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41
Nearest neighbor search on moving object trajectories
- In Proc. of the 9th Intl. Symp. on Spatial and Temporal Databases (SSTD
, 2005
"... Abstract. With the increasing number of Mobile Location Services (MLS), the need for effective k-NN query processing over historical trajectory data has become the vehicle for data analysis, thus improving existing or even proposing new services. In this paper, we investigate mechanisms to perform N ..."
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Cited by 40 (3 self)
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Abstract. With the increasing number of Mobile Location Services (MLS), the need for effective k-NN query processing over historical trajectory data has become the vehicle for data analysis, thus improving existing or even proposing new services. In this paper, we investigate mechanisms to perform NN search on R-tree-like structures storing historical information about moving object trajectories. The proposed branch-and-bound algorithms vary with respect to the type of the query object (stationary or moving point) as well as the type of the query result (continuous or not). We also propose novel metrics to support our search ordering and pruning strategies. Using the implementation of the proposed algorithms on a member of the R-tree family for trajectory data (the TBtree), we demonstrate their scalability and efficiency through an extensive experimental study using synthetic and real datasets. 1
Clustering Uncertain Data Using Voronoi Diagrams and R-tree index
, 2010
"... We study the problem of clustering uncertain objects whose locations are described by probability density functions (pdfs). We show that the UK-means algorithm, which generalizes the k-means algorithm to handle uncertain objects, is very inefficient. The inefficiency comes from the fact that UK-mea ..."
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Cited by 22 (1 self)
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We study the problem of clustering uncertain objects whose locations are described by probability density functions (pdfs). We show that the UK-means algorithm, which generalizes the k-means algorithm to handle uncertain objects, is very inefficient. The inefficiency comes from the fact that UK-means computes expected distances (EDs) between objects and cluster representatives. For arbitrary pdfs, expected distances are computed by numerical integrations, which are costly operations. We propose pruning techniques that are based on Voronoi diagrams to reduce the number of expected distance calculations. These techniques are analytically proven to be more effective than the basic bounding-box-based technique previously known in the literature. We then introduce an R-tree index to organize the uncertain objects so as to reduce pruning overheads. We conduct experiments to evaluate the effectiveness of our novel techniques. We show that our techniques are additive and, when used in combination, significantly outperform previously known methods.
Building Confidential and Efficient Query Services in the Cloud with RASP Data Perturbation
- IEEE Trans. Knowledge and Data Eng
, 2014
"... ar ..."
A Fun Application of Compact Data Structures to Indexing Geographic Data
"... The way memory hierarchy has evolved in recent decades has opened new challenges in the development of indexing structures in general and spatial access methods in particular. In this paper we propose an original approach to represent geographic data based on compact data structures used in other f ..."
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Cited by 10 (7 self)
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The way memory hierarchy has evolved in recent decades has opened new challenges in the development of indexing structures in general and spatial access methods in particular. In this paper we propose an original approach to represent geographic data based on compact data structures used in other fields such as text or image compression. A wavelet tree-based structure allows us to represent minimum bounding rectangles solving geographic range queries in logarithmic time. A comparison with classical spatial indexes, such as the R-tree, shows that our structure can be considered as a fun, yet seriously competitive, alternative to these classical approaches.
Locality and bounding-box quality of twodimensional space-filling curves
- in: ESA
"... Space-filling curves can be used to organise points in the plane into bounding-box hierarchies (such as R-trees). We develop measures of the bounding-box quality of space-filling curves that express how effective different space-filling curves are for this purpose. We give general lower bounds on th ..."
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Cited by 9 (6 self)
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Space-filling curves can be used to organise points in the plane into bounding-box hierarchies (such as R-trees). We develop measures of the bounding-box quality of space-filling curves that express how effective different space-filling curves are for this purpose. We give general lower bounds on the bounding-box quality measures and on locality according to Gotsman and Lindenbaum for a large class of space-filling curves. We describe a generic algorithm to approximate these and similar quality measures for any given curve. Using our algorithm we find good approximations of the locality and the bounding-box quality of several known and new space-filling curves. Surprisingly, some curves with relatively bad locality by Gotsman and Lindenbaum’s measure, have good bounding-box quality, while the curve with the best-known locality has relatively bad bounding-box quality. 1
RASP: Efficient Multidimensional Range Query on Attack-Resilient Encrypted Databases
"... Range query is one of the most frequently used queries for online data analytics. Providing such a query service could be expensive for the data owner. With the development of services computing and cloud computing, it has become possible to outsource large databases to database service providers an ..."
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Cited by 9 (3 self)
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Range query is one of the most frequently used queries for online data analytics. Providing such a query service could be expensive for the data owner. With the development of services computing and cloud computing, it has become possible to outsource large databases to database service providers and let the providers maintain the range-query service. With outsourced services, the data owner can greatly reduce the cost in maintaining computing infrastructure and data-rich applications. However, the service provider, although honestly processing queries, may be curious about the hosted data and received queries. Most existing encryption based approaches require linear scan over the entire database, which is inappropriate for online data analytics on large databases. While a few encryption solutions are more focused on efficiency side, they are vulnerable to attackers equipped with certain prior knowledge. We propose the Random Space Encryption (RASP) approach that allows efficient range search with stronger attack resilience than existing efficiency-focused approaches. We use RASP to generate indexable auxiliary data that is resilient to prior knowledge enhanced attacks. Range queries are securely transformed to the encrypted data space and then efficiently processed with a two-stage processing algorithm. We thoroughly studied the potential attacks on the encrypted data and queries at three different levels of prior knowledge available to an attacker. Experimental results on synthetic and real datasets show that this encryption approach allows efficient processing of range queries with high resilience to attacks. Categories and Subject Descriptors H.2.0 [Database Management]: General—Security, integrity, and
Four-Dimensional Hilbert Curves for R-Trees
"... Two-dimensional R-trees are a class of spatial index structures in which objects are arranged to enable fast window queries: report all objects that intersect a given query window. One of the most successful methods of arranging the objects in the index structure is based on sorting the objects acco ..."
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Cited by 6 (4 self)
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Two-dimensional R-trees are a class of spatial index structures in which objects are arranged to enable fast window queries: report all objects that intersect a given query window. One of the most successful methods of arranging the objects in the index structure is based on sorting the objects according to the positions of their centres along a two-dimensional Hilbert spacefilling curve. Alternatively one may use the coordinates of the objects ’ bounding boxes to represent each object by a four-dimensional point, and sort these points along a four-dimensional Hilbert-type curve. In experiments by Kamel and Faloutsos and by Arge et al. the first solution consistently outperformed the latter when applied to point data, while the latter solution clearly outperformed the first on certain artificial rectangle data. These authors did not specify which four-dimensional Hilbert-type curve was used; many exist. In this paper we show that the results of the previous papers can be explained by the choice of the fourdimensional Hilbert-type curve that was used and by the way it was rotated in four-dimensional space. By selecting a curve that has certain properties and choosing the right rotation one can combine the strengths of the two-dimensional and the four-dimensional approach into one, while avoiding their apparent weaknesses. The effectiveness of our approach is demonstrated with experiments on various data sets. For real data taken from VLSI design, our new curve yields R-trees with query times that are better than those of R-trees that were obtained with previously used curves. 1
A New Point Access Method based on Wavelet Trees
, 2009
"... The development of index structures that allow efficient retrieval of spatial objects has been a topic of interest in the last decades. Most of these structures have been designed for secondary memory. However, in the last years the price of memory has decreased drastically. Nowadays it is feasible ..."
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Cited by 6 (5 self)
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The development of index structures that allow efficient retrieval of spatial objects has been a topic of interest in the last decades. Most of these structures have been designed for secondary memory. However, in the last years the price of memory has decreased drastically. Nowadays it is feasible to place complete spatial indexes in main memory. In this paper we focus in a subcategory of spatial indexes named Point Access Methods. These indexes are designed to solve the problem of indexing points. We present a new index structure designed for two dimensions and main memory that keeps a good trade-off between the space needed to store the index and its search efficiency. Our structure is based on a wavelet tree, which was originally designed to represent sequences, but has been successfully used as an index in areas like information retrieval or image compression.
Efficient Similarity Join of Large Sets of Moving Object Trajectories
"... 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 th ..."
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Cited by 5 (1 self)
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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
Finding Probabilistic Nearest Neighbors for Query Objects with Imprecise Locations
- Tenth International Conference on Mobile Data Management: Systems, Services and Middleware
, 2009
"... Abstract—A nearest neighbor query is an important notion in spatial databases and moving object databases. In the emerging application fields of moving object technologies, such as mobile sensors and mobile robotics, the location of an object is often imprecise due to noise and estimation errors. We ..."
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Cited by 3 (0 self)
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Abstract—A nearest neighbor query is an important notion in spatial databases and moving object databases. In the emerging application fields of moving object technologies, such as mobile sensors and mobile robotics, the location of an object is often imprecise due to noise and estimation errors. We propose techniques for processing a nearest neighbor query when the location of the query object is specified by an imprecise Gaussian distribution. First, we consider two query processing strategies for pruning candidate objects, which can reduce the number of objects that require numerical integration for computing the qualification probabilities. In addition, we consider a hybrid approach that combines the two strategies. The performance of the proposed methods is evaluated using test data. I.
