@inproceedings {INPROC-2022-08,
author = {Rebecca Eichler and Christoph Gr{\"o}ger and Eva Hoos and Christoph Stach and Holger Schwarz and Bernhard Mitschang},
title = {{Establishing the Enterprise Data Marketplace: Characteristics, Architecture, and Challenges}},
booktitle = {Proceedings of the Workshop on Data Science for Data Marketplaces in Conjunction with the 48th International Conference on Very Large Data Bases},
editor = {Xiaohui Yu and Jian Pei},
publisher = {-},
institution = {Universit{\"a}t Stuttgart, Fakult{\"a}t Informatik, Elektrotechnik und Informationstechnik, Germany},
pages = {1--12},
type = {Workshop-Beitrag},
month = {September},
year = {2022},
language = {Englisch},
cr-category = {E.m Data Miscellaneous,
H.3.7 Digital Libraries,
H.4.m Information Systems Applications Miscellaneous},
department = {Universit{\"a}t Stuttgart, Institut f{\"u}r Parallele und Verteilte Systeme, Anwendersoftware},
abstract = {Companies today have increasing amounts of data at their disposal, most of
which is not used, leaving the data value unexploited. In order to leverage the
data value, the data must be democratized, i.e., made available to the company
employees. In this context, the use of enterprise data marketplaces, platforms
for trading data within a company, are proposed. However, specifics of
enterprise data marketplaces and how these can be implemented have not been
investigated in literature so far. To shed light on these topics, we illustrate
the characteristics of an enterprise data marketplace and highlight according
marketplace requirements. We provide an enterprise data marketplace
architecture, discuss how it integrates into a company's system landscape and
present an enterprise data marketplace prototype. Finally, we examine
organizational and technical challenges which arise when operating a
marketplace in the enterprise context. In this paper, we thereby present the
enterprise data marketplace as a distinct marketplace type and provide the
basis for establishing it within a company.},
url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=INPROC-2022-08&engl=0}
}
@inproceedings {INPROC-2022-05,
author = {Rebecca Eichler and Christoph Gr{\"o}ger and Eva Hoos and Holger Schwarz and Bernhard Mitschang},
title = {{Data Shopping — How an Enterprise Data Marketplace Supports Data Democratization in Companies}},
booktitle = {Proceedings of the 34th International Conference on Intelligent Information Systems},
editor = {Jochen De Weerdt and Artem Polyvyanyy},
address = {Stuttgart},
publisher = {Springer International Publishing},
institution = {Universit{\"a}t Stuttgart, Fakult{\"a}t Informatik, Elektrotechnik und Informationstechnik, Germany},
series = {Rebecca Eichler},
pages = {19--26},
type = {Konferenz-Beitrag},
month = {Mai},
year = {2022},
isbn = {https://doi.org/10.1007/978-3-031-07481-3_3},
keywords = {Data Marketplace; Data Sharing; Data Democratization},
language = {Englisch},
cr-category = {H.0 Information Systems General},
department = {Universit{\"a}t Stuttgart, Institut f{\"u}r Parallele und Verteilte Systeme, Anwendersoftware},
abstract = {To exploit the company's data value, employees must be able to find, understand
and access it. The process of making corporate data available to the majority
of the company's employees is referred to as data democratization. In this
work, we present the current state and challenges of data democratization in
companies, derived from a comprehensive literature study and expert interviews
we conducted with a manufacturer. In this context a data consumer's journey is
presented that reflects the required steps, tool types and roles for finding,
understanding and accessing data in addition to revealing three data
democratization challenges. To address these challenges we propose the use of
an enterprise data marketplace, a novel type of information system for sharing
data within the company. We developed a prototype based on which a suitability
assessment of a data marketplace yields an improved consumer journey and
demonstrates that the marketplace addresses the data democratization challenges
and consequently, shows that the marketplace is suited for realizing data
democratization.},
url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=INPROC-2022-05&engl=0}
}
@inproceedings {INPROC-2021-06,
author = {Rebecca Eichler and Corinna Giebler and Christoph Gr{\"o}ger and Eva Hoos and Holger Schwarz and Bernhard Mitschang},
title = {{Enterprise-Wide Metadata Management - An Industry Case on the Current State and Challenges}},
booktitle = {24thInternational Conference on Business Information Systems},
editor = {Witold Abramowicz and S{\"o}ren Auer and Lewa\&\#324 and El\&\#380 Ska and Bieta},
publisher = {TIB Open Publishing},
institution = {Universit{\"a}t Stuttgart, Fakult{\"a}t Informatik, Elektrotechnik und Informationstechnik, Germany},
pages = {269--279},
type = {Konferenz-Beitrag},
month = {Juli},
year = {2021},
doi = {https://doi.org/10.52825/bis.v1i.47},
language = {Englisch},
cr-category = {A.0 General Literature, General},
department = {Universit{\"a}t Stuttgart, Institut f{\"u}r Parallele und Verteilte Systeme, Anwendersoftware},
abstract = {Abstract. Metadata management is a crucial success factor for companies today,
as for example, it enables exploiting data value fully or enables legal
compliance. With the emergence of new concepts, such as the data lake, and new
objectives, such as the enterprise-wide sharing of data, metadata management
has evolved and now poses a renewed challenge for companies. In this context,
we interviewed a globally active manufacturer to reveal how metadata management
is implemented in practice today and what challenges companies are faced with
and whether these constitute research gaps. As an outcome, we present the
company{\^a}€™s metadata management goals and their corresponding solution
approaches and challenges. An evaluation of the challenges through a literature
and tool review yields three research gaps, which are concerned with the
topics: (1) metadata management for data lakes, (2) categorizations and
compositions of metadata management tools for comprehensive metadata
management, and (3) the use of data marketplaces as metadata-driven exchange
platforms within an enterprise. The gaps lay the groundwork for further
research activities in the field of metadata management and the industry case
represents a starting point for research to realign with real-world industry
needs.},
url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=INPROC-2021-06&engl=0}
}
@inproceedings {INPROC-2021-05,
author = {Corinna Giebler and Christoph Gr{\"o}ger and Eva Hoos and Rebecca Eichler and Holger Schwarz and Bernhard Mitschang},
title = {{The Data Lake Architecture Framework}},
booktitle = {Datenbanksysteme f{\"u}r Business, Technologie und Web (BTW 2021), 19. Fachtagung des GI-Fachbereichs Datenbanken und Informationssysteme (DBIS), 13.-17. September 2021, Dresden, Germany},
publisher = {Gesellschaft f{\"u}r Informatik},
institution = {Universit{\"a}t Stuttgart, Fakult{\"a}t Informatik, Elektrotechnik und Informationstechnik, Germany},
pages = {351--370},
type = {Konferenz-Beitrag},
month = {September},
year = {2021},
doi = {10.18420/btw2021-19},
language = {Englisch},
cr-category = {H.4 Information Systems Applications},
department = {Universit{\"a}t Stuttgart, Institut f{\"u}r Parallele und Verteilte Systeme, Anwendersoftware},
abstract = {During recent years, data lakes emerged as a way to manage large amounts of
heterogeneous data for modern data analytics. Although various work on
individual aspects of data lakes exists, there is no comprehensive data lake
architecture yet. Concepts that describe themselves as a {\^a}€śdata lake
architecture{\^a}€ť are only partial. In this work, we introduce the data lake
architecture framework. It supports the definition of data lake architectures
by defining nine architectural aspects, i.e., perspectives on a data lake, such
as data storage or data modeling, and by exploring the interdependencies
between these aspects. The included methodology helps to choose appropriate
concepts to instantiate each aspect. To evaluate the framework, we use it to
configure an exemplary data lake architecture for a real-world data lake
implementation. This final assessment shows that our framework provides
comprehensive guidance in the configuration of a data lake architecture.},
url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=INPROC-2021-05&engl=0}
}
@inproceedings {INPROC-2020-55,
author = {Corinna Giebler and Christoph Gr{\"o}ger and Eva Hoos and Holger Schwarz and Bernhard Mitschang},
title = {{A Zone Reference Model for Enterprise-Grade Data Lake Management}},
booktitle = {Proceedings of the 24th IEEE Enterprise Computing Conference},
publisher = {IEEE},
institution = {Universit{\"a}t Stuttgart, Fakult{\"a}t Informatik, Elektrotechnik und Informationstechnik, Germany},
pages = {57--66},
type = {Konferenz-Beitrag},
month = {Oktober},
year = {2020},
keywords = {Data Lake; Zones; Reference Model; Industry Case; Industry Experience},
language = {Englisch},
cr-category = {H.4 Information Systems Applications},
contact = {Senden Sie eine E-Mail an corinna.giebler@ipvs.uni-stuttgart.de},
department = {Universit{\"a}t Stuttgart, Institut f{\"u}r Parallele und Verteilte Systeme, Anwendersoftware},
abstract = {Data lakes are on the rise as data platforms for any kind of analytics, from
data exploration to machine learning. They achieve the required flexibility by
storing heterogeneous data in their raw format, and by avoiding the need for
pre-defined use cases. However, storing only raw data is inefficient, as for
many applications, the same data processing has to be applied repeatedly. To
foster the reuse of processing steps, literature proposes to store data in
different degrees of processing in addition to their raw format. To this end,
data lakes are typically structured in zones. There exists various zone models,
but they are varied, vague, and no assessments are given. It is unclear which
of these zone models is applicable in a practical data lake implementation in
enterprises. In this work, we assess existing zone models using requirements
derived from multiple representative data analytics use cases of a real-world
industry case. We identify the shortcomings of existing work and develop a zone
reference model for enterprise-grade data lake management in a detailed manner.
We assess the reference model's applicability through a prototypical
implementation for a real-world enterprise data lake use case. This assessment
shows that the zone reference model meets the requirements relevant in practice
and is ready for industry use.},
url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=INPROC-2020-55&engl=0}
}
@inproceedings {INPROC-2019-15,
author = {Corinna Giebler and Christoph Gr{\"o}ger and Eva Hoos and Holger Schwarz},
title = {{Modeling Data Lakes with Data Vault: Practical Experiences, Assessment, and Lessons Learned}},
booktitle = {Proceedings of the 38th Conference on Conceptual Modeling (ER 2019)},
publisher = {Springer},
institution = {Universit{\"a}t Stuttgart, Fakult{\"a}t Informatik, Elektrotechnik und Informationstechnik, Germany},
pages = {1--14},
type = {Konferenz-Beitrag},
month = {November},
year = {2019},
keywords = {Data Lakes; Data Vault; Data Modeling; Industry Experience; Assessment; Lessons Learned},
language = {Deutsch},
cr-category = {H.2.1 Database Management Logical Design},
contact = {Senden Sie eine E-Mail an Corinna.Giebler@ipvs.uni-stuttgart.de},
department = {Universit{\"a}t Stuttgart, Institut f{\"u}r Parallele und Verteilte Systeme, Anwendersoftware},
abstract = {Data lakes have become popular to enable organization-wide analytics on
heterogeneous data from multiple sources. Data lakes store data in their raw
format and are often characterized as schema-free. Nevertheless, it turned out
that data still need to be modeled, as neglecting data modeling may lead to
issues concerning e.g., quality and integration. In current research literature
and industry practice, Data Vault is a popular modeling technique for
structured data in data lakes. It promises a flexible, extensible data model
that preserves data in their raw format. However, hardly any research or
assessment exist on the practical usage of Data Vault for modeling data lakes.
In this paper, we assess the Data Vault model{\^a}€™s suitability for the data lake
context, present lessons learned, and investigate success factors for the use
of Data Vault. Our discussion is based on the practical usage of Data Vault in
a large, global manufacturer{\^a}€™s data lake and the insights gained in
real-world analytics projects.},
url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=INPROC-2019-15&engl=0}
}
@inproceedings {INPROC-2019-14,
author = {Corinna Giebler and Christoph Gr{\"o}ger and Eva Hoos and Holger Schwarz},
title = {{Leveraging the Data Lake - Current State and Challenges}},
booktitle = {Proceedings of the 21st International Conference on Big Data Analytics and Knowledge Discovery (DaWaK'19)},
publisher = {Springer Nature},
institution = {Universit{\"a}t Stuttgart, Fakult{\"a}t Informatik, Elektrotechnik und Informationstechnik, Germany},
pages = {1--10},
type = {Konferenz-Beitrag},
month = {August},
year = {2019},
keywords = {Data Lakes, State of the Art, Challenges},
language = {Deutsch},
cr-category = {H.2.4 Database Management Systems,
H.2.8 Database Applications},
contact = {Senden Sie eine E-Mail an Corinna.Giebler@ipvs.uni-stuttgart.de},
department = {Universit{\"a}t Stuttgart, Institut f{\"u}r Parallele und Verteilte Systeme, Anwendersoftware},
abstract = {The digital transformation leads to massive amounts of heterogeneous data
challenging traditional data warehouse solutions in enterprises. In order to
exploit these complex data for competitive advantages, the data lake recently
emerged as a concept for more flexible and powerful data analytics. However,
existing literature on data lakes is rather vague and incomplete, and the
various realization approaches that have been proposed neither cover all
aspects of data lakes nor do they provide a comprehensive design and
realization strategy. Hence, enterprises face multiple challenges when building
data lakes. To address these shortcomings, we investigate existing data lake
literature and discuss various design and realization aspects for data lakes,
such as governance or data models. Based on these insights, we identify
challenges and research gaps concerning (1) data lake architecture, (2) data
lake governance, and (3) a comprehensive strategy to realize data lakes. These
challenges still need to be addressed to successfully leverage the data lake in
practice.},
url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=INPROC-2019-14&engl=0}
}
@inproceedings {INPROC-2017-49,
author = {Eva Hoos and Matthias Wieland and Bernhard Mitschang},
title = {{Analysis Method for Conceptual Context Modeling Applied in Production Environments}},
booktitle = {Proceedings of 20th International Conference on Business Information Systems (BIS)},
publisher = {Springer International Publishing},
institution = {Universit{\"a}t Stuttgart, Fakult{\"a}t Informatik, Elektrotechnik und Informationstechnik, Germany},
pages = {313--325},
type = {Konferenz-Beitrag},
month = {Mai},
year = {2017},
keywords = {Context-awareness; production environments; Industry 4.0},
language = {Englisch},
cr-category = {J.1 Administration Data Processing},
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-2017-49&engl=0}
}
@inproceedings {INPROC-2017-48,
author = {Eva Hoos and Pascal Hirmer and Bernhard Mitschang},
title = {{Context-Aware Decision Information Packages: An Approach to Human-Centric Smart Factories}},
booktitle = {Proceedings of the 21st European Conference on Advances in Databases and Information Systems (ADBIS)},
publisher = {Springer International Publishing AG 2017},
institution = {Universit{\"a}t Stuttgart, Fakult{\"a}t Informatik, Elektrotechnik und Informationstechnik, Germany},
pages = {42--56},
type = {Konferenz-Beitrag},
month = {August},
year = {2017},
keywords = {Industry 4.0; Context-awareness; Data Provisioning; Smart Factory},
language = {Deutsch},
cr-category = {H.3.3 Information Search and Retrieval},
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-2017-48&engl=0}
}
@inproceedings {INPROC-2017-40,
author = {Eva Hoos and Pascal Hirmer and Bernhard Mitschang},
title = {{Improving Problem Resolving on the Shop Floor by Context-Aware Decision Information Packages}},
booktitle = {Proceedings of the CAiSE 2017 Forum},
editor = {Xavier Franch and Jolita Ralyt{\'e}},
address = {Essen},
publisher = {CEUR Workshop Proceedings},
institution = {Universit{\"a}t Stuttgart, Fakult{\"a}t Informatik, Elektrotechnik und Informationstechnik, Germany},
pages = {121--128},
type = {Workshop-Beitrag},
month = {Juni},
year = {2017},
keywords = {Industry 4.0; Context-Awareness; Engineering},
language = {Englisch},
cr-category = {J.1 Administration Data Processing},
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-2017-40&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-2015-62,
author = {Eva Maria Grochowski and Eva Hoos and Stefan Waitzinger and Dieter Spath and Bernhard Mitschang},
title = {{Web-based collaboration system for interdisciplinary and interorganizational development teams: case study}},
booktitle = {Proceeding of the 23rd International Conference on Production Research},
publisher = {-},
institution = {Universit{\"a}t Stuttgart, Fakult{\"a}t Informatik, Elektrotechnik und Informationstechnik, Germany},
pages = {1--11},
type = {Konferenz-Beitrag},
month = {August},
year = {2015},
keywords = {Collaboration; Web-based Platform},
language = {Englisch},
cr-category = {J.1 Administration Data Processing},
department = {Universit{\"a}t Stuttgart, Institut f{\"u}r Parallele und Verteilte Systeme, Anwendersoftware},
abstract = {The automotive industry faces three major challenges – shortage of fossil
fuels, politics of global warming and rising competition. In order to remain
competitive companies have to develop more efficient and alternative fuel
vehicles. Out of these challenges new cooperation models become inevitable. The
development of complex products like automobiles claims skills of various
disciplines e.g. engineering, IT. Furthermore, these skills are spread all over
various companies within the supply chain and beyond. Hence, supporting IT
systems for collaborative, innovative work is absolutely essential.
Interdisciplinary and interorganizational development has new demands on
information systems. These demands are not well analyzed at the moment and
therefore, existing collaboration platforms cannot address them. In order to
determine these new requirements and show the gap to existing collaboration
platform we performed a case study. In this case study, we analyze the research
campus “Active Research Environment for the Next Generation of Automobiles”
(ARENA2036). It is a is a new cooperation form, where diverse partners from the
industry, research institutes and universities elaborate collaboratively future
topics in the field of production and light weight construction under “one
single roof”. We focus on the special needs of the interdisciplinary,
interorganizational partners. The requirements were polled by a questionnaire.
About 80 percent of the active research workers in ARENA2036 answered the
questionnaire. By the answers we can identify the special needs and also role
profiles of the collaborators. The resulting role profiles specify the personal
requirements. These are used for an evaluation of existing information
platforms. The deficits between the offered features and the demands of the
partners, as well as new technologies supporting the individual needs of users
are the foundation for the information system concept for ARENA2036. In our
findings we present a role-based view on requirements for the development of an
information system for collaboration and cooperation. Based on these
requirements we then develop a concept for mobile apps with focus on a
role-based design.},
url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=INPROC-2015-62&engl=0}
}
@inproceedings {INPROC-2014-65,
author = {Eva Hoos},
title = {{Design method for developing a Mobile Engineering-Application Middleware (MEAM)}},
booktitle = {Proceedings of the 2014 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops,24-28 March, 2014, Budapest, Hungary},
publisher = {IEEE},
institution = {Universit{\"a}t Stuttgart, Fakult{\"a}t Informatik, Elektrotechnik und Informationstechnik, Germany},
pages = {176--177},
type = {Workshop-Beitrag},
month = {M{\"a}rz},
year = {2014},
doi = {10.1109/PerComW.2014.6815193},
language = {Englisch},
cr-category = {J.2 Physical Sciences and Engineering,
J.4 Social and Behavioral Sciences},
department = {Universit{\"a}t Stuttgart, Institut f{\"u}r Parallele und Verteilte Systeme, Anwendersoftware},
abstract = {Mobile Apps running on smartphones and tablets offer a new possibility to
enhance the work of engineers because they provide an easy-to-use,
touchscreen-based handling and can be used anytime and anywhere. Introducing
mobile apps in the engineering domain is difficult because the IT environment
is heterogeneous and engineering-specific challenges in the app development
arise e. g., large amount of data and high security requirements. There is a
need for an engineering-specific middleware to facilitate and standardize the
app development. However, such a middleware does not yet exist as well as a
holistic set of requirements for the development. Therefore, we propose a
design method which offers a systematic procedure to develop Mobile
Engineering-Application Middleware.},
url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=INPROC-2014-65&engl=0}
}
@inproceedings {INPROC-2014-64,
author = {Eva Hoos and Christoph Gr{\"o}ger and Bernhard Mitschang},
title = {{Mobile Apps in Engineering: A Process-Driven Analysis of Business Potentials and Technical Challenges}},
booktitle = {Proceedings of the 9th CIRP Conference on Intelligent Computation in Manufacturing Engineering (CIRP ICME), 23-25 July, 2014, Capri (Naples), Italy},
publisher = {CIRP},
institution = {Universit{\"a}t Stuttgart, Fakult{\"a}t Informatik, Elektrotechnik und Informationstechnik, Germany},
type = {Konferenz-Beitrag},
month = {Juli},
year = {2014},
language = {Deutsch},
cr-category = {H.4.0 Information Systems Applications General,
J.4 Social and Behavioral Sciences,
J.2 Physical Sciences and Engineering},
department = {Universit{\"a}t Stuttgart, Institut f{\"u}r Parallele und Verteilte Systeme, Anwendersoftware},
abstract = {Mobile apps on smartphones and tablet PCs are more and more employed in
enterprises to optimize business processes, e.g. by elimination of paper-based
data collection. With respect to engineering, mobile apps provide a huge
potential for increased flexibility and efficiency due to their anywhere and
anytime characteristics, e.g., for product testing in the field. However, not
every usage of mobile apps is beneficial from a business point of view and
existing apps for engineering represent only rudimentary front-ends for
stationary IT systems without an app-oriented redesign. Hence, there are three
core challenges to leverage the potential of mobile apps in engineering: (1)
identifying value-added app usage scenarios from a process point of view, (2)
realizing a task-oriented and context-aware user interface design and (3)
mastering technical obstacles at the app implementation. In this paper, we
address these challenges by a case-oriented analysis of selected engineering
processes in the automotive industry in order to identify engineering tasks
suited for the usage of mobile apps. On this basis, we design corresponding
engineering apps and analyze their business potentials. Moreover, we derive
common technological challenges for the development of engineering apps, e.g.
data synchronization aspects, and highlight further research issues.},
url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=INPROC-2014-64&engl=0}
}
@inproceedings {INPROC-2014-14,
author = {Eva Hoos and Christoph Gr{\"o}ger and Stefan Kramer and Bernhard Mitschang},
title = {{Improving Business Processes through Mobile Apps - An Analysis Framework to Identify Value-added App Usage Scenarios}},
booktitle = {Proceedings of the 16th International Conference on Enterprise Information Systems (ICEIS), 27-30 April, 2014, Lisbon, Portugal},
publisher = {SciTePress},
institution = {Universit{\"a}t Stuttgart, Fakult{\"a}t Informatik, Elektrotechnik und Informationstechnik, Germany},
type = {Konferenz-Beitrag},
month = {April},
year = {2014},
keywords = {Business Processes; Analysis Framework; Mobile Application},
language = {Englisch},
cr-category = {H.1.1 Systems and Information Theory,
K.6.1 Project and People Management},
department = {Universit{\"a}t Stuttgart, Institut f{\"u}r Parallele und Verteilte Systeme, Anwendersoftware},
abstract = {Mobile apps offer new possibilities to improve business processes. However, the
introduction of mobile apps is typically carried out from a technology point of
view. Hence, process improvement from a business point of view is not
guaranteed. There is a methodological lack for a holistic analysis of business
processes regarding mobile technology. For this purpose, we present an analysis
framework, which comprises a systematic methodology to identify value-added
usage scenarios of mobile technology in business processes with a special focus
on mobile apps. The framework is based on multi-criteria analysis and portfolio
analy- sis techniques and it is evaluated in a case-oriented investigation in
the automotive industry.},
url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=INPROC-2014-14&engl=0}
}
@article {ART-2023-07,
author = {Rebecca Eichler and Christoph Gr{\"o}ger and Eva Hoos and Christoph Stach and Holger Schwarz and Bernhard Mitschang},
title = {{Introducing the enterprise data marketplace: a platform for democratizing company data}},
journal = {Journal of Big Data},
publisher = {Springer Nature},
volume = {10},
pages = {1--38},
type = {Artikel in Zeitschrift},
month = {November},
year = {2023},
issn = {2196-1115},
doi = {10.1186/s40537-023-00843-z},
keywords = {Data Catalog; Data Democratization; Data Market; Data Sharing; Enterprise Data Marketplace; Metadata Management},
language = {Englisch},
cr-category = {E.m Data Miscellaneous,
H.3.7 Digital Libraries,
H.4.m Information Systems Applications Miscellaneous},
department = {Universit{\"a}t Stuttgart, Institut f{\"u}r Parallele und Verteilte Systeme, Anwendersoftware},
abstract = {In this big data era, multitudes of data are generated and collected which
contain the potential to gain new insights, e.g., for enhancing business
models. To leverage this potential through, e.g., data science and analytics
projects, the data must be made available. In this context, data marketplaces
are used as platforms to facilitate the exchange and thus, the provisioning of
data and data-related services. Data marketplaces are mainly studied for the
exchange of data between organizations, i.e., as external data marketplaces.
Yet, the data collected within a company also has the potential to provide
valuable insights for this same company, for instance to optimize business
processes. Studies indicate, however, that a significant amount of data within
companies remains unused. In this sense, it is proposed to employ an Enterprise
Data Marketplace, a platform to democratize data within a company among its
employees. Specifics of the Enterprise Data Marketplace, how it can be
implemented or how it makes data available throughout a variety of systems like
data lakes has not been investigated in literature so far. Therefore, we
present the characteristics and requirements of this kind of marketplace. We
also distinguish it from other tools like data catalogs, provide a platform
architecture and highlight how it integrates with the company{\^a}€™s system
landscape. The presented concepts are demonstrated through an Enterprise Data
Marketplace prototype and an experiment reveals that this marketplace
significantly improves the data consumer workflows in terms of efficiency and
complexity. This paper is based on several interdisciplinary works combining
comprehensive research with practical experience from an industrial
perspective. We therefore present the Enterprise Data Marketplace as a distinct
marketplace type and provide the basis for establishing it within a company.},
url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=ART-2023-07&engl=0}
}
@article {ART-2020-20,
author = {Corinna Giebler and Christoph Gr{\"o}ger and Eva Hoos and Rebecca Eichler and Holger Schwarz and Bernhard Mitschang},
title = {{Data Lakes auf den Grund gegangen - Herausforderungen und Forschungsl{\"u}cken in der Industriepraxis}},
journal = {Datenbank Spektrum},
publisher = {Springer},
volume = {20},
pages = {57--69},
type = {Artikel in Zeitschrift},
month = {Januar},
year = {2020},
keywords = {Data Lakes; Analytics; Stand der Technik; Herausforderungen; Praxisbeispiel},
language = {Deutsch},
cr-category = {H.4 Information Systems Applications},
contact = {Senden Sie eine E-Mail an Corinna.Giebler@ipvs.uni-stuttgart.de},
department = {Universit{\"a}t Stuttgart, Institut f{\"u}r Parallele und Verteilte Systeme, Anwendersoftware},
abstract = {Unternehmen stehen zunehmend vor der Herausforderung, gro{\ss}e, heterogene Daten
zu verwalten und den darin enthaltenen Wert zu extrahieren. In den letzten
Jahren kam darum der Data Lake als neuartiges Konzept auf, um diese komplexen
Daten zu verwalten und zu nutzen. Wollen Unternehmen allerdings einen solchen
Data Lake praktisch umsetzen, so sto{\ss}en sie auf vielf{\"a}ltige
Herausforderungen, wie beispielsweise Widerspr{\"u}che in der Definition oder
unscharfe und fehlende Konzepte. In diesem Beitrag werden konkrete Projekte
eines global agierenden Industrieunternehmens genutzt, um bestehende
Herausforderungen zu identifizieren und Anforderungen an Data Lakes
herzuleiten. Diese Anforderungen werden mit der verf{\"u}gbaren Literatur zum
Thema Data Lake sowie mit existierenden Ans{\"a}tzen aus der Forschung
abgeglichen. Die Gegen{\"u}berstellung zeigt, dass f{\"u}nf gro{\ss}e Forschungsl{\"u}cken
bestehen: 1. Unklare Datenmodellierungsmethoden, 2. Fehlende
Data-Lake-Referenzarchitektur, 3. Unvollst{\"a}ndiges Metadatenmanagementkonzept,
4. Unvollst{\"a}ndiges Data-Lake-Governance-Konzept, 5. Fehlende ganzheitliche
Realisierungsstrategie.},
url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=ART-2020-20&engl=0}
}
@article {ART-2020-11,
author = {Corinna Giebler and Christoph Gr{\"o}ger and Eva Hoos and Rebecca Eichler and Holger Schwarz and Bernhard Mitschang},
title = {{Data Lakes auf den Grund gegangen - Herausforderungen und Forschungsl{\"u}cken in der Industriepraxis}},
journal = {Datenbank Spektrum},
publisher = {Springer-Verlag},
volume = {20},
pages = {57--69},
type = {Artikel in Zeitschrift},
month = {Januar},
year = {2020},
keywords = {Data Lakes; Industryerfahrung},
language = {Deutsch},
cr-category = {H.2.1 Database Management Logical Design},
contact = {Senden Sie eine E-Mail an Corinna.Giebler@ipvs.uni-stuttgart.de},
department = {Universit{\"a}t Stuttgart, Institut f{\"u}r Parallele und Verteilte Systeme, Anwendersoftware},
abstract = {Unternehmen stehen zunehmend vor der Herausforderung, gro{\ss}e, heterogene Daten
zu verwalten und den darin enthaltenen Wert zu extrahieren. In den letzten
Jahren kam darum der Data Lake als neuartiges Konzept auf, um diese komplexen
Daten zu verwalten und zu nutzen. Wollen Unternehmen allerdings einen solchen
Data Lake praktisch umsetzen, so sto{\ss}en sie auf vielf{\"a}ltige
Herausforderungen, wie beispielsweise Widerspr{\"u}che in der Definition oder
unscharfe und fehlende Konzepte. In diesem Beitrag werden konkrete Projekte
eines global agierenden Industrieunternehmens genutzt, um bestehende
Herausforderungen zu identifizieren und Anforderungen an Data Lakes
herzuleiten. Diese Anforderungen werden mit der verf{\"u}gbaren Literatur zum
Thema Data Lake sowie mit existierenden Ans{\"a}tzen aus der Forschung
abgeglichen. Die Gegen{\"u}berstellung zeigt, dass f{\"u}nf gro{\ss}e Forschungsl{\"u}cken
bestehen: 1. Unklare Datenmodellierungsmethoden, 2. Fehlende
Data-Lake-Referenzarchitektur, 3. Unvollst{\"a}ndiges Metadatenmanagementkonzept,
4. Unvollst{\"a}ndiges Data-Lake-Governance-Konzept, 5. Fehlende ganzheitliche
Realisierungsstrategie.},
url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=ART-2020-11&engl=0}
}
@article {ART-2020-10,
author = {Corinna Giebler and Christoph Gr{\"o}ger and Eva Hoos and Rebecca Eichler and Holger Schwarz and Bernhard Mitschang},
title = {{Data Lakes auf den Grund gegangen - Herausforderungen und Forschungsl{\"u}cken in der Industriepraxis}},
journal = {Datenbank Spektrum},
publisher = {Springer-Verlag},
volume = {20},
pages = {57--69},
type = {Artikel in Zeitschrift},
month = {Januar},
year = {2020},
keywords = {Data Lakes; Industryerfahrung},
language = {Deutsch},
cr-category = {H.2.1 Database Management Logical Design},
contact = {Senden Sie eine E-Mail an Corinna.Giebler@ipvs.uni-stuttgart.de},
department = {Universit{\"a}t Stuttgart, Institut f{\"u}r Parallele und Verteilte Systeme, Anwendersoftware},
abstract = {Unternehmen stehen zunehmend vor der Herausforderung, gro{\ss}e, heterogene Daten
zu verwalten und den darin enthaltenen Wert zu extrahieren. In den letzten
Jahren kam darum der Data Lake als neuartiges Konzept auf, um diese komplexen
Daten zu verwalten und zu nutzen. Wollen Unternehmen allerdings einen solchen
Data Lake praktisch umsetzen, so sto{\ss}en sie auf vielf{\"a}ltige
Herausforderungen, wie beispielsweise Widerspr{\"u}che in der Definition oder
unscharfe und fehlende Konzepte. In diesem Beitrag werden konkrete Projekte
eines global agierenden Industrieunternehmens genutzt, um bestehende
Herausforderungen zu identifizieren und Anforderungen an Data Lakes
herzuleiten. Diese Anforderungen werden mit der verf{\"u}gbaren Literatur zum
Thema Data Lake sowie mit existierenden Ans{\"a}tzen aus der Forschung
abgeglichen. Die Gegen{\"u}berstellung zeigt, dass f{\"u}nf gro{\ss}e Forschungsl{\"u}cken
bestehen: 1. Unklare Datenmodellierungsmethoden, 2. Fehlende
Data-Lake-Referenzarchitektur, 3. Unvollst{\"a}ndiges Metadatenmanagementkonzept,
4. Unvollst{\"a}ndiges Data-Lake-Governance-Konzept, 5. Fehlende ganzheitliche
Realisierungsstrategie.},
url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=ART-2020-10&engl=0}
}
@article {ART-2020-04,
author = {Corinna Giebler and Christoph Gr{\"o}ger and Eva Hoos and Rebecca Eichler and Holger Schwarz and Bernhard Mitschang},
title = {{Data Lakes auf den Grund gegangen: Herausforderungen und Forschungsl{\"u}cken in der Industriepraxis}},
journal = {Datenbank-Spektrum},
publisher = {Springer},
volume = {20},
number = {1},
pages = {57--69},
type = {Artikel in Zeitschrift},
month = {Januar},
year = {2020},
doi = {10.1007/s13222-020-00332-0},
keywords = {Data Lake; Analytics; Stand der Technik; Herausforderungen; Praxisbeispiel},
language = {Deutsch},
cr-category = {A.1 General Literature, Introductory and Survey,
E.0 Data General},
ee = {https://rdcu.be/b0WM8},
contact = {Senden Sie eine E-Mail an Corinna.Giebler@ipvs.uni-stuttgart.de},
department = {Universit{\"a}t Stuttgart, Institut f{\"u}r Parallele und Verteilte Systeme, Anwendersoftware},
abstract = {Unternehmen stehen zunehmend vor der Herausforderung, gro{\ss}e, heterogene Daten
zu verwalten und den darin enthaltenen Wert zu extrahieren. In den letzten
Jahren kam darum der Data Lake als neuartiges Konzept auf, um diese komplexen
Daten zu verwalten und zu nutzen. Wollen Unternehmen allerdings einen solchen
Data Lake praktisch umsetzen, so sto{\ss}en sie auf vielf{\"a}ltige
Herausforderungen, wie beispielsweise Widerspr{\"u}che in der Definition oder
unscharfe und fehlende Konzepte. In diesem Beitrag werden konkrete Projekte
eines global agierenden Industrieunternehmens genutzt, um bestehende
Herausforderungen zu identifizieren und Anforderungen an Data Lakes
herzuleiten. Diese Anforderungen werden mit der verf{\"u}gbaren Literatur zum
Thema Data Lake sowie mit existierenden Ans{\"a}tzen aus der Forschung
abgeglichen. Die Gegen{\"u}berstellung zeigt, dass f{\"u}nf gro{\ss}e Forschungsl{\"u}cken
bestehen: 1. Unklare Datenmodellierungsmethoden, 2. Fehlende
Data-Lake-Referenzarchitektur, 3. Unvollst{\"a}ndiges Metadatenmanagementkonzept,
4. Unvollst{\"a}ndiges Data-Lake-Governance-Konzept, 5. Fehlende ganzheitliche
Realisierungsstrategie.},
url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=ART-2020-04&engl=0}
}
@article {ART-2018-07,
author = {Eva Hoos and Pascal Hirmer and Bernhard Mitschang},
title = {{Automated Creation and Provisioning of Decision Information Packages for the Smart Factory}},
journal = {Complex Systems Informatics and Modeling Quarterly},
publisher = {Online},
volume = {15},
pages = {72--89},
type = {Artikel in Zeitschrift},
month = {August},
year = {2018},
issn = {2255-9922},
doi = {10.7250/csimq.2018-15.04},
keywords = {Industry 4.0; Context-awareness; Data Provisioning},
language = {Englisch},
cr-category = {H.0 Information Systems General},
ee = {https://csimq-journals.rtu.lv/article/view/csimq.2018-15.04},
contact = {Pascal.Hirmer@ipvs.uni-stuttgart.de},
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=ART-2018-07&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-2015-02,
author = {Eva Hoos and Christoph Gr{\"o}ger and Stefan Kramer and Bernhard Mitschang},
title = {{ValueApping: An Analysis Method to Identify Value-Adding Mobile Enterprise Apps in Business Processes}},
series = {Enterprise Information Systems},
publisher = {Springer International Publishing},
series = {Lecture Notes in Business Information Processing},
volume = {227},
type = {Beitrag in Buch},
month = {September},
year = {2015},
language = {Englisch},
cr-category = {H.1.1 Systems and Information Theory},
department = {Universit{\"a}t Stuttgart, Institut f{\"u}r Parallele und Verteilte Systeme, Anwendersoftware},
abstract = {Mobile enterprise apps provide novel possibilities for the optimization and
redesign of business processes, e.g., by the elimination of paper-based data
acquisitioning or ubiquitous access to up-to-date information. To leverage
these business potentials, a critical success factor is the identification and
evaluation of valueadding MEAs based on an analysis of the business process.
For this purpose, we present ValueApping, a systematic analysis method to
identify usage scenarios for value-adding mobile enterprise apps in business
processes and to analyze their business benefits. We describe the different
analysis steps and corresponding analysis artifacts of ValueApping and discuss
the results of a case-oriented evaluation in the automotive industry.},
url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=INBOOK-2015-02&engl=0}
}