Results 1  10
of
30
Indexing moving points
, 2003
"... We propose three indexing schemes for storing a set S of N points in the plane, each moving along a linear trajectory, so that any query of the following form can be answered quickly: Given a rectangle R and a real value t; report all K points of S that lie inside R at time t: We first present an in ..."
Abstract

Cited by 168 (13 self)
 Add to MetaCart
We propose three indexing schemes for storing a set S of N points in the plane, each moving along a linear trajectory, so that any query of the following form can be answered quickly: Given a rectangle R and a real value t; report all K points of S that lie inside R at time t: We first present an indexing structure that, for any given constant e> 0; uses OðN=BÞ disk blocks and answers a query in OððN=BÞ 1=2þe þ K=BÞ I/Os, where B is the block size. It can also report all the points of S that lie inside R during a given time interval. A point can be inserted or deleted, or the trajectory of a point can be changed, in Oðlog 2 B NÞ I/Os. Next, we present a general approach that improves the query time if the queries arrive in chronological order, by allowing the index to evolve over time. We obtain a tradeoff between the query time and the number of times the index needs to be updated as the points move. We also describe an indexing scheme in which the number of I/Os required to answer a query depends monotonically on the difference between the query time stamp t and the current time. Finally, we develop an efficient indexing scheme to answer approximate
External Memory Data Structures
, 2001
"... In many massive dataset applications the data must be stored in space and query efficient data structures on external storage devices. Often the data needs to be changed dynamically. In this chapter we discuss recent advances in the development of provably worstcase efficient external memory dynami ..."
Abstract

Cited by 81 (36 self)
 Add to MetaCart
In many massive dataset applications the data must be stored in space and query efficient data structures on external storage devices. Often the data needs to be changed dynamically. In this chapter we discuss recent advances in the development of provably worstcase efficient external memory dynamic data structures. We also briefly discuss some of the most popular external data structures used in practice.
Range Searching
, 1996
"... Range searching is one of the central problems in computational geometry, because it arises in many applications and a wide variety of geometric problems can be formulated as a rangesearching problem. A typical rangesearching problem has the following form. Let S be a set of n points in R d , an ..."
Abstract

Cited by 70 (1 self)
 Add to MetaCart
Range searching is one of the central problems in computational geometry, because it arises in many applications and a wide variety of geometric problems can be formulated as a rangesearching problem. A typical rangesearching problem has the following form. Let S be a set of n points in R d , and let R be a family of subsets; elements of R are called ranges . We wish to preprocess S into a data structure so that for a query range R, the points in S " R can be reported or counted efficiently. Typical examples of ranges include rectangles, halfspaces, simplices, and balls. If we are only interested in answering a single query, it can be done in linear time, using linear space, by simply checking for each point p 2 S whether p lies in the query range.
Efficient Searching with Linear Constraints (Extended Abstract)
"... ) Pankaj K. Agarwal Lars Arge y Jeff Erickson z Paolo G. Franciosa x Jeffrey Scott Vitter  Abstract We show how to preprocess a set S of points in R d to get an external memory data structure that efficiently supports linearconstraint queries. Each query is in the form of a linear c ..."
Abstract

Cited by 58 (17 self)
 Add to MetaCart
) Pankaj K. Agarwal Lars Arge y Jeff Erickson z Paolo G. Franciosa x Jeffrey Scott Vitter  Abstract We show how to preprocess a set S of points in R d to get an external memory data structure that efficiently supports linearconstraint queries. Each query is in the form of a linear constraint a \Delta x b; the data structure must report all the points of S that satisfy the query. Our goal is to minimize the number of disk blocks required to store the data structure and the number of disk accesses (I/Os) required to answer a query. For d = 2, we present the first nearlinear size data structures that can answer linearconstraint queries using an optimal number of I/Os. We also present a linearsize data structure that can answer queries efficiently in the worst case. We combine these two approaches to obtain tradeoffs between space and query time. Finally, we show that some of our techniques extend to higher dimensions d. Center for Geometric Computing, Computer...
Efficient Bulk Operations on Dynamic RTrees
 ALGORITHMICA
, 2002
"... In recent years there has been an upsurge of interest in spatial databases. A major issue is how to manipulate efficiently massive amounts of spatial data stored on disk in multidimensional spatial indexes (data structures). Construction of spatial indexes (bulk loading) has been studied intensivel ..."
Abstract

Cited by 41 (10 self)
 Add to MetaCart
In recent years there has been an upsurge of interest in spatial databases. A major issue is how to manipulate efficiently massive amounts of spatial data stored on disk in multidimensional spatial indexes (data structures). Construction of spatial indexes (bulk loading) has been studied intensively in the database community. The continuous arrival of massive amounts of new data makes it important to update existing indexes (bulk updating) efficiently. In this paper we present a simple, yet efficient, technique for performing bulk update and query operations on multidimensional indexes. We present our technique in terms of the socalled Rtree and its variants, as they have emerged as practically efficient indexing methods for spatial data. Our method uses ideas from the buffer tree lazy buffering technique and fully utilizes the available internal memory and the page size of the operating system. We give a theoretical analysis of our technique, showing that it is efficient both in terms of I/O communication, disk storage, and internal computation time. We also present the results of an extensive set of experiments showing that in practice our approach performs better than the previously best known bulk update methods with respect to update time, and that it produces a better quality index in terms of query performance. One important novel feature of our technique is that in most cases it allows us to perform a batch of updates and queries simultaneously. To be able to do so is essential in environments where queries have to be answered even while the index is being updated and reorganized.
The skip quadtree: a simple dynamic data structure for multidimensional data
 In Proc. 21st ACM Symposium on Computational Geometry
, 2005
"... We present a new multidimensional data structure, which we call the skip quadtree (for point data in R 2) or the skip octree (for point data in R d, with constant d> 2). Our data structure combines the best features of two wellknown data structures, in that it has the welldefined “box”shaped reg ..."
Abstract

Cited by 36 (5 self)
 Add to MetaCart
We present a new multidimensional data structure, which we call the skip quadtree (for point data in R 2) or the skip octree (for point data in R d, with constant d> 2). Our data structure combines the best features of two wellknown data structures, in that it has the welldefined “box”shaped regions of region quadtrees and the logarithmicheight search and update hierarchical structure of skip lists. Indeed, the bottom level of our structure is exactly a region quadtree (or octree for higher dimensional data). We describe efficient algorithms for inserting and deleting points in a skip quadtree, as well as fast methods for performing point location and approximate range queries. 1
Optimal External Memory Interval Management
, 2002
"... In this paper we present the external interval tree, an optimal external memory data structure for answering stabbing queries on a set of dynamically maintained intervals. The external interval tree can be used in an optimal solution to the dynamic interval management problem, which is a central pro ..."
Abstract

Cited by 32 (6 self)
 Add to MetaCart
In this paper we present the external interval tree, an optimal external memory data structure for answering stabbing queries on a set of dynamically maintained intervals. The external interval tree can be used in an optimal solution to the dynamic interval management problem, which is a central problem for objectoriented and temporal databases and for constraint logic programming. Part of the structure uses a novel weightbalancing technique for efficient worstcase manipulation of balanced trees of independent interest. The external interval tree, as well at our new balancing technique, have recently been used to develop several efficient external data structures.
BoxTrees and Rtrees with NearOptimal Query Time
, 2001
"... A boxtree is a boundingvolume hierarchy that uses axisaligned boxes as bounding volumes. The query complexity of a boxtree with respect to a given type of query is the maximum number of nodes visited when answering such a query. We describe several new algorithms for constructing boxtrees with ..."
Abstract

Cited by 31 (6 self)
 Add to MetaCart
A boxtree is a boundingvolume hierarchy that uses axisaligned boxes as bounding volumes. The query complexity of a boxtree with respect to a given type of query is the maximum number of nodes visited when answering such a query. We describe several new algorithms for constructing boxtrees with small worstcase query complexity with respect to queries with axisparallel boxes and with points. We also prove lower bounds on the worstcase query complexity for boxtrees, which show that our results are optimal or close to optimal. Finally, we present algorithms to convert boxtrees to Rtrees, resulting in Rtrees with (almost) optimal query complexity. 1
ExternalMemory Algorithms with Applications in Geographic Information Systems
 Algorithmic Foundations of GIS
, 1997
"... In the design of algorithms for largescale applications it is essential to consider the problem of minimizing Input/Output (I/O) communication. Geographical information systems (GIS) are good examples of such largescale applications as they frequently handle huge amounts of spatial data. In this n ..."
Abstract

Cited by 27 (9 self)
 Add to MetaCart
In the design of algorithms for largescale applications it is essential to consider the problem of minimizing Input/Output (I/O) communication. Geographical information systems (GIS) are good examples of such largescale applications as they frequently handle huge amounts of spatial data. In this note we survey the recent developments in externalmemory algorithms with applications in GIS. First we discuss the AggarwalVitter I/Omodel and illustrate why normal internalmemory algorithms for even very simple problems can perform terribly in an I/Oenvironment. Then we describe the fundamental paradigms for designing I/Oefficient algorithms by using them to design efficient sorting algorithms. We then go on and survey externalmemory algorithms for computational geometry problems  with special emphasis on problems with applications in GIS  and techniques for designing such algorithms: Using the orthogonal line segment intersection problem we illustrate the distributionsweeping and ...
Kinetic Medians and kdTrees
, 2002
"... We propose algorithms for maintaining two variants of kd trees of a set of moving points in the plane. A pseudo kdtree allows the number of points stored in the two children to di#er by a constant factor. ..."
Abstract

Cited by 22 (8 self)
 Add to MetaCart
We propose algorithms for maintaining two variants of kd trees of a set of moving points in the plane. A pseudo kdtree allows the number of points stored in the two children to di#er by a constant factor.