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First Acm Sigmm International Workshop On Social Media (Wsm'09), Suzanne Boll, Steven C. H. Hoi, Jiebo Luo, Rong Jin, Dong Xu, Irwin King
First Acm Sigmm International Workshop On Social Media (Wsm'09), Suzanne Boll, Steven C. H. Hoi, Jiebo Luo, Rong Jin, Dong Xu, Irwin King
Research Collection School Of Computing and Information Systems
The ACM SIGMM International Workshop on Social Media(WSM’09) is the first workshop held in conjunction withthe ACM International Multimedia Conference (MM’09) atBejing, P.R. China, 2009. This workshop provides a forumfor researchers and practitioners from all over the world toshare information on their latest investigations on social mediaanalysis, exploration, search, mining, and emerging newsocial media applications.
First Acm Sigmm International Workshop On Social Media (Wsm'09), Suzanne Boll, Steven C. H. Hoi, Jiebo Luo, Rong Jin, Dong Xu, Irwin King
First Acm Sigmm International Workshop On Social Media (Wsm'09), Suzanne Boll, Steven C. H. Hoi, Jiebo Luo, Rong Jin, Dong Xu, Irwin King
Research Collection School Of Computing and Information Systems
No abstract provided.
Fast Object Search On Road Networks, Ken C. K. Lee, Wang-Chien Lee, Baihua Zheng
Fast Object Search On Road Networks, Ken C. K. Lee, Wang-Chien Lee, Baihua Zheng
Research Collection School Of Computing and Information Systems
In this paper, we present ROAD, a general framework to evaluate Location-Dependent Spatial Queries (LDSQ)s that searches for spatial objects on road networks. By exploiting search space pruning technique and providing a dynamic object mapping mechanism, ROAD is very efficient and flexible for various types of queries, namely, range search and nearest neighbor search, on objects over large-scale networks. ROAD is named after its two components, namely, Route Overlay and Association Directory, designed to address the network traversal and object access aspects of the framework. In ROAD, a large road network is organized as a hierarchy of interconnected regional sub-networks …
Computing Medoids In Large Spatial Datasets, Kyriakos Mouratidis, Dimitris Papadias, Spiros Papadimitriou
Computing Medoids In Large Spatial Datasets, Kyriakos Mouratidis, Dimitris Papadias, Spiros Papadimitriou
Research Collection School Of Computing and Information Systems
In this chapter, we consider a class of queries that arise in spatial decision making and resource allocation applications. Assume that a company wants to open a number of warehouses in a city. Let P be the set of residential blocks in the city. P represents customer locations to be potentially served by the company. At the same time, P also comprises the candidate warehouse locations because the warehouses themselves must be opened in some residential blocks.