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43
Multidimensional Access Methods
, 1998
"... Search operations in databases require special support at the physical level. This is true for conventional databases as well as spatial databases, where typical search operations include the point query (find all objects that contain a given search point) and the region query (find all objects that ..."
Abstract

Cited by 561 (3 self)
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Search operations in databases require special support at the physical level. This is true for conventional databases as well as spatial databases, where typical search operations include the point query (find all objects that contain a given search point) and the region query (find all objects that overlap a given search region). More
Partition Based SpatialMerge Join
, 1996
"... This paper describes PBSM (Partition Based SpatialMerge), a new algorithm for performing spatial join operation. This algorithm is especially effective when neither of the inputs to the join have an index on the joining attribute. Such a situation could arise if both inputs to the join are interme ..."
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Cited by 166 (9 self)
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This paper describes PBSM (Partition Based SpatialMerge), a new algorithm for performing spatial join operation. This algorithm is especially effective when neither of the inputs to the join have an index on the joining attribute. Such a situation could arise if both inputs to the join are intermediate results in a complex query, or in a parallel environment where the inputs must be dynamically redistributed. The PBSM algorithm partitions the inputs into manageable chunks, and joins them using a computational geometry based planesweeping technique. This paper also presents a performance study comparing the the traditional indexed nested loops join algorithm, a spatial join algorithm based on joining spatial indices, and the PBSM algorithm. These comparisons are based on complete implementations of these algorithms in Paradise, a database system for handling GIS applications. Using real data sets, the performance study examines the behavior of these spatial join algorithms in a vari...
Estimating the Selectivity of Spatial Queries Using the `Correlation' Fractal Dimension
, 1995
"... We examine the estimation of selectivities for range and spatial join queries in real spatial databases. As we have shown earlier [FK94a], real point sets: (a) violate consistently the "uniformity" and "independence" assumptions, (b) can often be described as "fractals", with noninteger (fractal) d ..."
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Cited by 121 (16 self)
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We examine the estimation of selectivities for range and spatial join queries in real spatial databases. As we have shown earlier [FK94a], real point sets: (a) violate consistently the "uniformity" and "independence" assumptions, (b) can often be described as "fractals", with noninteger (fractal) dimension. In this paper we show that, among the infinite family of fractal dimensions, the so called "Correlation Dimension" D 2 is the one that we need to predict the selectivity of spatial join. The main contribution is that, for all the real and synthetic pointsets we tried, the average number of neighbors for a given point of the pointset follows a power law, with D 2 as the exponent. This immediately solves the selectivity estimation for spatial joins, as well as for "biased" range queries (i.e., queries whose centers prefer areas of high point density). We present the formulas to estimate the selectivity for the biased queries, including an integration constant (K `shape 0 ) for ea...
Spatial HashJoins
, 1996
"... The hashjoin paradigm works well for relational joins, but is hard to apply to spatial joins. Relational hashjoins can guarantee that items in different hash buckets are irrelevant to each other for the purpose of join, but complexities intrinsic to spatial join predicates preclude such guarantees ..."
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Cited by 95 (1 self)
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The hashjoin paradigm works well for relational joins, but is hard to apply to spatial joins. Relational hashjoins can guarantee that items in different hash buckets are irrelevant to each other for the purpose of join, but complexities intrinsic to spatial join predicates preclude such guarantees. It is also difficult to design spatial partition functions that produce equalsized buckets. We examine how to apply the hashjoin paradigm to spatial joins, and define a new framework for spatial hashjoins. Our spatial partition functions have two components: a set of bucket extents and an assignment function, which may map a data item into multiple buckets. Furthermore, the partition functions for the two input datasets may be different. We have designed and tested a spatial hashjoin method based on this framework. The partition function for the inner dataset is initialized by sampling the dataset, and evolves as data are inserted. The partition function for the outer dataset is immutab...
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 ..."
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Cited by 81 (36 self)
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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.
Scalable sweepingbased spatial join
 IN PROC. 24TH INT. CONF. VERY LARGE DATA BASES, VLDB
, 1998
"... In this paper, we consider the filter step of the spatial join problem, for the case where neither of the inputs are indexed. We present a new algorithm, Scalable SweepingBased Spatial Join (SSSJ), that achieves both efficiency on reallife data and robustness against highly skewed and worstcase d ..."
Abstract

Cited by 64 (7 self)
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In this paper, we consider the filter step of the spatial join problem, for the case where neither of the inputs are indexed. We present a new algorithm, Scalable SweepingBased Spatial Join (SSSJ), that achieves both efficiency on reallife data and robustness against highly skewed and worstcase data sets. The algorithm combines a method with theoretically optimal bounds on I/O transfers based on the recently proposed distributionsweeping technique with a highly optimized implementation of internalmemory planesweeping. We present experimental results based on an efficient implementation of the SSSJ algorithm, and compare it to the stateoftheart PartitionBased SpatialMerge (PBSM) algorithm of Pate1 and DeWitt.
Indexing Animated Objects Using Spatiotemporal Access Methods
 IEEE Transactions on Knowledge and Data Engineering
, 2001
"... AbstractÐWe present a new approach for indexing animated objects and efficiently answering queries about their position in time and space. In particular, we consider an animated movie as a spatiotemporal evolution. A movie is viewed as an ordered sequence of frames, where each frame is a 2D space oc ..."
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Cited by 48 (7 self)
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AbstractÐWe present a new approach for indexing animated objects and efficiently answering queries about their position in time and space. In particular, we consider an animated movie as a spatiotemporal evolution. A movie is viewed as an ordered sequence of frames, where each frame is a 2D space occupied by the objects that appear in that frame. The queries of interest are range queries of the form, ªfind the objects that appear in area S between frames fi and fjº as well as nearest neighbor queries such as, ªfind the q nearest objects to a given position A between frames fi and fj.º The straightforward approach to index such objects considers the frame sequence as another dimension and uses a 3D access method (such as, an RTree or its variants). This, however, assigns long ªlifetimeº intervals to objects that appear through many consecutive frames. Long intervals are difficult to cluster efficiently in a 3D index. Instead, we propose to reduce the problem to a partialpersistence problem. Namely, we use a 2D access method that is made partially persistent. We show that this approach leads to faster query performance while still using storage proportional to the total number of changes in the frame evolution. What differentiates this problem from traditional temporal indexing approaches is that objects are allowed to move and/or change their extent continuously between frames. We present novel methods to approximate such object evolutions. We formulate an optimization problem for which we provide an optimal solution for the case where objects move linearly. Finally, we present an extensive experimental study of the proposed methods. While we concentrate on animated movies, our approach is general and can be applied to other spatiotemporal applications as well. Index TermsÐAccess methods, spatiotemporal databases, animated objects, multimedia. 1
Querying Mobile Objects in SpatioTemporal Databases
, 2001
"... . In dynamic spatiotemporal environments where objects may continuously move in space, maintaining consistent information about the location of objects and processing motionspecific queries is a challenging problem. In this paper, we focus on indexing and query processing techniques for mobile ..."
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Cited by 34 (1 self)
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. In dynamic spatiotemporal environments where objects may continuously move in space, maintaining consistent information about the location of objects and processing motionspecific queries is a challenging problem. In this paper, we focus on indexing and query processing techniques for mobile objects. Specifically, we develop a classification of different types of selection queries that arise in mobile environments and explore efficient algorithms to evaluate them. Query processing algorithms are developed for both native space and parametric space indexing techniques. A performance study compares the two indexing strategies for different types of queries. 1
DOT: A spatial access method using fractals
 Proc. 7th IEEE Internat. Conf. on Data Engineering
, 1991
"... Existing Database Management Systems (DBMSs) do not handle efficiently multidimensional data such as boxes, polygons, or even points in a multidimensional space. We examine access methods for these data with two design goals in mind: (a) efficiency in terms of search speed and space overhead and ( ..."
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Cited by 34 (1 self)
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Existing Database Management Systems (DBMSs) do not handle efficiently multidimensional data such as boxes, polygons, or even points in a multidimensional space. We examine access methods for these data with two design goals in mind: (a) efficiency in terms of search speed and space overhead and (b) ability to be integrated in a DBMS easily. We propose a method to map multidimensional objects into points in a 1dimensional space; thus, traditional primarykey access methods can be applied, with very few extensions on the part of the DBMS. We propose such mappings based on fractals; we implemented the whole method on top of a B +tree, along with several mappings. Simulation experiments on several distributions of the input data show
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 ..."
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Cited by 31 (6 self)
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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