Advancing a Gateway Infrastructure for Wind Turbine Data Analysis

Aguilera, Alvaro; Grunzke, Richard; Habich, Dirk; Luong, Johannes; Schollbach, Dirk; Markwardt, Ulf; Garcke, Jochen

doi:10.1007/s10723-016-9376-9

Cited by 6 publications

(7 citation statements)

References 30 publications

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“…Based on the set of given features and their occurrences in the respective use cases, no. 17 [54], 19 [44], 21 [66], 22 [67], 25 [58] and 30 [59] were automatically assigned to the third cluster. Again, a thorough investigation and comparison was made to identify conspicuous similarities.…”

Section: ) Description Of Clustermentioning

confidence: 99%

“…However, this cluster contains three further cases that have not been considered yet [54,58,67]. One of them represents a system for the remote 24/7 patient monitoring, which can be also used to determine the future medication procedure [54].…”

Section: ) Description Of Clustermentioning

confidence: 99%

“…In particular, real-time and batch processing on differently structured data is performed. The last case study makes use of high-performance computing (HPC) to analyze and optimize the operation of wind turbines [67]. This one also deviates from the smart city concept, represented in the first three cases.…”

Section: ) Description Of Clustermentioning

confidence: 99%

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Identifying Similarities of Big Data Projects–A Use Case Driven Approach

et al. 2020

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Big data is considered as one of the most promising technological advancements in the last decades. Today it is used for a multitude of data intensive projects in various domains and also serves as the technical foundation for other recent trends in the computer science domain. However, the complexity of its implementation and utilization renders its adoption a sophisticated endeavor. For this reason, it is not surprising that potential users are often overwhelmed and tend to rely on existing guidelines and best practices to successfully realize and monitor their projects. A valuable source of knowledge are use case descriptions, of which a multitude exists, each of them with a varying information density. In this design science research endeavor, 43 use cases are identified by conducting a thorough literature review in combination with the application and adaption of a corresponding template for big data projects. By a subsequent categorization, which is performed by identifying and employing a hierarchical clustering algorithm, nine different standard use cases emerge, as the contribution's artifact. This provides decision-makers with an initial entry point, which can be utilized to shape their project ideas, not only by identifying the general meaningfulness of their potential big data project but also in terms of concrete implementation details.

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Section: ) Description Of Clustermentioning

confidence: 99%

Section: ) Description Of Clustermentioning

confidence: 99%

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Identifying Similarities of Big Data Projects–A Use Case Driven Approach

et al. 2020

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“…Here, the development and operation of safety-critical systems implies an implicit need of managing data, information and knowledge that is generated by the different engineering teams. In this light, it is necessary to emphasize the research advances in Aerospace [105,137,138,150] and Automotive [64,65,72,89,139] engineering. In both cases, the "one-size fits all" is again not true.…”

Section: General Application To Engineeringmentioning

confidence: 99%

Survey of Scientific Programming Techniques for the Management of Data-Intensive Engineering Environments

Álvarez-Rodríguez

Alor-Hernández

Mejia-Miranda

2018

Scientific Programming

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The present paper introduces and reviews existing technology and research works in the field of scientific programming methods and techniques in data-intensive engineering environments. More specifically, this survey aims to collect those relevant approaches that have faced the challenge of delivering more advanced and intelligent methods taking advantage of the existing large datasets. Although existing tools and techniques have demonstrated their ability to manage complex engineering processes for the development and operation of safety-critical systems, there is an emerging need to know how existing computational science methods will behave to manage large amounts of data. That is why, authors review both existing open issues in the context of engineering with special focus on scientific programming techniques and hybrid approaches. 1193 journal papers have been found as the representative in these areas screening 935 to finally make a full review of 122. Afterwards, a comprehensive mapping between techniques and engineering and nonengineering domains has been conducted to classify and perform a meta-analysis of the current state of the art. As the main result of this work, a set of 10 challenges for future data-intensive engineering environments have been outlined.

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“…In their submission "Advancing a Gateway Infrastructure for Wind Turbine Data Analysis" [1], the authors describe the need for a data gateway (VAVID) to monitor and process sensor information from wind turbines. Numerical simulation and sensor measurement combine and activities include development of improved techniques for data compression, new methods for data management and interactive data visualization.…”

mentioning

confidence: 99%