On Warehouses, Lakes, and Spaces: The Changing Role of Conceptual Modeling for Data Integration

Jarke, Matthias; Quix, Christoph

doi:10.1007/978-3-319-67271-7_16

Cited by 19 publications

(11 citation statements)

References 44 publications

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“…For example, the quality assurance of production companies could require access to detailed process data a long time after the production is finished, which cannot be realized by traditional data integration approaches that use carefully engineered data processing workflows to extract, transform, and load data into an integrated data store. For the collection of data, we envision a data lake platform in which data is stored in its raw format without prior integration or aggregation [61,74]. Compression techniques on sensor data could be applied to address the real-time requirements by reducing the amount of data, but a lossless compression should be guaranteed.…”

Section: Interconnected and Industry-capable Infrastructurementioning

confidence: 99%

A Computer Science Perspective on Digital Transformation in Production

Brauner

Dalibor

Jarke

et al. 2022

ACM Trans. Internet Things

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The Industrial Internet-of-Things (IIoT) promises significant improvements for the manufacturing industry by facilitating the integration of manufacturing systems by Digital Twins. However, ecological and economic demands also require a cross-domain linkage of multiple scientific perspectives from material sciences, engineering, operations, business, and ergonomics, as optimization opportunities can be derived from any of these perspectives. To extend the IIoT to a true Internet of Production , two concepts are required: first, a complex, interrelated network of Digital Shadows which combine domain-specific models with data-driven AI methods; and second, the integration of a large number of research labs, engineering, and production sites as a World Wide Lab which offers controlled exchange of selected, innovation-relevant data even across company boundaries. In this article, we define the underlying Computer Science challenges implied by these novel concepts in four layers: Smart human interfaces provide access to information that has been generated by model-integrated AI . Given the large variety of manufacturing data, new data modeling techniques should enable efficient management of Digital Shadows, which is supported by an interconnected infrastructure . Based on a detailed analysis of these challenges, we derive a systematized research roadmap to make the vision of the Internet of Production a reality.

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Section: Interconnected and Industry-capable Infrastructurementioning

confidence: 99%

A Computer Science Perspective on Digital Transformation in Production

Brauner

Dalibor

Jarke

et al. 2022

ACM Trans. Internet Things

Self Cite

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“…Stimulated by the growing concerns of losing data sovereignty in the presence of dominating Internet platforms, the industrial data space initiative extends these issues by a strong emphasis on sovereign data exchange among local data spaces/data lakes of small and medium IT users or vendor organizations [70]. Additional requirements collected from a broad range of industries and public organizations [101] include, e.g., trusted connectors controlling the import and export of data among dataspace partners, as well as usage control policies and services, in addition to what is needed for data lakes.…”

Section: Data Lakes and Dataspacesmentioning

confidence: 99%

“…Tasks in the middle (maintenance) conduct the general management and organization of the ingested datasets, but can also be considered as the preparation for querying. This architecture can also be seen as an abstraction of earlier layer-based data lake proposals [120,115,70,38]. But here we define relevant components, and specify their functions.…”

Section: Our Proposed Architecturementioning

confidence: 99%

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Data lake concept and systems: a survey

Hai¹,

Koutras²,

Quix³

et al. 2021

Preprint

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Although big data has been discussed for some years, it still has many research challenges, especially the variety of data. It poses a huge difficulty to efficiently integrate, access, and query the large volume of diverse data in information silos with the traditional 'schema-on-write' approaches such as data warehouses. Data lakes have been proposed as a solution to this problem. They are repositories storing raw data in its original formats and providing a common access interface. This survey reviews the development, definition, and architectures of data lakes. We provide a comprehensive overview of research questions for designing and building data lakes. We classify the existing data lake systems based on their provided functions, which makes this survey a useful technical reference for designing, implementing and applying data lakes. We hope that the thorough comparison of existing solutions and the discussion of open research challenges in this survey would motivate the future development of data lake research and practice.

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“…IDS provides an architecture, blueprint, standard, and platform for data-sharing among member organizations in a reliable, transparent, compliant, and accountable manner [56]. IDS functions as an intermediary actor that enables member organizations to share knowledge in a fair and seamless manner by enabling the formation and enforcement of data-sharing commitments and obligations [57].…”

Section: Redundant [Knowledge Assets]mentioning

confidence: 99%

A Modeling Approach for Getting to Win-Win in Industrial Collaboration under Strategic Coopetition

Pant

2019

CSIMQ

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Interorganizational coopetition describes a relationship in which two or more organizations cooperate and compete simultaneously. Actors under coopetition cooperate to achieve collective objectives and compete to maximize their individual benefits. Such relationships are based on the logic of win-win strategies that necessitate decision-makers in coopeting organizations to develop relationships that yield favorable outcomes for each actor. We follow a strategic modeling approach that combines i* goal-modeling to explore strategic alternatives of actors with Game Tree decision-modeling to evaluate the actions and payoffs of those players. In this article, we elaborate on the method, illustrating one particular pathway towards a positive-sum outcomethrough the introduction of an intermediary actor. This article demonstrates the activation of one component in this guided approach of systematically searching for alternatives to generate a new win-win strategy. We also present a metamodel for relating i* models and Game Trees. A hypothetical industrial scenario focusing on the Industrial Data Space, which is a platform that can help organizations to overcome obstacles to data sharing in a coopetitive ecosystem, is used to explain this approach.

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On Warehouses, Lakes, and Spaces: The Changing Role of Conceptual Modeling for Data Integration

Cited by 19 publications

References 44 publications

A Computer Science Perspective on Digital Transformation in Production

A Computer Science Perspective on Digital Transformation in Production

Data lake concept and systems: a survey

A Modeling Approach for Getting to Win-Win in Industrial Collaboration under Strategic Coopetition

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