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Titlebook: Database and Expert Systems Applications; 29th International C Sven Hartmann,Hui Ma,Roland R. Wagner Conference proceedings 2018 Springer N

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發(fā)表于 2025-3-21 18:15:20 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱Database and Expert Systems Applications
副標(biāo)題29th International C
編輯Sven Hartmann,Hui Ma,Roland R. Wagner
視頻videohttp://file.papertrans.cn/264/263465/263465.mp4
叢書(shū)名稱Lecture Notes in Computer Science
圖書(shū)封面Titlebook: Database and Expert Systems Applications; 29th International C Sven Hartmann,Hui Ma,Roland R. Wagner Conference proceedings 2018 Springer N
描述.This two volume set of LNCS 11029 and LNCS 11030 constitutes the refereed proceedings of the 29th International Conference on Database and Expert Systems Applications, DEXA 2018, held in Regensburg, Germany, in September 2018..The 35 revised full papers presented together with 40 short papers were carefully reviewed and selected from 160 submissions. The papers of the first volume discuss a range of topics including: Big data analytics; data integrity and privacy; decision support systems; data semantics; cloud data processing; time series data; social networks; temporal and spatial databases; and graph data and road networks. The papers of the second volume discuss a range of the following topics: Information retrieval; uncertain information; data warehouses and recommender systems; data streams; information networks and algorithms; database system architecture and performance; novel database solutions; graph querying and databases; learning; emerging applications;data mining; privacy; and text processing.
出版日期Conference proceedings 2018
關(guān)鍵詞big data analytics; cloud data; clustering algorithms; computer networks; data mining; data privacy; datab
版次1
doihttps://doi.org/10.1007/978-3-319-98809-2
isbn_softcover978-3-319-98808-5
isbn_ebook978-3-319-98809-2Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer Nature Switzerland AG 2018
The information of publication is updating

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Continuum Analysis of Biological Systemsterference variables into state space models. In addition, to realize the trend shift model efficiently, we propose an effective trend detection method, TP-TBSM (two-phase TBSM), by extending TBSM (trend-based segmentation method). The experimental results validate the proposed model and method.
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https://doi.org/10.1007/978-3-540-74298-2ata centers..We implemented BuckTop and compared its performance for processing top-k queries over encrypted data with that of the popular threshold algorithm (TA) over original (plaintext) data. The results show the effectiveness of BuckTop for outsourcing sensitive data in the cloud and answering top-k queries.
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ScaleSCAN: Scalable Density-Based Graph Clusterings as those of SCAN with much shorter computation time. Extensive experiments on both real-world and synthetic graphs demonstrate that the performance superiority of ScaleSCAN over the state-of-the-art methods.
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Answering Top-k Queries over Outsourced Sensitive Data in the Cloudata centers..We implemented BuckTop and compared its performance for processing top-k queries over encrypted data with that of the popular threshold algorithm (TA) over original (plaintext) data. The results show the effectiveness of BuckTop for outsourcing sensitive data in the cloud and answering top-k queries.
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Stochastic Modelling and Applied Probabilityee different sequence related approaches: process mining, dependency graph and sequential pattern mining. Then, we evaluate the impact of the recommender system. The result shows that all can improve the performance of students while the approach based on dependency graph contributes most.
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https://doi.org/10.1007/978-3-540-74298-2real-life setting. Our approach combines the trust, determined by the reputation of the provider, and the QoS. We present different algorithms for processing such selection queries and evaluate them through a set of experiments.
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