In this paper, we present a new privacy-aware query processing framework Capser * in which mobile and stationary users can obtain snapshot and/or continuous location-based services without revealing their private location information. In particular, we propose a privacy-aware query processor embedded inside a location-based database server to deal with snapshot and continuous queries based on the knowledge of the user’s cloaked location rather than the exact location. Our proposed privacy-aware query processor is completely independent of how we compute the user’s cloaked location. In other words, any existing location anonymization algorithms that blur the user’s private location into cloaked rectilinear areas can be employed to protect the user’s location privacy. We first propose a privacy-aware query processor that not only supports three new privacy-aware query types, but it also achieves a trade-off between query processing cost and answer optimality. Then, to improve system scalability of processing continuous privacy-aware queries, we propose a shared execution paradigm that shares query processing among a large number of continuous queries. The proposed scalable paradigm can be tuned through two parameters to trade off between system scalability and answer optimality. Experimental results show that our query processor achieves high quality snapshot and continuous location-based services while

This paper presents a novel algorithm for Incremental and General Evaluation of continuous Reverse Nearest neighbor queries (IGERN, for short). The IGERN algorithm is general as it is applicable for both the monochromatic and bichromatic reverse nearest neighbor queries. The incremental aspect of IGERN is achieved through determining only a small set of objects to be monitored. While previous algorithms for monochromatic queries rely mainly on monitoring six pie regions, IGERN takes a radical approach by monitoring only a single region around the query object. The IGERN algorithm clearly outperforms the state-of-theart algorithms in monochromatic queries. In addition, the IGERN algorithm presents the first attempt for continuous evaluation of bichromatic reverse nearest neighbor queries. The computational complexity of IGERN is presented in comparison to the state-of-the-art algorithms in the monochromatic case and to the use of Voronoi diagrams for the bichromatic case. In addition, the correctness of IGERN in both the monochromatic and bichromatic cases are proved. Extensive experimental analysis shows that IGERN is efficient, is scalable, and outperforms previous techniques for continuous reverse nearest neighbor queries. 1

The collection of moving object data is becoming more and more common, and therefore there is an increasing need for the efficient analysis and knowledge extraction of these data in different application domains. Trajectory data are normally available as sample points, and do not carry semantic information, which is of fundamental importance for the comprehension of these data. Therefore, the analysis of trajectory data becomes expensive from a computational point of view and complex from a user’s perspective. Enriching trajectories with semantic geographical information may simplify queries, analysis, and mining of moving object data. In this paper we propose a data preprocessing model to add semantic information to trajectories in order to facilitate trajectory data analysis in different application domains. The model is generic enough to represent the important parts of trajectories that are relevant to the application, not being restricted to one specific application. We present an algorithm to compute the important parts and show that the query complexity for the semantic analysis of trajectories will be significantly reduced with the proposed model.

In the real world, lots of objects with changing position can be found. Some of them repeat the same movement several times, called periodic movements. Examples include airplanes, trains, planets, and marine turtles. This paper describes a model for representing the periodic movements to be stored in a database system, exploiting the information about the repetitions. The model is generic enough to represent any kind of movement, not being restricted to objects with repetitions in their movement. We present algorithms to detect the repetitions and to convert to the periodic representation as well as the implementation of some operations on such representation. We claim that the data volume can be drastically reduced when repetitions in movements occur. Moreover, some operations can take advantage on the data representation and therefore have their performance improved. We show, in an experimental evaluation against the so-called flat representation, that the approach presented in this paper significantly improves the performance of query processing in a database system when dealing with objects with some periodic movement. We also show that, for the worst case where the objects do not follow any periodic movement at all, our approach still performs acceptably. 1.

Abstract. The volume of real-time streaming data produced by georeferenced sensors and sensor networks is staggeringly large and growing rapidly. Queries on these geo-streams often require tracking spatiotemporal extent (e.g. evolving region) continuously in real time. The notion of real-time monitoring and notification requires support from a database capable of tracking and querying dynamic and transient spatiotemporal events as well as static spatial objects and sending out real-time notifications. In this paper, we leverage the work in data type based spatio-temporal databases and propose new data types called STREAM and their abstract semantics to support geo-stream applications. New operations on STREAM data types are defined and illustrated by embedding them into SQL. 1

This paper tackles a major privacy concern in current location-based services where users have to continuously report their locations to the database server in order to obtain the service. For example, a user asking about the nearest gas station has to report her exact location. With untrusted servers, reporting the location information may lead to several privacy threats. In this paper, we present Casper 1; a new framework in which mobile and stationary users can entertain location-based services without revealing their location information. Casper consists of two main components, the location anonymizer and the privacy-aware query processor. The location anonymizer blurs the users ’ exact location information into cloaked spatial regions based on userspecified privacy requirements. The privacy-aware query processor is embedded inside the location-based database server in order to deal with the cloaked spatial areas rather than the exact location information. Experimental results show that Casper achieves high quality location-based services while providing anonymity for both data and queries. 1.

The explosive growth of location-detection devices, wireless communications, and mobile databases has resulted in the realization of location-based services as commercial products and research prototypes. Unfortunately, current locationbased applications (e.g., store finders) are rigid as they are completely isolated from various concepts of user “preferences” and/or “context”. Such rigidness results in nonsuitable services (e.g., a vegetarian user may get a restaurant with non-vegetarian menu). In this paper, we introduce the system architecture of a Context and Preference-Aware Location-based Database Server (CareDB, for short), currently under development at University of Minnesota, that delivers personalized services to its customers based on the surrounding context. CareDB goes beyond the traditional scheme of “one size fits all ” of existing location-aware database systems. Instead, CareDB tailors its functionalities and services based on the preference and context of each customer. Examples of services provided by CareDB include a restaurant finder application in which CareDB does not base its choice of restaurants solely on the user location. Instead, CareDB will base its choice on both the user location and surrounding context (e.g., user dietary restriction, user preferences, and road traffic conditions). Within the framework of CareDB, we discuss research challenges and directions towards an efficient and practical realization of context-aware location-based query processing. Namely, we discuss the challenges for designing user profiles, multiobjective query processing, context-aware query optimizers, context-aware query operators, and continuous queries.

We address the problem of performing efficient similarity join for large sets of moving objects trajectories. Unlike previous approaches which use a dedicated index in a transformed space, our premise is that in many applications of location-based services, the trajectories are already indexed in their native space, in order to facilitate the processing of common spatio-temporal queries, e.g., range, nearest neighbor etc. We introduce a novel distance measure adapted from the classic Fréchet distance, which can be naturally extended to support lower/upper bounding using the underlying indices of moving object databases in the native space. This, in turn, enables efficient implementation of various trajectory similarity joins. We report on extensive experiments demonstrating that our methodology provides performance speed-up of trajectory similarity join by more than 50 % on average, while maintaining effectiveness comparable to the well-known approaches for identifying trajectory similarity based on time-series analysis. 1

The technologies of mobile communications and ubiquitous computing pervade our society, and wireless networks sense the movement of people and vehicles, generating large volumes of mobility data. Miniaturization, wearability, pervasiveness of mobile devices are producing traces of our mobile activity, with increasing positioning accuracy and semantic richness: location data from mobile phones (Global System for Mobile Communications: GSM cell positions), Geographic Positioning System (GPS) tracks from mobile devices receiving geo-positions from satellites, etc. The objective of the GeoPKDD (Geographic Privacy-aware Knowledge Discovery and Delivery), a project funded by European Commission under the Future and emerging technologies (FET) program of the 6th Framework(FP6), has been to discover useful knowledge about human movement behavior from mobility data, while preserving the privacy of the people under observation. Pursuing this ambitious objective, the GeoPKDD project has started a new exciting multidisciplinary research area, at the crossroads of mobility, data mining, and privacy. This paper gives a short overview of the envisaged research challenges and the project achievements. 1

Many database applications deal with spatio-temporal phenomena, and during the last decade a lot of research targeted locationbased services, moving objects, traffic jam preventions, meteorology, etc. In strong contrast, there exist only very few proposals for an implementation of a spatio-temporal database system let alone a web-based spatio-temporal information system. This paper describes the design and implementation of a webbased spatio-temporal information system. The system uses Secondo as spatio-temporal DBMS for handling moving objects and MapServer as an OGC-compliant rendering engine for static spatial data. We describe the architecture of the system and compare our system with a standalone application. The paper investigates in detail issues that arise in the context of the web. First, we describe an implementation of a lightweight client that takes advantage of the functionality offered by Secondo and MapServer. Second, we describe how moving objects can be represented in GML. We discuss possible GML representations, propose an extension of GML that uses 3D segments (2D location + time) to represent moving objects, and present experiments that compare the solutions.