Results 11  20
of
393
Techniques for Similarity Searching in Multimedia Databases
, 2010
"... Techniques for similarity searching in multimedia databases are reviewed. This includes a discussion of the curse of dimensionality, as well as multidimensional indexing, distancebased indexing, and the actual search process which is realized by nearest neighbor finding. ..."
Abstract

Cited by 27 (3 self)
 Add to MetaCart
Techniques for similarity searching in multimedia databases are reviewed. This includes a discussion of the curse of dimensionality, as well as multidimensional indexing, distancebased indexing, and the actual search process which is realized by nearest neighbor finding.
Fully Dynamic Spatial Approximation Trees
 In Proceedings of the 9th International Symposium on String Processing and Information Retrieval (SPIRE 2002), LNCS 2476
, 2002
"... The Spatial Approximation Tree (satree) is a recently proposed data structure for searching in metric spaces. It has been shown that it compares favorably against alternative data structures in spaces of high dimension or queries with low selectivity. Its main drawbacks are: costly construction ..."
Abstract

Cited by 27 (13 self)
 Add to MetaCart
The Spatial Approximation Tree (satree) is a recently proposed data structure for searching in metric spaces. It has been shown that it compares favorably against alternative data structures in spaces of high dimension or queries with low selectivity. Its main drawbacks are: costly construction time, poor performance in low dimensional spaces or queries with high selectivity, and the fact of being a static data structure, that is, once built, one cannot add or delete elements.
Path Oracles for Spatial Networks
, 2009
"... The advent of locationbased services has led to an increased demand for performing operations on spatial networks in real time. The challenge lies in being able to cast operations on spatial networks in terms of relational operators so that they can be performed in the context of a database. A line ..."
Abstract

Cited by 26 (8 self)
 Add to MetaCart
(Show Context)
The advent of locationbased services has led to an increased demand for performing operations on spatial networks in real time. The challenge lies in being able to cast operations on spatial networks in terms of relational operators so that they can be performed in the context of a database. A linearsized construct termed a path oracle is introduced that compactly encodes the n2 shortest paths between every pair of vertices in a spatial network having n vertices thereby reducing each of the paths to a single tuple in a relational database and enables finding shortest paths by repeated application of a single SQL SELECT operator. The construction of the path oracle is based on the observed coherence between the spatial positions of both source and destination vertices and the shortest paths between them which facilitates the aggregation of source and destination vertices into groups that share common vertices or edges on the shortest paths between them. With the aid of the WellSeparated Pair (WSP) technique, which has been applied to spatial networks using the network distance measure, a path oracle is proposed that takes O(sdn) space, where s is empirically estimated to be around 12 for road networks, but that can retrieve an intermediate link in a shortest path in O(logn) time using a Btree. An additional construct termed the pathdistance oracle of size O(n · max(sd, 1 d ε)) (empirically (n · max(122, 2.5 2 ε))) is proposed that can retrieve an intermediate vertex as well as an εapproximation of the network distances in O(logn) time using a Btree. Experimental results indicate that the proposed oracles are linear in n which means that they are scalable and can enable complicated query processing scenarios on massive spatial network datasets.
Indexing uncertain data
, 2009
"... Querying uncertain data has emerged as an important problem in data management due to the imprecise nature of many measurement data. In this paper we study answering range queries over uncertain data. Specifically, we are given a collection P of n points in R, each represented by its onedimensional ..."
Abstract

Cited by 23 (2 self)
 Add to MetaCart
(Show Context)
Querying uncertain data has emerged as an important problem in data management due to the imprecise nature of many measurement data. In this paper we study answering range queries over uncertain data. Specifically, we are given a collection P of n points in R, each represented by its onedimensional probability density function (pdf). The goal is to build an index on P such that given a query interval I and a probability threshold τ, we can quickly report all points of P that lie in I with probability at least τ. We present various indexing schemes with linear or nearlinear space and logarithmic query time. Our schemes support pdf’s that are either histograms or more complex ones such as Gaussian or piecewise algebraic. They also extend to the external memory model in which the goal is to minimize the number of disk accesses when querying the index.
Analysis and Evaluation of V*kNN: An Efficient Algorithm for Moving kNN Queries
"... The moving k nearest neighbor (MkNN) query continuously finds the k nearest neighbors of a moving query point. MkNN queries can be efficiently processed through the use of safe regions. In general, a safe region is a region within which the query point can move without changing the query answer. Th ..."
Abstract

Cited by 23 (18 self)
 Add to MetaCart
The moving k nearest neighbor (MkNN) query continuously finds the k nearest neighbors of a moving query point. MkNN queries can be efficiently processed through the use of safe regions. In general, a safe region is a region within which the query point can move without changing the query answer. This paper presents an incremental saferegionbased technique for answering MkNN queries, called the V*Diagram, as well as analysis and evaluation of its associated algorithm, V*kNN. Traditional saferegion approaches compute a safe region based on the data objects but independent of the query location. Our approachexploitstheknowledgeofthequerylocationand the boundary ofthesearchspaceinadditiontothedata objects. Asaresult,V*kNNhasmuchsmaller I/O and computation coststhanexistingmethods.Wefurtherprovidecostmodels to estimate the number of data accesses for V*kNN and a competitivetechnique,RISkNN.TheV*DiagramandV*kNN are also applicable to the domain of spatial networks and we present algorithms to construct a spatialnetwork V*Diagram. Our experimental results show that V*kNN significantlyoutperforms the competitive technique. The results also verify the accuracy of thec ost models.
Data Exploration of Turbulence Simulations using a Database Cluster
, 2007
"... We describe a new environment for the exploration of turbulent flows that uses a cluster of databases to store complete histories of Direct Numerical Simulation (DNS) results. This allows for spatial and temporal exploration of highresolution data that were traditionally too large to store and too c ..."
Abstract

Cited by 21 (4 self)
 Add to MetaCart
(Show Context)
We describe a new environment for the exploration of turbulent flows that uses a cluster of databases to store complete histories of Direct Numerical Simulation (DNS) results. This allows for spatial and temporal exploration of highresolution data that were traditionally too large to store and too computationally expensive to produce on demand. We perform analysis of these data directly on the databases nodes, which minimizes the volume of network traffic. The low network demands enable us to provide public access to this experimental platform and its datasets through Web services. This paper details the system design and implementation. Specifically, we focus on hierarchical spatial indexing, cachesensitive spatial scheduling of batch workloads, localizing computation through data partitioning, and load balancing techniques that minimize data movement. We provide real examples of how scientists use the system to perform highresolution turbulence research from standard desktop computing environments.
Ray Tracing Dynamic Scenes using Selective Restructuring
 EUROGRAPHICS SYMPOSIUM ON RENDERING (2007) JAN KAUTZ AND SUMANTA PATTANAIK (EDITORS)
, 2007
"... We present a novel algorithm to selectively restructure bounding volume hierarchies (BVHs) for ray tracing dynamic scenes. We derive two new metrics to evaluate the culling efficiency and restructuring benefit of any BVH. Based on these metrics, we perform selective restructuring operations that eff ..."
Abstract

Cited by 20 (7 self)
 Add to MetaCart
We present a novel algorithm to selectively restructure bounding volume hierarchies (BVHs) for ray tracing dynamic scenes. We derive two new metrics to evaluate the culling efficiency and restructuring benefit of any BVH. Based on these metrics, we perform selective restructuring operations that efficiently reconstruct small portions of a BVH instead of the entire BVH. Our approach is general and applicable to complex and dynamic scenes, including topological changes. We use the selective restructuring algorithm to improve the performance of ray tracing dynamic scenes that consist of hundreds of thousands of triangles. In our benchmarks, we observe up to an order of magnitude improvement over prior BVHbased ray tracing algorithms.
On fast construction of spatial hierarchies for ray tracing
 IN PROCEEDINGS OF THE 2006 IEEE SYMPOSIUM ON INTERACTIVE RAY TRACING
, 2006
"... In this paper we address the problem of fast construction of spatial hierarchies for ray tracing with applications in animated environments including nonrigid animations. We discuss properties of currently used techniques with O(N log N) construction time for kdtrees and bounding volume hierarchie ..."
Abstract

Cited by 20 (1 self)
 Add to MetaCart
In this paper we address the problem of fast construction of spatial hierarchies for ray tracing with applications in animated environments including nonrigid animations. We discuss properties of currently used techniques with O(N log N) construction time for kdtrees and bounding volume hierarchies. Further, we propose a hybrid data structure blending between a spatial kdtree and bounding volume primitives. We keep our novel hierarchical data structures algorithmically efficient and comparable with kdtrees by the use of a cost model based on surface area heuristics. Although the time complexity O(N log N) is a lower bound required for construction of any spatial hierarchy that corresponds to sorting based on comparisons, using approximate method based on discretization we propose a new hierarchical data structures with expected O(N log log N) time complexity. We also discuss constants behind the construction algorithms of spatial hierarchies important in practice. We document the performance of our algorithms by results obtained from the implementation on nine scenes.