
language = {Englisch},
cr-category = {E.0 Data General,
H.2 Database Management,
H.3 Information Storage and Retrieval,
H.4 Information Systems Applications},
ee = {http://hdl.handle.net/10125/41355},
contact = {pascal.hirmer@ipvs.uni-stuttgart.de},
department = {Universit{\"a}t Stuttgart, Institut f{\"u}r Parallele und Verteilte Systeme, Anwendersoftware},
abstract = {Manufacturing environments are socio-technical systems $\backslash$ where people have to
interact with machines to achieve $\backslash$ a common goal. The goal of the fourth
industrial revolution is $\backslash$ to improve their flexibility for mass customization
and rapidly $\backslash$ changing production conditions. As a contribution towards $\backslash$ this
goal, we introduce the Social Factory: a social network $\backslash$ with a powerful
analytics backend to improve the connection $\backslash$ between the persons working in
the production environment, $\backslash$ the manufacturing machines, and the data that is
created $\backslash$ in the process. We represent machines, people and chatbots $\backslash$ for
information provisioning as abstract users in the social $\backslash$ network. We enable
natural language based communication between $\backslash$ them and provide a rich
knowledge base and automated $\backslash$ problem solution suggestions. Access to complex
production $\backslash$ environments thus becomes intuitive, cooperation among users $\backslash$
improves and problems are resolved more easily.},
url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=INPROC-2017-05&engl=0}
}
@inproceedings {INPROC-2016-07,
author = {Christoph Gr{\"o}ger and Laura Kassner and Eva Hoos and Jan K{\"o}nigsberger and Cornelia Kiefer and Stefan Silcher and Bernhard Mitschang},
title = {{The Data-Driven Factory. Leveraging Big Industrial Data for Agile, Learning and Human-Centric Manufacturing}},
booktitle = {Proceedings of the 18th International Conference on Enterprise Information Systems},
editor = {Slimane Hammoudi and Leszek Maciaszek and Michele M. Missikoff and Olivier Camp and Jose Cordeiro},
publisher = {SciTePress},
institution = {Universit{\"a}t Stuttgart, Fakult{\"a}t Informatik, Elektrotechnik und Informationstechnik, Germany},
pages = {40--52},
type = {Konferenz-Beitrag},
month = {April},
year = {2016},
isbn = {978-989-758-187-8},
keywords = {IT Architecture, Data Analytics, Big Data, Smart Manufacturing, Industrie 4.0},
language = {Englisch},
cr-category = {H.4.0 Information Systems Applications General,
J.2 Physical Sciences and Engineering},
contact = {Email an Christoph.Groeger@ipvs.uni-stuttgart.de oder laura.kassner@ipvs.uni-stuttgart.de},
department = {Universit{\"a}t Stuttgart, Institut f{\"u}r Parallele und Verteilte Systeme, Anwendersoftware},
abstract = {Global competition in the manufacturing industry is characterized by ever
shorter product life cycles, increas-ing complexity and a turbulent
environment. High product quality, continuously improved processes as well as
changeable organizational structures constitute central success factors for
manufacturing companies. With the rise of the internet of things and Industrie
4.0, the increasing use of cyber-physical systems as well as the digitalization
of manufacturing operations lead to massive amounts of heterogeneous industrial
data across the product life cycle. In order to leverage these big industrial
data for competitive advantages, we present the concept of the data-driven
factory. The data-driven factory enables agile, learning and human-centric
manu-facturing and makes use of a novel IT architecture, the Stuttgart IT
Architecture for Manufacturing (SITAM), overcoming the insufficiencies of the
traditional information pyramid of manufacturing. We introduce the SITAM
architecture and discuss its conceptual components with respect to
service-oriented integration, ad-vanced analytics and mobile information
provisioning in manufacturing. Moreover, for evaluation purposes, we present a
prototypical implementation of the SITAM architecture as well as a real-world
application sce-nario from the automotive industry to demonstrate the benefits
of the data-driven factory.},
url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=INPROC-2016-07&engl=0}
}
@inproceedings {INPROC-2016-06,
author = {Laura Kassner and Bernhard Mitschang},
title = {{Exploring Text Classification for Messy Data: An Industry Use Case for Domain-Specific Analytics}},
booktitle = {Advances in Database Technology - EDBT 2016, 19th International Conference on Extending Database Technology, Bordeaux, France, March 15-16, Proceedings},
publisher = {OpenProceedings.org},
institution = {Universit{\"a}t Stuttgart, Fakult{\"a}t Informatik, Elektrotechnik und Informationstechnik, Germany},
pages = {491--502},
type = {Konferenz-Beitrag},
month = {M{\"a}rz},
year = {2016},
isbn = {978-3-89318-070-7},
keywords = {recommendation system; automotive; text analytics; domain-specific language; automatic classification},
language = {Englisch},
cr-category = {H.3.1 Content Analysis and Indexing,
H.3.3 Information Search and Retrieval,
H.4.2 Information Systems Applications Types of Systems,
J.1 Administration Data Processing},
ee = {http://openproceedings.org/2016/conf/edbt/paper-52.pdf},
contact = {Email an laura.kassner@ipvs.uni-stuttgart.de},
department = {Universit{\"a}t Stuttgart, Institut f{\"u}r Parallele und Verteilte Systeme, Anwendersoftware},
abstract = {Industrial enterprise data present classification problems which are different
from those problems typically discussed in the scientific community -- with
larger amounts of classes and with domain-specific, often unstructured data. We
address one such problem through an analytics environment which makes use of
domain-specific knowledge. Companies are beginning to use analytics on large
amounts of text data which they have access to, but in day-to-day business,
manual effort is still the dominant method for processing unstructured data. In
the face of ever larger amounts of data, faster innovation cycles and higher
product customization, human experts need to be supported in their work through
data analytics. In cooperation with a large automotive manufacturer, we have
developed a use case in the area of quality management for supporting human
labor through text analytics: When processing damaged car parts for quality
improvement and warranty handling, quality experts have to read text reports
and assign error codes to damaged parts. We design and implement a system to
recommend likely error codes based on the automatic recognition of error
mentions in textual quality reports. In our prototypical implementation, we
test several methods for filtering out accurate recommendations for error codes
and develop further directions for applying this method to a competitive
business intelligence use case.},
url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=INPROC-2016-06&engl=0}
}
@inproceedings {INPROC-2015-45,
author = {Laura Kassner and Cornelia Kiefer},
title = {{Taxonomy Transfer: Adapting a Knowledge Representing Resource to new Domains and Tasks}},
booktitle = {Proceedings of the 16th European Conference on Knowledge Management},
publisher = {acpi Online},
institution = {Universit{\"a}t Stuttgart, Fakult{\"a}t Informatik, Elektrotechnik und Informationstechnik, Germany},
pages = {399--407},
type = {Konferenz-Beitrag},
month = {September},
year = {2015},
keywords = {taxonomy; ontology; ontology population; semantic resources; domain-specific language},
language = {Englisch},
cr-category = {I.2.7 Natural Language Processing,
I.2.4 Knowledge Representation Formalisms and Methods,
J.7 Computers in Other Systems},
contact = {Email an laura.kassner@ipvs.uni-stuttgart.de},
department = {Universit{\"a}t Stuttgart, Institut f{\"u}r Parallele und Verteilte Systeme, Anwendersoftware},
abstract = {Today, data from different sources and different phases of the product life
cycle are usually analyzed in isolation and with considerable time delay.
Real-time integrated analytics is especially beneficial in a production
context. We present an architecture fordata- and analytics-driven exception
escalation in manufacturing and show the advantages of integrating unstructured
data.},
url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=INPROC-2015-45&engl=0}
}
@inproceedings {INPROC-2015-15,
author = {Laura Kassner and Bernhard Mitschang},
title = {{MaXCept – Decision Support in Exception Handling through Unstructured Data Integration in the Production Context. An Integral Part of the Smart Factory.}},
booktitle = {Proceedings of the 48th Hawaii International Conference on System Sciences: HICSS 48, 2015},
publisher = {IEEE Computer Society},
institution = {Universit{\"a}t Stuttgart, Fakult{\"a}t Informatik, Elektrotechnik und Informationstechnik, Germany},
pages = {1007--1016},
type = {Konferenz-Beitrag},
month = {Januar},
year = {2015},
keywords = {smart manufacturing; industrial internet; unstructured data; data integration; exception escalation; expert search},
language = {Englisch},
cr-category = {H.4.0 Information Systems Applications General,
J.1 Administration Data Processing,
J.7 Computers in Other Systems},
contact = {laura.kassner@gsame.uni-stuttgart.de},
department = {Universit{\"a}t Stuttgart, Institut f{\"u}r Parallele und Verteilte Systeme, Anwendersoftware},
abstract = {Today, data from different sources and different phases of the product life
cycle are usually analyzed in isolation and with considerable time delay.
Real-time integrated analytics is especially beneficial in a production
context. We present an architecture fordata- and analytics-driven exception
escalation in manufacturing and show the advantages of integrating unstructured
data.},
url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=INPROC-2015-15&engl=0}
}
@inproceedings {INPROC-2014-59,
author = {Laura Kassner and Christoph Gr{\"o}ger and Bernhard Mitschang and Engelbert Westk{\"a}mper},
title = {{Product Life Cycle Analytics - Next Generation Data Analytics on Structured and Unstructured Data}},
booktitle = {Proceedings of the 9th CIRP Conference on Intelligent Computation in Manufacturing Engineering - CIRP ICME '14},
address = {Naples},
publisher = {Elsevier},
institution = {Universit{\"a}t Stuttgart, Fakult{\"a}t Informatik, Elektrotechnik und Informationstechnik, Germany},
pages = {1--6},
type = {Konferenz-Beitrag},
month = {Juli},
year = {2014},
keywords = {analytics, big data, unstructured data, text analytics, product life cycle management, PLM, data warehousing, product life cycle analytics, data integration},
language = {Englisch},
cr-category = {H.3.1 Content Analysis and Indexing,
H.3.4 Information Storage and Retrieval Systems and Software,
J.2 Physical Sciences and Engineering,
J.6 Computer-Aided Engineering},
contact = {Per Mail an laura.kassner@gsame.uni-stuttgart.de},
department = {Universit{\"a}t Stuttgart, Institut f{\"u}r Parallele und Verteilte Systeme, Anwendersoftware},
abstract = {Enormous amounts of unstructured data, e. g., emails, failure reports and
customer complaints, are abundant around the product life cycle and provide a
huge potential for analytics-driven optimization. However, existing analytics
approaches on unstructured data are fraught with three major insufficiencies
limiting comprehensive business improvement: (1) they focus on isolated data
sources from a single life cycle phase {\^a}€“ for example, data from a customer
relationship management system are mined for frequent complaints without
considering manufacturing failure reports related to the same product; (2) they
do not make use of structured data for holistic analytics, e. g., to
automatically correlate unstructured failure reports with structured
performance data of a manufacturing execution system; (3) existing
implementations of data integration and analytics components are typically
cost-intensive, manual and case-based, without a general framework.
To address these issues, we present our Product Life Cycle Analytics (PLCA)
approach, a platform and a reference architecture for the holistic integration
and analysis of unstructured and structured data from multiple data sources
around the product life cycle. For this purpose, we survey structured and
unstructured data sources around the product life cycle and discuss limitations
of existing analytics approaches like traditional Business Intelligence
applications. Moreover, we develop use cases for holistic life-cycle-oriented
analytics and give examples based on case study investigations, e. g., for the
holistic analysis of unstructured failure reports in the automotive industry.
On this basis, we discuss technical requirements and components of our
reference architecture, such as a versatile, plug-and-play Natural Language
Processing pipeline and mechanisms for linking structured and unstructured data
in a holistic data warehouse. Finally, we analyse implementation issues and
investigate underlying technologies from the areas of text analytics and data
mining in order to evaluate our architecture with respect to the identified use
cases.},
url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=INPROC-2014-59&engl=0}
}
@inproceedings {INPROC-2008-150,
author = {Laura Kassner and Vivi Nastase and Michael Strube},
title = {{Acquiring a Taxonomy from the German Wikipedia}},
booktitle = {Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)},
editor = {Nicoletta Calzolari},
publisher = {European Language Resources Association (ELRA)},
institution = {Universit{\"a}t Stuttgart, Fakult{\"a}t Informatik, Elektrotechnik und Informationstechnik, Germany},
pages = {1--4},
type = {Konferenz-Beitrag},
month = {Mai},
year = {2008},
isbn = {2-9517408-4-0},
keywords = {taxonomy; ontology; taxonomy generation; ontology generation; semantic network; Wikipedia; WordNet; GermaNet; multilinguality},
language = {Englisch},
cr-category = {I.2.4 Knowledge Representation Formalisms and Methods,
I.2.7 Natural Language Processing},
ee = {ftp://ftp.informatik.uni-stuttgart.de/pub/library/ncstrl.ustuttgart_fi/INPROC-2008-150/INPROC-2008-150.pdf,
http://www.lrec-conf.org/proceedings/lrec2008/},
contact = {laura.kassner@gsame.uni-stuttgart.de},
department = {Universit{\"a}t Stuttgart, Institut f{\"u}r Parallele und Verteilte Systeme, Anwendersoftware},
abstract = {This paper presents the process of acquiring a large, domain independent,
taxonomy from the German Wikipedia. We build upon a previously implemented
platform that extracts a semantic network and taxonomy from the English version
of theWikipedia. We describe two accomplishments of our work: the semantic
network for the German language in which isa links are identifed and annotated,
and an expansion of the platform for easy adaptation for a new language. We
identify the platform's strengths and shortcomings, which stem from the
scarcity of free processing resources for languages other than English. We show
that the taxonomy induction process is highly reliable - evaluated against the
German version of WordNet, GermaNet, the resource obtained shows an accuracy of
83.34\%.},
url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=INPROC-2008-150&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}
}
@inbook {INBOOK-2008-16,
author = {Anne-Sophie Br{\"u}ggen and Sarah Jessen and Laura Kassner and Thorsten Liebelt and Yvonne Schweizer and Annika Weschler},
title = {{Imagination}},
series = {Kognition und Verhalten: Theory of Mind, Zeit, Imagination, Vergessen, Altruismus},
address = {M{\"u}nster},
publisher = {LIT-Verlag},
series = {Interdisziplin{\"a}re Forschungsarbeiten am FORUM SCIENTIARUM},
volume = {1},
pages = {85--128},
type = {Beitrag in Buch},
month = {Januar},
year = {2008},
isbn = {978-3-8258-1826-5},
keywords = {Imagination; Interdisziplin{\"a}re Forschung; K{\"u}nstliche Intelligenz},
language = {Deutsch},
cr-category = {A.m General Literature, Miscellaneous},
ee = {http://www.forum-scientiarum.uni-tuebingen.de/studium/studienkolleg/archiv/studienkolleg0607.html},
department = {Universit{\"a}t Stuttgart, Institut f{\"u}r Parallele und Verteilte Systeme, Anwendersoftware},
abstract = {'Die F{\"a}higkeit des Menschen zu denken ist Thema der Geisteswissenschaften,
aber auch der Psychologie, Anthropologie und zunehmend der Neurowissenschaften.
Dieser Sammelband, in dem die Abschlussarbeiten des ersten Jahrgangs des
Studienkollegs am Forum Scientiarum der Universit{\"a}t T{\"u}bingen dokumentiert
werden, besch{\"a}ftigt sich mit einigen ausgew{\"a}hlten Themen im Zusammenhang der
biologischen und kulturellen Grundlagen menschlichen Denkens.'
(Autorenreferat). Inhaltsverzeichnis: Judith Benz-Schwarzburg, Linda Braun,
Alexander Ecker, Tobias Kobitzsch, Christian L{\"u}cking: Theory of Mind bei
Mensch und Tier (I-50); Nina Baier, Christoph Paret, Sarah Wiethoff: Zeit und
Zeitbewusstsein (51-84); Anne-Sophie Br{\"u}ggen, Sarah Jessen, Laura Kassner,
Thorsten Liebelt, Yvonne Schweizer, Annika Weschler: Imagination (85-128);
Rainer Engelken, Kathleen Hildebrand, Nikolaus Schmitz, Silke Wagenh{\"a}user:
Vergessen als eine Grundlage menschlichen Denkens (129-176); Christian
G{\"a}ssler, Ralf J. Geretshauser, Bilal Hawa, Steffen Kudella, Sebastian Sehr,
Nora Umbach: Altruismus (177-211).},
url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=INBOOK-2008-16&engl=0}
}