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For additional information, see the DB TECH REPORTS homepage: www.cs.aau.dk/DBTR . Any software made available via DB TECH REPORTS is provided "as is" and without any express or implied warranties, including, without limitation, the implied warranty of merchantability and fitness for a particular purpose.The DB TECH REPORTS icon is made from two letters in an early version of the Rune alphabet, which was used by the Vikings, among others. Runes have angular shapes and lack horizontal lines because the primary storage medium was wood, although they may also be found on jewelry, tools, and weapons. Runes were perceived as having magic, hidden powers. The first letter in the logo is "Dagaz," the rune for day or daylight and the phonetic equivalent of "d." Its meanings include happiness, activity, and satisfaction. The second letter is "Berkano," which is associated with the birch tree. Its divinatory meanings include health, new beginnings, growth, plenty, and clearance. It is associated with Idun, goddess of Spring, and with fertility. It is the phonetic equivalent of "b." AbstractThe popularity of embedded positioning technologies in mobile devices and the development of mobile communication technology have paved the way for powerful location-based services (LBSs). To make LBSs useful and user-friendly, heavy use is made of context information, including patterns in user location data which are extracted by data mining methods. However, there is a potential conflict of interest: the data mining methods want as precise data as possible, while the users want to protect their privacy by not disclosing their exact movements. This paper aims to resolve this conflict by proposing a general framework that allows user location data to be anonymized, thus preserving privacy, while still allowing interesting patterns to be discovered. The framework allows users to specify individual desired levels of privacy that the data collection and mining system will then meet. Privacy-preserving methods are proposed for two core data mining tasks, namely finding dense spatiotemporal regions and finding frequent routes. An extensive set of experiments evaluate the methods, comparing them to their non-privacy-preserving equivalents. The experiments show that the framework still allows most patterns to be found, even when privacy is preserved.

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Density queries are of practical importance in many mobility related applications. In this paper, we employ the location sensors, which are placed in a geographical area and can only detect the amount of objects moving in vicinity, to estimate the dense regions of objects. Our approach partitions the region of interest into subregions, and deploys in each subregion both location sensors and a processing node that issues and answers density queries. Three algorithms are proposed to process density queries on those processing nodes. Their accuracy and efficiency are empirically evaluated.

Location-based services (LBSs) utilize consumer electronics, mobile communications, positioning technology, and traditional map information to provide mobile users with new kinds of online services. Examples include location-sensitive information services that identify points of interest that are in some sense nearest and of interest to their users and that offer travel directions to their users. Data management is a core aspect of the provisioning of LBSs. The diversity and complexity of LBSs demand novel and advanced data management techniques. The scenario of network-constrained movement is of particular interest to LBSs as a large amount of users of LBSs are car drivers and mobile users moving in vehicles. We term the transportation networks where LBS users are moving as spatial networks. The databases that manage the spatial network data as well as other relevant data are termed spatial network databases (SNDBs). Data management in SNDBs poses novel challenges to the database research community. Specifically, most existing discussions on spatial databases assume that objects can move arbitrarily in Euclidean space. The fundamental difference between network-constrained space and the Euclidean space is that the distance of two objects cannot be computed by their coordinates but by the shortest path on the network (Jensen, Kolár, Pedersen, & Timko, 2003). This makes it difficult to extend the existing data models, indexing structures, and query processing techniques from spatial databases to SNDBs.