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The rapid expansion of broadband mobile networks by Telecom Operators, has introduced a versatile global infrastructure that internally generates vast amounts of spatio-temporal network-level data (e.g., User id, location, device type, etc.) At the same time, mobile app vendors have nowadays at their fingertips massive amounts of app-level data collected through implicit or explicit crowd sourcing...
With the proliferation of mobile devices and explosive growth of spatio-temporal data, Location-Based Services (LBS) have become an indispensable technology in our daily lives. The key characteristics of the LBS applications include a high rate of time-stamped location updates, and many concurrent historical, present and predictive queries. The commercial providers of LBS must support all three kinds...
The efficiency of spatial query processing is crucial for many applications such as location-based services. In spatial networks, queries like k-NN queries are all based on network distance evaluation. Classic solutions for these queries rely on network expansion and are not efficient enough for large networks. Some approaches have improved the query efficiency but brought considerable space cost...
We consider the problem of finding the k nearest points of interest from a given location in time-dependent road networks, i.e., One where travel time along each edge is a function of the departure time, and where the operating times of the points of interest are also taken into consideration. More specifically, we address the following query: find the k points of interest in which a user can start...
With the advances of mobile communication techniques in recent years, numerous kinds of Location-Based Services (LBSs) have been developed and one popular application of LBSs is trip recommendation. Although there exist already a number of studies on this topic in literatures, most of them focused on combining a set of point-of-interests (POIs, or say attractions) as a trip based on user-specific...
Accurate information about how people commonly travel in a given large-scale building environment and which routes they take for given start and destination points is essential for applications such as indoor navigation, route prediction, and mobile work planning and logistics. In this paper, we propose methods for detecting commonly used routes by robust aggregation, clustering, and merging of indoor...
With the rapid development of GPS-enabled mobile devices, huge amounts of user-contributed data with location information can be collected from the Internet. With this kind of data, one promising application is travel recommendation, which has attracted a considerable number of researches recently. However, most of the previous studies only focus on one aspect of the relations among users and locations...
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