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Titlebook: Big Data Analytics and Knowledge Discovery; 21st International C Carlos Ordonez,Il-Yeol Song,Ismail Khalil Conference proceedings 2019 Spri

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31#
發(fā)表于 2025-3-26 23:36:30 | 只看該作者
Introducing .NET for Apache Spark. We introduce a number of current optimizations and extensions to the state-of-the-art. Furthermore, this paper presents the first comparative benchmark between XRT and many popular MapReduce runtimes in order to highlight the performance capabilities of programming-language independent data processing.
32#
發(fā)表于 2025-3-27 02:48:44 | 只看該作者
Improved Programming-Language Independent MapReduce on Shared-Memory Systems. We introduce a number of current optimizations and extensions to the state-of-the-art. Furthermore, this paper presents the first comparative benchmark between XRT and many popular MapReduce runtimes in order to highlight the performance capabilities of programming-language independent data processing.
33#
發(fā)表于 2025-3-27 09:18:30 | 只看該作者
34#
發(fā)表于 2025-3-27 10:06:44 | 只看該作者
35#
發(fā)表于 2025-3-27 15:43:07 | 只看該作者
Gianluca Baldassarre,Marco Mirolliroach to anomaly detection for early detection of faults for a condition-based maintenance. For the purpose of this study, a belt-driven single degree of freedom robot arm is designed. The robot arm is conditioned on the torque required to move the arm forward and backward, simulating a door opening
36#
發(fā)表于 2025-3-27 20:51:03 | 只看該作者
37#
發(fā)表于 2025-3-27 23:05:41 | 只看該作者
,Schalenf?rmige Hybridverbunde und Inserts,ch, which in turn has led to .. In urban research, researchers who conduct paper-based or telephone-based travel surveys often collect biased and inaccurate data about movements of their participants. Although the use of global positioning system (GPS) trackers in travel studies improves the accurac
38#
發(fā)表于 2025-3-28 02:57:57 | 只看該作者
https://doi.org/10.1007/978-3-662-68075-9costly. How can we quickly discover all the frequent items that are favored individually by at least a given number of users? This new problem not only has strong connections with several well-known problems, such as the frequent item mining problem, it also finds applications in fields such as spon
39#
發(fā)表于 2025-3-28 06:59:21 | 只看該作者
https://doi.org/10.1007/978-1-4302-2456-3in its user-item matrix input, to make it more informative before collaborative filtering. Existing recommendation systems that attempt to use mining and some sequences are those referred to as LiuRec09, ChoiRec12, SuChenRec15, and HPCRec18. These systems use mining based techniques of clustering, f
40#
發(fā)表于 2025-3-28 13:41:32 | 只看該作者
Windows Communication Foundation,ta. In sequential pattern mining, patterns are selected based on criteria such as the occurrence frequency, periodicity, or utility (eg. profit). Although this has many applications, it does not consider the effort or resources consumed to apply these patterns. To address this issue, this paper prop
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