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Titlebook: Database Systems for Advanced Applications; 21st International C Shamkant B. Navathe,Weili Wu,Hui Xiong Conference proceedings 2016 Springe

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https://doi.org/10.1007/978-1-349-86105-7among different semantic labels of locations, and enables more meaningful measures of semantic similarity among indoor trajectories. We conduct experiments on indoor trajectories, comparing our proposal with several popular methods. The results suggest that our proposal is effective and represents an improvement over existing methods.
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STH-Bass: A Spatial-Temporal Heterogeneous Bass Model to Predict Single-Tweet Popularitycuracy of STH-Bass based on real-world Twitter data. The evaluation results show that STH-Bass obtains much less APE than the baselines when predicting the trend of a single tweet, and an average of 24 % higher . when classifying the tweets popularity.
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Pre-computed Region Guardian Sets Based Reverse kNN Queriesat the results of a query . can be computed by using SLICE on only the objects in its guardian set instead of using the whole dataset. Our comprehensive experimental study on synthetic and real datasets demonstrates the proposed approach is the most efficient algorithm for R.NN.
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Effective Similarity Search on Indoor Moving-Object Trajectoriesamong different semantic labels of locations, and enables more meaningful measures of semantic similarity among indoor trajectories. We conduct experiments on indoor trajectories, comparing our proposal with several popular methods. The results suggest that our proposal is effective and represents an improvement over existing methods.
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Towards Neighborhood Window Analytics over Large-Scale Graphsover both real and synthetic datasets with hundreds of millions of vertices and edges show that our proposed solutions are four orders of magnitude faster in query performance than the non-index algorithm, and are superior over the state-of-the-art solution in terms of both scalability and efficiency.
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