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Titlebook: Databases Theory and Applications; 31st Australasian Da Renata Borovica-Gajic,Jianzhong Qi,Weiqing Wang Conference proceedings 2020 Springe

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A Survey on Map-Matching Algorithmstwo decades and, driven by the emerging applications, is still under development. There is a lack of categorisation of existing solutions recently and analysis for future research directions. In this paper, we review the current status of the map-matching problem and survey the existing algorithms.
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Gaussian Embedding of Large-Scale Attributed Graphsf graph analysis tasks including link prediction, node classification, recommendation and visualization. Most existing approaches represent graph nodes as point vectors in a low-dimensional embedding space, ignoring the uncertainty present in the real-world graphs. Furthermore, many real-world graph
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Effective and Efficient Community Search in Directed Graphs Across Heterogeneous Social Networksn retrieving communities online. Although certain effort has been put into community search, users’ information has not been well exploited for effective search. Meanwhile, existing approaches for retrieval of communities are not efficient when applied in huge social networks. Motivated by this, in
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Entity Extraction with Knowledge from Web Scale Corporags from free text against a dictionary of entities. In this paper, we present several techniques as a post-processing step for improving the effectiveness of the existing entity extraction technique. These techniques utilise models trained with the web-scale corpora which makes our techniques robust
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Graph-Based Relation-Aware Representation Learning for Clothing Matchingto infer complex fashion compatibility patterns from a large number of fashion items. Previous work mainly utilises metric learning techniques to model the compatibility relationships, such that compatible items are closer to each other than incompatible ones in the latent space. However, they ignor
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Chantal J. Snoeck,Siamak Zoharipied and non-occupied moving objects. Accordingly, we propose an efficient grid-based expand-and-bound algorithm for the approachable .NN search and conduct extensive experiments on real-world data. The results demonstrate the effectiveness and efficiency of our proposed solutions.
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https://doi.org/10.1007/978-1-0716-0900-2ing a multivariate gaussian distribution, and reduces backup data if we can recover such data from the partial backup data and the probabilistic model. In this paper, we focus on average, sum, max, and min queries and propose a greedy-based backup selection algorithm. We evaluate the validity and ef
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