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Titlebook: Web and Big Data; 4th International Jo Xin Wang,Rui Zhang,Yang-Sae Moon Conference proceedings 2020 Springer Nature Switzerland AG 2020 art

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樓主: Capricious
31#
發(fā)表于 2025-3-27 00:13:04 | 只看該作者
32#
發(fā)表于 2025-3-27 04:47:14 | 只看該作者
Active Classification of Cold-Start Users in Large Sparse Datasetsg, a query is selected based on the current knowledge learned in these two online factorization models. We demonstrate with real-world movie rating datasets that our framework is highly effective. It not only gains better improvement in classification, but also reduces the number of invalid queries.
33#
發(fā)表于 2025-3-27 07:23:29 | 只看該作者
34#
發(fā)表于 2025-3-27 10:18:50 | 只看該作者
35#
發(fā)表于 2025-3-27 15:01:36 | 只看該作者
Partition-Oriented Subgraph Matching on GPUeal-world graphs, and further reduce the redundant global memory access caused by the redundant neighbor set accessing. Besides, to further improve the performance, we propose a well-directed filtering strategy by exploiting a property of real-world graphs. The experiments show that compared with th
36#
發(fā)表于 2025-3-27 17:45:48 | 只看該作者
Content Sharing Prediction for Device-to-Device (D2D)-based Offline Mobile Social Networks by Networand achieve more accurate predictions for both discovered and undiscovered relations in the D2D social network. Specifically, we consider the Global Positioning System (GPS) information as a critical relation slice to avoid the loss of potential information. Experiments on a realistic large-scale D2
37#
發(fā)表于 2025-3-28 01:21:52 | 只看該作者
38#
發(fā)表于 2025-3-28 02:57:55 | 只看該作者
Instance-Aware Evaluation of Sensitive Columns in Tabular Dataset relational schema varies. Moreover, our scheme can quantify the risks of the columns no matter the semantics of columns are known or not. We also empirically show that the proposed scheme is effective in dataset sensitivity governance comparing with baselines.
39#
發(fā)表于 2025-3-28 06:53:09 | 只看該作者
EPUR: An Efficient Parallel Update System over Large-Scale RDF Data, parallel update operations are developed to handle incremental RDF data. Based on the innovations above, we implement an efficient parallel update system (EPUR). Extensive experiments show that EPUR outperforms RDF-3X, Virtuoso, PostgreSQL and achieves good scalability on the number of threads.
40#
發(fā)表于 2025-3-28 13:11:53 | 只看該作者
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