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31
Never walk alone: Uncertainty for anonymity in moving objects databases
 In ICDE
, 2008
"... Abstract — Preserving individual privacy when publishing data is a problem that is receiving increasing attention. According to the kanonymity principle, each release of data must be such that each individual is indistinguishable from at least k −1 other individuals. In this paper we study the prob ..."
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Cited by 73 (5 self)
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Abstract — Preserving individual privacy when publishing data is a problem that is receiving increasing attention. According to the kanonymity principle, each release of data must be such that each individual is indistinguishable from at least k −1 other individuals. In this paper we study the problem of anonymity preserving data publishing in moving objects databases. We propose a novel concept of kanonymity based on colocalization that exploits the inherent uncertainty of the moving object’s whereabouts. Due to sampling and positioning systems (e.g., GPS) imprecision, the trajectory of a moving object is no longer a polyline in a threedimensional space, instead it is a cylindrical volume, where its radius δ represents the possible location imprecision: we know that the trajectory of the moving object is within this cylinder, but we do not know exactly where. If another object moves within the same cylinder they are indistinguishable from each other. This leads to the definition of (k, δ)anonymity for moving objects databases. We first characterize the (k, δ)anonymity problem and discuss techniques to solve it. Then we focus on the most promising technique by the point of view of information preservation, namely space translation. We develop a suitable measure of the information distortion introduced by space translation, and we prove that the problem of achieving (k, δ)anonymity by space translation with minimum distortion is NPhard. Faced with the hardness of our problem we propose a greedy algorithm based on clustering and enhanced with ad hoc preprocessing and outlier removal techniques. The resulting method, named N WA (N ever Walk Alone), is empirically evaluated in terms of data quality and efficiency. Data quality is assessed both by means of objective measures of information distortion, and by comparing the results of the same spatiotemporal range queries executed on the original database and on the (k, δ)anonymized one. Experimental results show that for a wide range of values of δ and k, the relative error introduced is kept low, confirming that N WA produces high quality (k, δ)anonymized data.
Efficient Computation of Reverse Skyline Queries
, 2007
"... In this paper, for the first time, we introduce the concept of Reverse Skyline Queries. At first, we consider for a multidimensional data set P the problem of dynamic skyline queries according to a query point q. This kind of dynamic skyline corresponds to the skyline of a transformed data space whe ..."
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Cited by 63 (0 self)
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In this paper, for the first time, we introduce the concept of Reverse Skyline Queries. At first, we consider for a multidimensional data set P the problem of dynamic skyline queries according to a query point q. This kind of dynamic skyline corresponds to the skyline of a transformed data space where point q becomes the origin and all points of P are represented by their distance vector to q. The reverse skyline query returns the objects whose dynamic skyline contains the query object q. In order to compute the reverse skyline of an arbitrary query point, we first propose a Branch and Bound algorithm (called BBRS), which is an improved customization of the original BBS algorithm. Furthermore, we identify a super set of the reverse skyline that is used to bound the search space while computing the reverse skyline. To further reduce the computational cost of determining if a point belongs to the reverse skyline, we propose an enhanced algorithm (called RSSA) that is based on accurate precomputed approximations of the skylines. These approximations are used to identify whether a point belongs to the reverse skyline or not. Through extensive experiments with both realworld and synthetic datasets, we show that our algorithms can efficiently support reverse skyline queries. Our enhanced approach improves reversed skyline processing by up to an order of magnitude compared to the algorithm without the usage of precomputed approximations.
Detecting Commuting Patterns by Clustering Subtrajectories
, 2008
"... In this paper we consider the problem of detecting commuting patterns in a trajectory. For this we search for similar subtrajectories. To measure spatial similarity we choose the Fréchet distance and the discrete Fréchet distance between subtrajectories, which are invariant under differences in spee ..."
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Cited by 30 (14 self)
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In this paper we consider the problem of detecting commuting patterns in a trajectory. For this we search for similar subtrajectories. To measure spatial similarity we choose the Fréchet distance and the discrete Fréchet distance between subtrajectories, which are invariant under differences in speed. We give several approximation algorithms, and also show that the problem of finding the ‘longest’ subtrajectory cluster is as hard as MaxClique to compute and approximate.
TrajStore: An Adaptive Storage System for Very Large Trajectory Data Sets
"... The rise of GPS and broadbandspeed wireless devices has led to tremendous excitement about a range of applications broadly characterized as “location based services”. Current database storage systems, however, are inadequate for manipulating the very large and dynamic spatiotemporal data sets req ..."
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Cited by 18 (2 self)
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The rise of GPS and broadbandspeed wireless devices has led to tremendous excitement about a range of applications broadly characterized as “location based services”. Current database storage systems, however, are inadequate for manipulating the very large and dynamic spatiotemporal data sets required to support such services. Proposals in the literature either present new indices without discussing how to cluster data, potentially resulting in many disk seeks for lookups of densely packed objects, or use static quadtrees or other partitioning structures, which become rapidly suboptimal as the data or queries evolve. As a result of these performance limitations, we built TrajStore, a dynamic storage system optimized for efficiently retrieving all data in a particular spatiotemporal region. TrajStore maintains an optimal index on the data and dynamically colocates and compresses spatially and temporally adjacent segments on disk. By letting the storage layer evolve with the index, the system adapts to incoming queries and data and is able to answer most queries via a very limited number of I/Os, even when the queries target regions containing hundreds or thousands of different trajectories.
On the Spatiotemporal Burstiness of Terms
"... Thousands of documents are made available to the users via the web on a daily basis. One of the most extensively studied problems in the context of such document streams is burst identification. Given a term t, a burst is generally exhibited when an unusually high frequency is observed for t. While ..."
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Cited by 14 (1 self)
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Thousands of documents are made available to the users via the web on a daily basis. One of the most extensively studied problems in the context of such document streams is burst identification. Given a term t, a burst is generally exhibited when an unusually high frequency is observed for t. While spatial and temporal burstiness have been studied individually in the past, our work is the first to simultaneously track and measure spatiotemporal term burstiness. In addition, we use the mined burstiness information toward an efficient documentsearch engine: given a user’s query of terms, our engine returns a ranked list of documents discussing influential events with a strong spatiotemporal impact. We demonstrate the efficiency of our methods with an extensive experimental evaluation on real and synthetic datasets. 1.
Boosting spatial pruning: on optimal pruning of mbrs
 In SIGMOD
, 2010
"... Fast query processing of complex objects, e.g. spatial or uncertain objects, depends on efficient spatial pruning of the objects ’ approximations, which are typically minimum bounding rectangles (MBRs). In this paper, we propose a novel effective and efficient criterion to determine the spatial topo ..."
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Cited by 12 (7 self)
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Fast query processing of complex objects, e.g. spatial or uncertain objects, depends on efficient spatial pruning of the objects ’ approximations, which are typically minimum bounding rectangles (MBRs). In this paper, we propose a novel effective and efficient criterion to determine the spatial topology between multidimensional rectangles. Given three rectangles R, A, and B in a multidimensional space, the task is to determine whether A is definitely closer to R than B. This domination relation is used in many applications to perform spatial pruning. Traditional techniques apply spatial pruning based on minimal and maximal distance. These techniques however show significant deficiencies in terms of effectivity. We prove that our decision criterion is correct, complete, and efficient to compute even for high dimensional databases. In addition, we tackle the problem of computing the number of objects dominating an object o. The challenge here is to incorporate objects that only partially dominate o. In this work we will show how to detect such partial domination topology by using a modified version of our decision criterion. We propose strategies for conservatively and progressively estimating the total number of objects dominating an object. Our experiments show that the new pruning criterion, albeit very general and widely applicable, significantly outperforms current stateoftheart pruning criteria.
Querying trajectories using flexible patterns
 In EDBT
, 2010
"... The wide adaptation of GPS and cellular technologies has created many applications that collect and maintain large repositories of data in the form of trajectories. Previous work on querying/analyzing trajectorial data typically falls into methods that either address spatial range and NN queries, or ..."
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Cited by 8 (4 self)
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The wide adaptation of GPS and cellular technologies has created many applications that collect and maintain large repositories of data in the form of trajectories. Previous work on querying/analyzing trajectorial data typically falls into methods that either address spatial range and NN queries, or, similarity based queries. Nevertheless, trajectories are complex objects whose behavior over time and space can be better captured as a sequence of interesting events. We thus facilitate the use of motion “pattern ” queries which allow the user to select trajectories based on specific motion patterns. Such patterns are described as regular expressions over a spatial alphabet that can be implicitly or explicitly anchored to the time domain. Moreover, we are interested in “flexible ” patterns that allow the user to include “variables” in the query pattern and thus greatly increase its expressive power. In this paper we introduce a framework for efficient processing of flexible pattern queries. The framework includes an underlying indexing structure and algorithms for query processing using different evaluation strategies. An extensive performance evaluation of this framework shows significant performance improvement when compared to existing solutions. 1.
FINDING POPULAR PLACES
, 2008
"... Widespread availability of location aware devices (such as GPS receivers) promotes capture of detailed movement trajectories of people, animals, vehicles and other moving objects. We investigate spatiotemporal movement patterns in large tracking data sets, i.e. in large sets of polygonal paths. Sp ..."
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Cited by 6 (3 self)
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Widespread availability of location aware devices (such as GPS receivers) promotes capture of detailed movement trajectories of people, animals, vehicles and other moving objects. We investigate spatiotemporal movement patterns in large tracking data sets, i.e. in large sets of polygonal paths. Specifically, we study socalled ‘popular places’, that is, regions that are visited by many entities. Given a set of polygonal paths with a total of ¯n vertices, we look at the problem of computing such popular places in two different settings. For the discrete model, where only the vertices of the polygonal paths are considered, we propose an O(¯nlog ¯n) algorithm; and for the continuous model, where also the straight line segments between the vertices of a polygonal path are considered, we develop an O(¯n²) algorithm. We also present lower bounds and hardness results.
On Efficiently Searching Trajectories and Archival Data for Historical Similarities
, 2008
"... We study the problem of efficiently evaluating similarity queries on histories, where a history is a ddimensional time series for d ≥ 1. While there are some solutions for timeseries and spatiotemporal trajectories where typically d ≤ 3, we are not aware of any work that examines the problem for l ..."
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Cited by 6 (0 self)
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We study the problem of efficiently evaluating similarity queries on histories, where a history is a ddimensional time series for d ≥ 1. While there are some solutions for timeseries and spatiotemporal trajectories where typically d ≤ 3, we are not aware of any work that examines the problem for larger values of d. In this paper, we address the problem in its general case and propose a class of summaries for histories with a few interesting properties. First, for commonly used distance functions such as the Lpnorm, LCSS, and DTW, the summaries can be used to efficiently prune some of the histories that cannot be in the answer set of the queries. Second, histories can be indexed based on their summaries, hence the qualifying candidates can be efficiently retrieved. To further reduce the number of unnecessary distance computations for false positives, we propose a finer level approximation of histories, and an algorithm to find an approximation with the least maximum distance estimation error. Experimental results confirm that the combination of our feature extraction approaches and the indexability of our summaries can improve upon existing methods and scales up for larger values of d and database sizes, based on our experiments on real and synthetic datasets of 17dimensional histories.
OnLine Discovery of Hot Motion Paths
"... We consider an environment of numerous moving objects, equipped with locationsensing devices and capable of communicating to a central coordinator. In this setting, we investigate the problem of maintaining hot motion paths, i.e., routes frequently followed by multiple objects over the recent past. ..."
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Cited by 6 (0 self)
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We consider an environment of numerous moving objects, equipped with locationsensing devices and capable of communicating to a central coordinator. In this setting, we investigate the problem of maintaining hot motion paths, i.e., routes frequently followed by multiple objects over the recent past. Motion paths approximate portions of objects’ movement within a tolerance margin that depends on the uncertainty inherent in positional measurements. Discovery of hot motion paths is important to applications requiring classification/profiling based on monitored movement patterns, such as targeted advertising, resource allocation, etc. To achieve this goal, we delegate part of the path extraction process to objects, by assigning to them adaptive lightweight filters that dynamically suppress unnecessary location updates and thus, help reducing communication overhead. We demonstrate the benefits of our methods and their efficiency through extensive experiments on synthetic data sets. 1.