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Titlebook: Big Data Management and Analytics; 9th European Summer Ralf-Detlef Kutsche,Esteban Zimányi Conference proceedings 2020 Springer Nature Swi

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發(fā)表于 2025-3-21 16:53:01 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱Big Data Management and Analytics
期刊簡稱9th European Summer
影響因子2023Ralf-Detlef Kutsche,Esteban Zimányi
視頻videohttp://file.papertrans.cn/186/185652/185652.mp4
學(xué)科分類Lecture Notes in Business Information Processing
圖書封面Titlebook: Big Data Management and Analytics; 9th European Summer  Ralf-Detlef Kutsche,Esteban Zimányi Conference proceedings 2020 Springer Nature Swi
影響因子This book constitutes 5 revised tutorial lectures of the 9th European Business Intelligence and Big Data Summer School, eBISS 2019, held in Berlin, Germany, during June 30 – July 5, 2019.. The tutorials were given by renowned experts and covered advanced aspects of business intelligence and big data. This summer school, presented by leading researchers in the field, represented an opportunity for postgraduate students to equip themselves with the theoretical and practical skills necessary for developing challenging business intelligence applications..
Pindex Conference proceedings 2020
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沙發(fā)
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978-3-030-61626-7Springer Nature Switzerland AG 2020
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https://doi.org/10.1007/978-3-031-30804-8mendation systems, user behavior analytics). In practice, they are playing a crucial role in harnessing the power of massive amounts of data which we are currently producing every day in our digital world. In general, the process of building a high-quality machine learning model is an iterative, com
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https://doi.org/10.1007/978-3-031-30804-8y few minutes to data being collected every second. With large quantities of GPS data available it is possible to analyze the traffic on most of the road network without installing road-side equipment..A very important key performance indicator (KPI) in traffic planning is travel time. For this reas
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https://doi.org/10.1007/978-3-031-30804-8p between samples in terms of similarity, temporal proximity, or label information. Graphs can in turn be represented by matrices. A special example is the Laplacian matrix, which allows us to assign each node a value that varies only little between strongly connected nodes and more between distant
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Conference proceedings 2020rmany, during June 30 – July 5, 2019.. The tutorials were given by renowned experts and covered advanced aspects of business intelligence and big data. This summer school, presented by leading researchers in the field, represented an opportunity for postgraduate students to equip themselves with the
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