Titlebook: Web Technologies and Applications; 18th Asia-Pacific We Feifei Li,Kyuseok Shim,Guanfeng Liu Conference proceedings 2016 Springer Internatio

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0302-9743 nce APWeb 2016 held in Suzhou, China, in September 2016..The 79 full papers and presented together with 24 short papersand 17 demo papers were carefully reviewed and selectedfrom 215 submissions..the focus of the conference was on following subjects: .Spatio-temporal, Textual and Multimedia Data Man

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Conference proceedings 2016s..the focus of the conference was on following subjects: .Spatio-temporal, Textual and Multimedia Data Management.Social Media Data Analysis.Modelling and Learning with Big Data.Streaming and Real-time Data Analysis.Recommendation System.Data Quality and Privacy.Query Optimization and Scalable Data Processing.

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NERank: Bringing Order to Named Entities from Textsanked directly based on their relative importance, in order to support entity-oriented Web applications. In this paper, we introduce an entity ranking algorithm NERank to address this issue. NERank first constructs a graph model called Topical Tripartite Graph from a document collection. A ranking f

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FTS: A Practical Model for Feature-Based Trajectory Synthesissignificant attention during the past decade. Hence, large trajectory datasets are extremely necessary to test high performance algorithms for these applications and researches. However, real-world datasets are not accessible in many cases due to privacy concerns and business competition. For this r

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Probabilistic Nearest Neighbor Query in Traffic-Aware Spatial Networksic nearest neighbors and planning the corresponding travel routes in traffic-aware spatial networks (TANN queries) to avoid traffic congestions. We propose and study two probabilistic TANN queries: (1) a time-threshold query like “what is my closest restaurant with the minimum congestion probability

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Mining Co-locations from Continuously Distributed Uncertain Spatial Data focuses on discovering co-location patterns from deterministic spatial data sets, in this paper, we study the problem in the context of continuously distributed uncertain data. In particular, we aim to discover co-location patterns from uncertain spatial data where locations of spatial instances ar