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

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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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Collected

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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anarchist

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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

Asperity

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