Rigaux, P., Scholl, M., Vorsard, A.: Spatial Database with Application to GIS. Academic Press, ISBN 155860 -588-6/G70.212.R54 (2002).  Home/Search   Document Not in Database   Summary   Related Articles   Check

....range search, closest pairs and edistance joins, in the context of spatial network databases. 1. Introduction Spatial databases have been well studied in the last 20 years resulting in the development of numerous conceptual models, multi dimensional indexes and query processing techniques [RSV02]. Surprisingly, most of existing work considers Cartesian (typically, Euclidean) spaces, where the distance between two objects is determined solely by their relative position in space. However, in practice, objects can usually move only on a pre defined set of trajectories as specified by the ....

....[BKS93] traverses synchronously the two trees, following entry pairs that overlap; non intersecting pairs cannot lead to solutions at the lower levels. Several spatial join algorithms have been proposed for the case where only one of the inputs is indexed by an R tree or no input is indexed [RSV02]. For point datasets, where intersection joins are meaningless, the corresponding problem is the edistance join, which finds all pairs of objects (s,t) s S, t T within (Euclidean) distance e from each other. R tree join can be applied in this case as well, the only difference being that a ....

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Rigaux, P. Scholl, M. Voisard, A. Spatial Databases with Application to GIS. Morgan Kaufmann, 2002.

....search trees to facilitate the correlation based similarity query processing in spatial time series data. Scope and Outline In this paper we choose a simple quad tree like structure as the search tree due to its simplicity. R tree, k d tree, z ordering tree and their variations [16, 19, 18] could be other possible candidates of the search tree. However, the comparison of these spatial data structures is beyond the scope of this paper. We focus on the strategies for correlation based similarity queries in spatial time series data, and the computation saving methods we examine involve ....

....Tree Formation We explore spatial autocorrelation, i.e. the in uence of neighboring regions on each other, to form a search tree. Search tree structures have been widely used in traditional DBMS (e.g. B tree and B tree) and spatial DBMS (quad tree, R tree, R tree, R tree, and R link tree [16, 19] ) To fully exploit the spatial autocorrelation property, there are three major criteria for choosing a tree on the spatial time series datasets. First, a spatial tree structure is preferred to incorporate the spatial component of the datasets. Second, during the tree formation the time series ....

P. Rigaux, M. Scholl, and A. Voisard. Spatial Databases: With Application to GIS. Morgan Kaufmann Publishers, 2001.

....search trees to facilitate the correlation based similarity query processing in spatial time series data. Scope and Outline In this paper we choose a simple quad tree like structure as the search tree due to its simplicity. R tree, k d tree, z ordering tree and their variations [16, 19, 18] could be other possible candidates of the search tree. However, the comparison of these spatial data structures is beyond the scope of this paper. We focus on the strategies for correlation based similarity queries in spatial time series data, and the computation saving methods we examine involve ....

....Tree Formation We explore spatial autocorrelation, i.e. the influence of neighboring regions on each other, to form a search tree. Search tree structures have been widely used in traditional DBMS (e.g. B tree and B tree) and spatial DBMS (quad tree, R tree, R tree, R tree, and R link tree [16, 19] ) To fully exploit the spatial autocorrelation property, there are three major criteria for choosing a tree on the spatial time series datasets. First, a spatial tree structure is preferred to incorporate the spatial component of the datasets. Second, during the tree formation the time series ....

P. Rigaux, M. Scholl, and A. Voisard. Spatial Databases: With Application to GIS. Morgan Kaufmann Publishers, 2001.

....that can be composed of one or many feature classes. Data sets composed in this way form a feature catalogue [9] Available feature catalogues are also modelled as data components within the GSI system. The basic types for spatial data types are given below using an abstract data type notation [8, 15]: Type constructors Geobj points, lines, regions Geobj Theme theme Theme Comp comp Geographic object types (Geobj) are used to represent single entities in the real world. A geographic object has two main characteristics: a description part or set of attributes that define its nature, ....

P. Rigaux, M. Scholl, and A. Voisard. Spatial Databases: with Application to GIS. The Addison-Wesley object technology series. Morgan Kaufman Publishers, San Francisco, California, 2001.

....search trees to facilitate the correlation based similarity query processing in spatial time series data. Scope and Outline In this paper we choose a simple quad tree like structure as the search tree due to its simplicity. R tree, k d tree, z ordering tree and their variations [20, 24, 23] could be other possible candidates of the search tree. However, the comparison of these spatial data structures is beyond the scope of this paper. We focus on the strategies for correlation based similarity queries in spatial time series data, and the computation saving methods we examine involve ....

....Search Tree Formation We explore the spatial autocorrelation, i.e. neighboring regions in uence each other, to form a search tree. Search tree structures have been widely used in traditional DBMS (e.g. B tree and B [9] and spatial DBMS (quad tree, R tree, R tree, R tree, and R link tree [20, 23, 24] ) To fully exploit the spatial autocorrelation property, there are three criteria for choosing a tree on the spatial time series datasets. First, a spatial tree structure is preferred to incorporate the spatial component of the datasets. Second, during the tree formation the time series ....

P. Rigaux, M. Scholl, and A. Voisard. Spatial Databases: With Application to GIS. Morgan Kaufmann Publishers, 2001.

....The data stored in spatial databases are spatial objects such as locations, road segments, and geographical regions, which can be abstracted as points, polylines and polygons in a 2D or 3D coordinate system. One of the most important operations in spatial databases is geometric selection [RSV01], which in general can be classified as windowing and clipping. Given a set of spatial objects and a query rectangle, a windowing query retrieves all the objects that intersect the query rectangle. A clipping query can be considered as a windowing query with a post processing phase: once the ....

Philippe Rigaux, Michel Scholl, and Agn`es Voisard. Spatial Databases with Applications to GIS, chapter 1.3.1, page 14. Morgan Kaufmann Publishers, 2001.

....not shown the details associated with HORIZONTAL SPATIAL GRANULARITY as they have already been described in Figure 5. In Figure 6, a line starts with a node (i.e. z node start) and ends with a node (i.e. z node end) and has multiple points (i.e. z line points) between the start and end nodes [62]. The associated constraints related to horizontal granularity are identical to the ones already described. The constraints related to vertical spatial granularity are similarly defined as in constraints 6.1.12 6.1.17. Constraint 6.1.19: The horizontal and vertical geometry of borehole site are ....

....constraints among spatial regions could be added, outside the of the graphical schema. The semantics of topological constraints is specified in Table 8, where A denotes the interior of A (where A is a spatial region composed of non empty set of points) and #A denotes the boundary of A [62]. For example A disjoint B implies that intersection of the boundaries and interior is empty (#) # ## #A # ## # # ## #B A # ## # B # ## #A # ## # B A # ## # # ## #B A disjoint B # # # # A meets B # # # # A equals B # # # # A inside B # # # # A covered by B # # # # A contains B # # # # ....

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P. Rigaux, M. O. Scholl and A. Voisard, Spatial Databases: With Application to GIS: Morgan Kaufmann Publishers, 2001.

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Rigaux, P., Scholl, M., Vorsard, A.: Spatial Database with Application to GIS. Academic Press, ISBN 155860 -588-6/G70.212.R54 (2002).

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P. Rigaux, M. Scholl, and A. Voisard. Spatial Databases with Application to GIS. Academic Press, 2002.

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P. Rigaux, M. Scholl, A. Voisard. Spatial Databases with Application to GIS. Morgan Kaufmann, 2002.

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P. Rigaux, M. Scholl, and A. Voisard. Spatial Databases with Application to GIS. Academic Press, 2002.

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P.Rigaux, M.Scholl, A.Voisard. Spatial Databases With Application to GIS. Academic Press, 2002.

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P. Rigaux, M. Scholl, and A. Voisard. Spatial Databases: With Application to GIS. Morgan Kaufmann Publishers, 2001.

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P. Rigaux, M. Scholl, and A. Voisard. Spatial Databases: With Application to GIS. Morgan Kaufmann Publishers, 2001.

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P. Rigaux, M. Scholl, and A. Voisard. Spatial Databases with Application to GIS. Academic Press, 2002.

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Philippe Rigaux, Michel Scholl, and Agnes Voisard. Spatial Databases: With Application to GIS. Morgan Kaufmann Publishers, 2001.

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Ph. Rigaux, M. Scholl, and A. Voisard. Spatial Databases - with applications to GIS. Morgan Kaufmann, 2001.

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