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Titlebook: Databases Theory and Applications; 28th Australasian Da Zi Huang,Xiaokui Xiao,Xin Cao Conference proceedings 2017 Springer International Pu

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發(fā)表于 2025-3-21 18:19:37 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱(chēng)Databases Theory and Applications
副標(biāo)題28th Australasian Da
編輯Zi Huang,Xiaokui Xiao,Xin Cao
視頻videohttp://file.papertrans.cn/264/263526/263526.mp4
概述Includes supplementary material:
叢書(shū)名稱(chēng)Lecture Notes in Computer Science
圖書(shū)封面Titlebook: Databases Theory and Applications; 28th Australasian Da Zi Huang,Xiaokui Xiao,Xin Cao Conference proceedings 2017 Springer International Pu
描述.This book constitutes the refereed proceedings of the 28th Australasian Database Conference, ADC 2017, held in Brisbane, QLD, Australia, in September 2017. ..The 20 full papers presented together with 2 demo papers were carefully reviewed and selected from 32 submissions.?The mission of ADC is to share novel research solutions to problems of today’s information?society that fulfill the needs of heterogeneous applications and environments and to identify?new issues and directions for future research and development work. The topics of the presented papers are related to?all practical and theoretical aspects of advanced database theory and?applications, as well as case studies and implementation experiences..
出版日期Conference proceedings 2017
關(guān)鍵詞Artificial intelligence; Cloud, distributed, and parallel databases; Data mining and knowledge discove
版次1
doihttps://doi.org/10.1007/978-3-319-68155-9
isbn_softcover978-3-319-68154-2
isbn_ebook978-3-319-68155-9Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer International Publishing AG 2017
The information of publication is updating

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Naresh Polisetti,Nancy C. Joyceneighbour (NN), namely . and .. In this paper, we propose a new join query which is called . join query. Given two point datasets . and . of multidimensional objects, the . query retrieves for each point in . its all surrounding points in .. As a new spatial join query, we propose algorithms that ar
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Ashley M. Crane,Sanjoy K. Bhattacharyats. Differently than most existing works, we use the deviation from targeted pairwise correlation constraints as an objective to minimize in our problem. Moreover, we include users preferences as an objective in the form of maximizing similarity to users’ initial sub-intervals. The combination of th
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https://doi.org/10.1007/978-3-030-10696-6 Previous work such as [., ., .] has been proposed to answer the search. Such work typically measures the distance between trajectories and queries by the distance between query points and GPS points of trajectories. Such measurement could be inaccurate because those GPS points generated by some sam
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https://doi.org/10.1007/978-3-030-10696-6 and retrieval. Data-independent approaches (e.g., Locality Sensitive Hashing) normally construct hash functions using random projections, which neglect intrinsic data properties. To compensate this drawback, learning-based approaches propose to explore local data structure and/or supervised informa
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https://doi.org/10.1007/978-3-319-52264-7eed, semantic label independence. Among the existing solutions, graph hashing makes a significant contribution as it could effectively preserve the neighbourhood data similarities into binary codes via spectral analysis. However, existing graph hashing methods separate graph construction and hashing
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Vallendarer Schriften der Pflegewissenschaftal place for spreading rumors. Although different types of information are available in a post on social media, traditional approaches in rumor detection leverage only the text of the post, which limits their accuracy in detection. In this paper, we propose a provenance-aware approach based on recur
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