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In a mobile service scenario, users query a server for nearby points of interest but they may not want to disclose their locations to the service. Intuitively, location privacy may be obtained at the cost of query performance and query accuracy. The challenge addressed is how to obtain the best possible performance, subjected to given requirements for(More)
Skyline queries are well suited when retrieving data according to multiple criteria. While most previous work has assumed a centralized setting this paper considers skyline querying in a mobile and distributed setting, where each mobile device is capable of holding only a portion of the whole dataset; where devices communicate through mobile ad hoc(More)
Location privacy in mobile services has the potential to become a serious concern for service providers and users. Existing privacy protection techniques that use <i>k</i>-anonymity convert an original query into an anonymous query that contains the locations of multiple users. Such techniques, however, generally fail in offering guaranteed large privacy(More)
Indoor spaces accommodate large populations of individuals. With appropriate indoor positioning, e.g., Bluetooth and RFID, in place, large amounts of trajectory data result that may serve as a foundation for a wide variety of applications, e.g., space planning, way finding, and security. This scenario calls for the indexing of indoor trajectories. Based on(More)
The tracking of the locations of moving objects in large indoor spaces is important, as it enables a range of applications related to, e.g., security and indoor navigation and guidance. This paper presents a graph model based approach to indoor tracking that offers a uniform data management infrastructure for different symbolic positioning technologies,(More)
Skyline queries are capable of retrieving interesting points from a large data set according to multiple criteria. Most work on skyline queries so far has assumed a centralized storage, whereas in practice relevant data are often distributed among geographically scattered sites. In this work, we tackle constrained skyline queries in large-scale distributed(More)
An interesting problem in peer-based data management is efficient support for skyline queries within a multiattribute space. A skyline query retrieves from a set of multidimensional data points a subset of interesting points, compared to which no other points are better. Skyline queries play an important role in multi-criteria decision making and user(More)
Skyline queries are useful for finding interesting tuples from a large data set according to multiple criteria. The sizes of data sets are constantly increasing and the architecture of back-ends are switching from single-node environments to non-conventional paradigms like MapReduce. Despite the usefulness of skyline queries, existing works on skyline(More)
Location-Based Services (LBSs) constitutes one of the most popular classes of mobile services. However, while current LBSs typically target outdoor settings, we lead large parts of our lives indoors. The availability of easy-to-use and low-cost indoor positioning services is essential in also enabling indoor LBSs. Existing indoor positioning services(More)
People spend a significant amount of time in indoor spaces (e.g., office buildings, subway systems, etc.) in their daily lives. Therefore, it is important to develop efficient indoor spatial query algorithms for supporting various location-based applications. However, indoor spaces differ from outdoor spaces because users have to follow the indoor floor(More)