標(biāo)題: Titlebook: Business Intelligence and Big Data; 7th European Summer Esteban Zimányi Conference proceedings 2018 Springer Nature Switzerland AG 2018 bu [打印本頁] 作者: choleric 時(shí)間: 2025-3-21 18:19
書目名稱Business Intelligence and Big Data影響因子(影響力)
書目名稱Business Intelligence and Big Data影響因子(影響力)學(xué)科排名
書目名稱Business Intelligence and Big Data網(wǎng)絡(luò)公開度
書目名稱Business Intelligence and Big Data網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Business Intelligence and Big Data被引頻次
書目名稱Business Intelligence and Big Data被引頻次學(xué)科排名
書目名稱Business Intelligence and Big Data年度引用
書目名稱Business Intelligence and Big Data年度引用學(xué)科排名
書目名稱Business Intelligence and Big Data讀者反饋
書目名稱Business Intelligence and Big Data讀者反饋學(xué)科排名
作者: triptans 時(shí)間: 2025-3-21 23:03 作者: pancreas 時(shí)間: 2025-3-22 01:12 作者: 格言 時(shí)間: 2025-3-22 05:57
Pluralism of Media Types and Media Genresf-the-art data profiling systems and techniques. In particular, we discuss hard problems in data profiling, such as algorithms for dependency discovery and their application in data management and data analytics. We conclude with directions for future research in the area of data profiling.作者: Mundane 時(shí)間: 2025-3-22 09:46 作者: certain 時(shí)間: 2025-3-22 14:23
An Introduction to Data Profiling,f-the-art data profiling systems and techniques. In particular, we discuss hard problems in data profiling, such as algorithms for dependency discovery and their application in data management and data analytics. We conclude with directions for future research in the area of data profiling.作者: endarterectomy 時(shí)間: 2025-3-22 20:41 作者: 陶瓷 時(shí)間: 2025-3-23 01:12 作者: 包庇 時(shí)間: 2025-3-23 02:28
Henrik S?ndergaard,Rasmus Helleshe current snapshot of the graph. In this chapter, we present logical and physical models, query types, systems and algorithms for managing historical graphs. We also highlight promising directions for future work.作者: angina-pectoris 時(shí)間: 2025-3-23 07:30 作者: archaeology 時(shí)間: 2025-3-23 12:08
,Let’s Open the Black Box of Deep Learning!, what are the real mechanisms that make this technique a breakthrough with respect to the past. To this end, we will review what is a neural network, how we can learn its parameters by using observational data, some of the most common architectures (CNN, LSTM, etc.) and some of the tricks that have been developed during the last years.作者: 宏偉 時(shí)間: 2025-3-23 14:45
Pluralism of Media Types and Media Genrestadata discovery is known as data profiling. Profiling activities range from ad-hoc approaches, such as eye-balling random subsets of the data or formulating aggregation queries, to systematic inference of metadata via profiling algorithms. In this course, we will discuss the importance of data prof作者: 并置 時(shí)間: 2025-3-23 20:39 作者: JEER 時(shí)間: 2025-3-24 01:37 作者: Redundant 時(shí)間: 2025-3-24 05:38 作者: FLEET 時(shí)間: 2025-3-24 09:08
Yolande Stolte,Rachael Craufurd Smitht of techniques for handling and processing such streams of data is very challenging as the streaming context imposes severe constraints on the computation: we are often not able to store the whole data stream and making multiple passes over the data is no longer possible. As the stream is never fin作者: Vaginismus 時(shí)間: 2025-3-24 11:10
Henrik S?ndergaard,Rasmus Helles what are the real mechanisms that make this technique a breakthrough with respect to the past. To this end, we will review what is a neural network, how we can learn its parameters by using observational data, some of the most common architectures (CNN, LSTM, etc.) and some of the tricks that have 作者: esculent 時(shí)間: 2025-3-24 18:46 作者: appall 時(shí)間: 2025-3-24 19:34
Business Intelligence and Big Data978-3-319-96655-7Series ISSN 1865-1348 Series E-ISSN 1865-1356 作者: 諂媚于人 時(shí)間: 2025-3-25 02:45
Henrik S?ndergaard,Rasmus Helles what are the real mechanisms that make this technique a breakthrough with respect to the past. To this end, we will review what is a neural network, how we can learn its parameters by using observational data, some of the most common architectures (CNN, LSTM, etc.) and some of the tricks that have been developed during the last years.作者: 愉快嗎 時(shí)間: 2025-3-25 06:59 作者: 招人嫉妒 時(shí)間: 2025-3-25 11:02
978-3-319-96654-0Springer Nature Switzerland AG 2018作者: incarcerate 時(shí)間: 2025-3-25 11:51
,Temporal Data Management – An Overview,erspective, we provide an overview of basic temporal database concepts. Then we survey the state-of-the-art in temporal database research, followed by a coverage of the support for temporal data in the current SQL standard and the extent to which the temporal aspects of the standard are supported by作者: Fresco 時(shí)間: 2025-3-25 18:52
,Three Big Data Tools for a Data Scientist’s Toolbox, in every big data scientists’ toolbox, including approximate frequency counting of frequent items, cardinality estimation of very large sets, and fast nearest neighbor search in huge data collections.作者: 文件夾 時(shí)間: 2025-3-25 20:19
Sebastian Müller,Christoph Gusyerspective, we provide an overview of basic temporal database concepts. Then we survey the state-of-the-art in temporal database research, followed by a coverage of the support for temporal data in the current SQL standard and the extent to which the temporal aspects of the standard are supported by作者: Allure 時(shí)間: 2025-3-26 03:09
Yolande Stolte,Rachael Craufurd Smith in every big data scientists’ toolbox, including approximate frequency counting of frequent items, cardinality estimation of very large sets, and fast nearest neighbor search in huge data collections.作者: Prosaic 時(shí)間: 2025-3-26 04:35
An Introduction to Data Profiling,tadata discovery is known as data profiling. Profiling activities range from ad-hoc approaches, such as eye-balling random subsets of the data or formulating aggregation queries, to systematic inference of metadata via profiling algorithms. In this course, we will discuss the importance of data prof作者: Geyser 時(shí)間: 2025-3-26 08:27 作者: 愚笨 時(shí)間: 2025-3-26 14:35 作者: 苦惱 時(shí)間: 2025-3-26 17:55
Historical Graphs: Models, Storage, Processing,t corresponds to the state of the graph at the corresponding time instant. There is rich information in the history of the graph not present in just the current snapshot of the graph. In this chapter, we present logical and physical models, query types, systems and algorithms for managing historical作者: 合適 時(shí)間: 2025-3-27 00:50 作者: spinal-stenosis 時(shí)間: 2025-3-27 02:10 作者: creditor 時(shí)間: 2025-3-27 07:26
9樓作者: FIN 時(shí)間: 2025-3-27 09:31
9樓作者: SMART 時(shí)間: 2025-3-27 15:36
9樓作者: Anterior 時(shí)間: 2025-3-27 21:06
10樓作者: adj憂郁的 時(shí)間: 2025-3-28 00:55
10樓作者: Airtight 時(shí)間: 2025-3-28 05:28
10樓作者: 使饑餓 時(shí)間: 2025-3-28 07:50
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