Automatic Analysis of Large Data Sets: A Walk-Through on Methods from Different Perspectives

Hilbrich, Marcus; Weber, Matthias; Tschüter, Ronny

doi:10.1109/cloudcom-asia.2013.47

Cited by 7 publications

(2 citation statements)

References 46 publications

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“…For data optimization support, automatic data analysis tools [19] (e.g. data mining, machine learning) will support the automated filtering, categorization, and analysis of massive and complex datasets that are manually unfeasible in big data digital lean factories.…”

Section: New Digital Tools For New Forms Of Gemba Walksmentioning

confidence: 99%

New Forms of Gemba Walks and Their Digital Tools in the Digital Lean Manufacturing World

Romero

Gaiardelli

Wuest

et al. 2020

IFIP Advances in Information and Communication Technology

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Gemba Walks are an important mean of vertical integration in Lean Manufacturing environments. They ensure that all levels of the company stay connected with the front-line, "the Gemba", where the actual value is created. However, traditionally Gemba Walks have been restricted to one location. This is a shortcoming in production environments characterized by interconnected and often globally dispersed problems where information from several locations is needed simultaneously. In response, this paper explores the emergence of new forms of Gemba Walks enabled by the adoption of new digital technologies. We intend to identify the advantages and disadvantages of using digital technology to support the execution of these new forms of Gemba Walks in more complex, globalized environments and to get a grasp of the extent to which digitalization changes communication characteristics between the parties involved.

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Section: New Digital Tools For New Forms Of Gemba Walksmentioning

confidence: 99%

New Forms of Gemba Walks and Their Digital Tools in the Digital Lean Manufacturing World

Romero

Gaiardelli

Wuest

et al. 2020

IFIP Advances in Information and Communication Technology

View full text Add to dashboard Cite

show abstract

“…The analysis of monitoring data is a common Big Data challenge. Therefore, we started by studying related work [14], where we identified a set of analysis techniques like genetic algorithms [6,7,17,29], machine learning [2,[19][20][21], sequence comparison [1,9,24,25], intrusion detection [5,26,27], and statistic of events [4,18,28]. The most promising technique is a similarity comparison [8,13].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Series of Measurements From Job-Centric Monitoring by Statistical Functions

Hilbrich

Frank

2017

csci

Self Cite

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The rising number of executed programs (jobs) enabled by the growing amount of available resources from Clouds, Grids, and HPC (for example) has resulted in an enormous number of jobs. Nowadays, most of the executed jobs are mainly unobserved, so unusual behavior, non-optimal resource usage, and silent faults are not systematically searched and analyzed. Job-centric monitoring enables permanent job observation and, thus, enables the analysis of monitoring data.In this paper, we show how statistic functions can be used to analyze job-centric monitoring data and how the methods compare to more-complex analysis methods. Additionally, we present the usefulness of job-centric monitoring based on practical experiences.

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Data Analytics Framework Based on Cloud Environment

Kanagaraj¹,

Geetha²

2021

Integration of Cloud Computing With Internet of Things

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Automatic Analysis of Large Data Sets: A Walk-Through on Methods from Different Perspectives

Cited by 7 publications

References 46 publications

New Forms of Gemba Walks and Their Digital Tools in the Digital Lean Manufacturing World

New Forms of Gemba Walks and Their Digital Tools in the Digital Lean Manufacturing World

Analysis of Series of Measurements From Job-Centric Monitoring by Statistical Functions

Data Analytics Framework Based on Cloud Environment

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