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Titlebook: Databases Theory and Applications; 29th Australasian Da Junhu Wang,Gao Cong,Jianzhong Qi Conference proceedings 2018 Springer International

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發(fā)表于 2025-3-21 16:59:41 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Databases Theory and Applications
副標題29th Australasian Da
編輯Junhu Wang,Gao Cong,Jianzhong Qi
視頻videohttp://file.papertrans.cn/264/263528/263528.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Databases Theory and Applications; 29th Australasian Da Junhu Wang,Gao Cong,Jianzhong Qi Conference proceedings 2018 Springer International
描述 .This book constitutes the refereedproceedings of the 29th Australasian Database Conference, ADC 2018,held in Gold Coast, QLD, Australia, in May 2018..The 23 full papers plus 6 short papers presented together with 3 demo papers were carefully reviewed and selected from 53 submissions.?The Australasian Database Conference is an annual international forum for sharing the latest research advancements and novel applications of database systems, data-driven applications, and data analytics between researchers and practitioners from around the globe, particularly Australia and New Zealand..
出版日期Conference proceedings 2018
關(guān)鍵詞Data warehousing; Parallel databases; Cloud databases; Distributed databases; Database integration; Datab
版次1
doihttps://doi.org/10.1007/978-3-319-92013-9
isbn_softcover978-3-319-92012-2
isbn_ebook978-3-319-92013-9Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer International Publishing AG, part of Springer Nature 2018
The information of publication is updating

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Using SIMD Instructions to Accelerate Sequence Similarity Searches Inside a Database Systemlso compare it to implementations of the same logic outside the DBMS..Our implementation of an SIMD-accelerated Smith Waterman sequence-alignment algorithm shows an order of magnitude improvement on a non-accelerated version while running inside a DBMS. Our SIMD accelerated version also performs wit
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Interdependent Model for Point-of-Interest Recommendation via Social Networksf locations via links) between locations, based on the TF-IDF conversion of check-in times. Geographic information, such as the longitude and latitude of locations, are incorporated into the interdependent model. Substantial experiments on three social network data sets verify the POI recommendation
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Child Abuse and Domestic Abuse: Content and Feature Analysis from Social Media Disclosurests, with the underlying context. Various features such as psycholinguistic, textual and sentimental features are analyzed and Machine Learning techniques are trained to analyze the predictive power of extracted features. Hence, the resulting model achieves more predictive power with high accuracy in
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An Efficient Framework for the Analysis of Big Brain Signals Datae of brain signals data and also can be used for handling any other biomedical signal data in time series form (e.g. electrocardiogram (ECG); Electromyogram (EMG)). The main benefit of the proposed methodology is to provide an effective way for analyzing the vast amount of BBSD generated from the br
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https://doi.org/10.1007/978-3-642-45622-0an approach, which we call ., that adopts Google’s Auto Awesome philosophy: automatically improve an existing artifact and let the user (developer) decide whether to keep and use the improved artifact. The DBAutoAwesome approach ingests a database application to produce an enhanced application. In t
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