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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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11#
發(fā)表于 2025-3-23 10:37:54 | 只看該作者
1865-1348 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 themselve
12#
發(fā)表于 2025-3-23 17:32:06 | 只看該作者
https://doi.org/10.1007/978-3-031-30804-8nges are discussed. Along with the process of text analytics, examples are presented to demonstrate how text analytics should be carried out. Finally, potential applications of text analytics are given including sentiment analysis and automatic generation of content.
13#
發(fā)表于 2025-3-23 19:10:39 | 只看該作者
14#
發(fā)表于 2025-3-23 23:12:57 | 只看該作者
https://doi.org/10.1007/978-3-031-30804-8work presented here is an extension of previous work on computing accurate travel time from GPS data. In this paper, the logical data model is explained in more details and the result section showing weather’s impact on travel time has been significantly extended with previously unpublished material.
15#
發(fā)表于 2025-3-24 04:07:03 | 只看該作者
16#
發(fā)表于 2025-3-24 09:21:35 | 只看該作者
17#
發(fā)表于 2025-3-24 12:58:48 | 只看該作者
Laplacian Matrix for Dimensionality Reduction and Clustering, clustering on the original samples. In these lecture notes we first introduce the Laplacian matrix and then present a small number of algorithms designed around it for data visualization and feature extraction.
18#
發(fā)表于 2025-3-24 16:54:23 | 只看該作者
19#
發(fā)表于 2025-3-24 20:06:13 | 只看該作者
20#
發(fā)表于 2025-3-25 00:14:37 | 只看該作者
https://doi.org/10.1007/978-3-031-30804-8 a very accessible way the main techniques and feedback of the conformance checking field. Moreover, in order to make it actionable, code snippets are provided so that an organization can start a conformance checking project on its own data.
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