The Stuttgart IT Architecture for Manufacturing

Kassner, Laura; Gröger, Christoph; Königsberger, Jan; Hoos, Eva; Kiefer, Cornelia; Weber, Christian; Silcher, Stefan; Mitschang, Bernhard

doi:10.1007/978-3-319-62386-3_3

Cited by 15 publications

(9 citation statements)

References 23 publications

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“…Table VII lists the tools cited more than once. Of all the tools, Apache Hadoop was the most mentioned, having 37 citations ( [1], [6], [8], [9], [12], [18], [23]- [25], [27], [28], [30], [31], [40], [42]- [44], [47], [48], [50], [52], [55], [61], [65], [67]- [71], [73], [76], [80], [82]- [86], [88]). Hadoop is a platform that allows distributed processing of large data sets in computer clusters.…”

Section: Resultsmentioning

confidence: 99%

“…Tableau is a software for interactive data visualization. [6], [9], [31], [47], [55], [68], [76], [82], [85] Apache Flume 7 [1], [6], [27], [52], [61], [70], [83] Apache Sqoop 5 [1], [27], [47], [52], [ [6], [21], [40], [41], [45] MongoDB 6 [16], [33], [35], [41], [43], [ [1], [6], [8], [9], [12], [18], [24], [27], [33], [35], [40], [42], [44], [47], [49], [51], [52], [55], [61], [68], [69], [71],…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

A Mapping Study about Data Lakes: An Improved Definition and Possible Architectures

Couto¹,

Borges²,

Ruiz³

et al. 2019

International Conferences on Software Engineering and Knowledge Engineering

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In the past few years, data lakes emerged as a trending topic in big data technologies. Although literature presents different points of view related to its functionalities, it serves mainly to store a variety of data in a big data context. In this paper, we aim to identify and analyze data lake definitions and possible architectures. Our methodology was composed of a systematic literature mapping based on PRISMA, software engineering best practices to perform reviews, and Kappa method to assess results' quality. We performed the search in eight different electronic databases to achieve a wide variety of publishers in Computer Science. We first identified 662 papers matching our search criteria; after filtering, we selected 87 papers for review. We found that the term data lakes was first defined by James Dixon in 2010. We also found that the term is often related to raw data repositories. From the identified definitions, we propose a new one as a means to better state what data lakes refer to and improve how the community use them. Moreover, we foind that Hadoop and its ecosystem compose the most used toolset to create data lakes, revealing that this is the mainstream in architectures for data lakes as of today's available technologies.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

A Mapping Study about Data Lakes: An Improved Definition and Possible Architectures

Couto¹,

Borges²,

Ruiz³

et al. 2019

International Conferences on Software Engineering and Knowledge Engineering

View full text Add to dashboard Cite

show abstract

“…To evaluate the existent models by testing their limits and generic extent we use a representative metadata management use case, which is based on an Industry 4.0 scenario with an enterprise data lake. The data lake contains data on products, processes and customers including personal data (see [13] for data management in industrial enterprises and Industry 4.0).…”

Section: Metadata Management Use Case For Model Evaluationmentioning

confidence: 99%

HANDLE - A Generic Metadata Model for Data Lakes

Eichler

Giebler

Gröger

et al. 2020

Lecture Notes in Computer Science

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The substantial increase in generated data induced the development of new concepts such as the data lake. A data lake is a large storage repository designed to enable flexible extraction of the data's value. A key aspect of exploiting data value in data lakes is the collection and management of metadata. To store and handle the metadata, a generic metadata model is required that can reflect metadata of any potential metadata management use case, e.g., data versioning or data lineage. However, an evaluation of existent metadata models yields that none so far are sufficiently generic. In this work, we present HANDLE, a generic metadata model for data lakes, which supports the flexible integration of metadata, data lake zones, metadata on various granular levels, and any metadata categorization. With these capabilities HANDLE enables comprehensive metadata management in data lakes. We show HANDLE's feasibility through the application to an exemplary access-use-case and a prototypical implementation. A comparison with existent models yields that HANDLE can reflect the same information and provides additional capabilities needed for metadata management in data lakes.

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“…Als Konzept zur Adressierung solcher Herausforderungen bei der Datenanalyse hat sich die Lambda-Architektur etabliert [26]. Sie strukturiert die Datenverarbeitung in drei Schichten, die sogenannten Batch, Speed und Serving Layers, die sich insbesondere in ihrer Latenz bei der Datenverarbeitung unterscheiden [26,27]. Hintergrund ist, dass es technisch nicht möglich ist, bei Ereignissen in akzeptabler Rechenzeit alle historischen Daten auszuwerten.…”

Section: Lambda-architekturunclassified

“…Im Batch Layer findet deshalb eine Vorverarbeitung der großen Datenmengen statt, die über den Serving Layer performant zur Verfügung gestellt werden. Aufgrund der engen Verknüpfung werden Batch und Serving Layer oftmals auch gemeinsam als Batch Layer bezeichnet [26][27][28]. Ergänzt werden diese Auswertungen um echtzeitnahe Analysen im Speed Layer.…”

Section: Lambda-architekturunclassified

Wertschöpfungsnetzwerk und Produktion integrieren/Integrating supply network and manufacturing: An adaptive, closed-loop-based approach

Bauer¹,

Bauernhansl²,

Sauer³

2020

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Von der Integration von Wertschöpfungsnetzwerk und Produktion versprechen sich produzierende Unternehmen eine schnellere Reaktion auf Ereignisse und signifikante Kosteneinsparungen. Der Beitrag zeigt einen adaptiven Regelkreis als technischen Lösungsbaustein für solch eine vertiefte Integration.   By integrating supply network and manufacturing, companies expect a faster response to events and significant cost savings. This article shows an adaptive control loop as a technical solution component for such a deep integration.

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The Stuttgart IT Architecture for Manufacturing

Cited by 15 publications

References 23 publications

A Mapping Study about Data Lakes: An Improved Definition and Possible Architectures

A Mapping Study about Data Lakes: An Improved Definition and Possible Architectures

HANDLE - A Generic Metadata Model for Data Lakes

Wertschöpfungsnetzwerk und Produktion integrieren/Integrating supply network and manufacturing: An adaptive, closed-loop-based approach

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