Gunther, O. The Design of the Cell Tree: An Object-Oriented Index Structure for Geometric Databases. Proc. 5th International Conference on Data Engineering, 1989, pp. 508-605.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....approximations have the potential of improving the e#ciency. Such approximations include circles, ellipses, rectilinear polygons, rotated mbbs, trapezoids, k corner bounding convex polygons, and convex hull. Indexing techniques including sphere trees [14] and SS Trees [20] for circles, cell trees [7] and polyh edratree [10] for convex polygons, and extended R # trees for di#erent approximation methods [3] were developed for some of these approximations. It is not hard to believe that finer approximations than mbbs can also be used for trajectories in order to improve query performance. The ....

....on the new approximation are more e#cient than that for mbbs. Previous study in spatial databases focused on approximating 2 d spatial objects. Di#erent approximations have been proposed and data structures were developed for them: e.g. sphere trees [14] and SS Trees [20] for circles, Cell trees [7] and polyhedra tree [10] for convex polygons. 3] compared mbb, rotated mbb, minimum bounding circle, minimum bounding ellipse, convex hull (ch) and minimum bounding k corner convex polygon (k cn) based on point location region queries for 2 d region data. R # trees augmented by approximations ....

O. Gunther. The design of the cell tree: An object-oriented index structure for geometric databases. In Proc. ICDE, 1989.

....different page sizes. These data pages were then processed to generate the global distribution or sampling distribution, depending on the size of the data sets. We compared different data page clustering schemes: Connectivity Clustered Access Method(CCAM) 15] Z ordering [10] and Cell tree [5]. Other parameters of interest were the size of the memory buffer, the memory block size (page size) the number of neighbors, and the neighborhood depth. The measures of our experiments were the CRR value and I O cost for each outlier detection procedure. The experiments were conducted on many ....

....the points. The Z order of a coordinate (z, is computed by interweaving the bits in the binary representation of the two values. Alternatively, Hilbert ordering may be used. A conventional onedimensional primary index (e.g. B tree) can be used to facilitate the search. Cell Tree: A cell tree [5] is a height balanced tree. Each cell tree node corresponds not necessarily to a rectangular box but to a convex polyhedron. A cell tree restricts polyhedra to partitions ofa BSP(Binary Space Partitioning) in order to avoid overlaps among sibling polyhedra. Each cell tree node corresponds to one ....

O. Gunther, The Design of the Cell Tree: An Object-Oriented Index Structure for Geometric Databases, in: Proc. 5th Intl. Conference on Data Engineering, Feb. 1989.

....computation proce cost. The experiments were conducted on many spatial framew, input data set, a Twin Cities Minnesota Department of ng different page clustering ssed to generate sets of pages result computation algorithm. gate functions to be used by rs. s: the connectivity clustered :ee [6]. Other parameters of strategies, the memory block ental measures for the model res are the CE value and I O )rks. We present the results on ( CAM Page th fiji No of Buffering Z order Size Si e Neighbors Cell tre Highway of data Comlectivily Graph (ROD) Node Sets of pages [ Test Par ....

....index for accessing the dta file. The nodes of different partitions are (0,1,3,4) 5,6,7,8) 9,11,12,13) and (14,15,18,26) Figure 8. 16 18 24 2 3 (a) Z order Z order and Cell tree clustering methods. 3 5 2 4 0 HI: 18 I 2 (b) Ccl14rce 26( 5.2.3. Cell tree. A Cell tree [6] is a height balanc( corresponds not necessarily to a rectangular box but to a restricts polyhedra to partitions of a binary space partit: overlaps among sibling polyhedra. Each cell tree node. and the leaf nodes contain all the information required t The Cell tree can be viewed as a combination ....

O. Gunther. "The design of the cell tree: An object-oriented index structure for geometric databases," in Proc. 5th Intl. Cot[krence on Data Engineering, February 1989. [

....data pages using di erent clustering strategies and page sizes. These data pages were then processed to generate the global distribution or sampling distribution, depending on the size of the data sets. We compared di erent data page clustering schemes: CCAM [13] Z ordering [9] and Cell tree [4]. Other parameters of interest were the size of the memory bu er, the bu ering strategies, the memory block size(page size) and the number of neighbors. The measures of our experiments were the CRR values and I O cost for each outlier detection procedure. Clustering method Page Size Sets of ....

....the points. The Z order of a coordinate (#,#) is computed byinterweaving the bits in the binary representation of the twovalues. Alternatively, Hilbert ordering may be used. A conventional one dimensional primary index (e.g. # tree) can be used to facilitate the search. Cell Tree: A cell tree [4] is a height balanced tree. Each cell tree node corresponds, not necessarily to a rectangular box, but to a convex polyhedron. A cell tree restricts polyhedra to partitions of a BSP(Binary Space Partitioning) to avoid overlaps among sibling polyhedra. Each cell tree node corresponds to one disk ....

O. Gunther. The Design of the Cell Tree: An Object-Oriented Index Structure for Geometric Databases. In Proc. 5th Intl. Conference on Data

.... cost of outlier detection algorithms are dominated by the disk page access time (i.e. the time spent on accessing neighbors of each point) In this study we utilized three different data page clustering schemes: the Connectivity Clustered Access Method (CCAM) 27] Z ordering [23] and Cell tree [10] and found that CCAM produced the lowest number of data page accesses for outlier detection. The effectiveness of the Zs(x) method on a Minneapolis St. Paul traffic data set is illustrated in the following example. Figure 7 shows one example of traffic flow outliers. Figures 7(a) and (b) are the ....

O. Gunther. The Design of the Cell Tree: An Object-Oriented Index Structure for Geometric Databases. In Proc. 5th Intl. Conference on Data Engineering, Feb. 1989.

....which do not have good theoretical worst case bounds but have good average case behavior for common spatial database problems. These includes the grid file [NHS] various quad trees [Sama, Samb] z orders [Ore] and other space filling curves, k d B trees [Rob] hB trees [LoS] cell trees [Gun], and various R trees [Gut, SRF] For these external data structures there has been a lot of experimentation but relatively little algorithmic analysis. Their average case performance (e.g. some achieve the desirable static query I O time of O(log B n t=B) on average inputs) is heuristic and ....

O. Gunther, "The Design of the Cell Tree: An Object-Oriented Index Structure for Geometric Databases," Proc. of the fifth Int. Conf. on Data Engineering (1989), 598--605.

....pages using different clustering strategies and page sizes. These data pages were then processed to generate the global distribution or sampling distribution, depending on the size of the data sets. We compared different data page clustering schemes: CCAM [15] Z ordering [10] and Cell tree [5]. Other parameters of interest were the size of the memory buffer, the buffering strategies, the memory block size(page size) and the number of neighbors. The measures of our experiments were the CRR values and I O cost for each outlier detection procedure. Clustering method Page Size Sets ....

....points. The Z order of a coordinate (x,y) is computed by interweaving the bits in the binary representation of the two values. Alternatively, Hilbert ordering may be used. A conventional one dimensional primary index (e.g. B tree) can be used to facilitate the search. Cell Tree: A cell tree [5] is a height balanced tree. Each cell tree node corresponds, not necessarily to a rectangular box, but to a convex polyhedron. A cell tree restricts polyhedra to partitions of a BSP(Binary Space Partitioning) to avoid overlaps among sibling polyhedra. Each cell tree node corresponds to one disk ....

O. Gunther. The Design of the Cell Tree: An Object-Oriented Index Structure for Geometric Databases. In Proc. 5th Intl. Conference on Data Engineering, Feb. 1989.

....The Model Building algorithm computes the algebraic aggregate functions to be used by the Test Result Computation algorithm to detect spatial outliers. We compared three different data page clustering schemes: the ConnectivityClustered Access Method (CCAM) 25] Z ordering [19] and Cell tree [7]. Other parameters of interest were the size of the memory buffer, the buffering strategies, the memory block size (page size) and the number of neighbors. The experimental measures for the Model Building procedure and the Test Result Computation procedures are the CE value and I O cost. ....

.... tree) can be used to facilitate a search. Figure 10 shows an example of using Z order as the page clustering method and B tree as the primary index for accessing the data file. The nodes of different partitions are (0,1,3,4) 5,6,7,8) 9,11,12,13) and (14,15,18,26) Cell Tree: A Cell tree [7] is a height balanced tree. Each cell tree node corresponds not necessarily to a rectangular box but to a convex polyhedron. A cell tree restricts polyhedra to partitions of a BSP (Binary Space Partitioning) in order to avoid overlaps among sibling polyhedra. Each cell tree node corresponds to one ....

O. Gunther. The Design of the Cell Tree: An Object-Oriented Index Structure for Geometric Databases. In Proc. 5th Intl. Conference on Data Engineering, Feb. 1989.

....extensively researched problem. A large number of structures have been developed for this problem, including space filling curves (see e.g. 123, 1, 32] grid files [119, 94] various quadtrees [133, 134] kd B tress [128] and variants like Buddy trees [138] hB trees [109, 75] and cell trees [91] and various R trees [92, 88, 139, 37, 100] Often these structures are broadly classified into two types, namely space driven structures (like quad trees and grid files) which partition the embedded space containing the data points and data driven structures (like kd B trees and R trees) ....

O. Gunther. The design of the cell tree: An object-oriented index structure for geometric databases. In Proc. IEEE International Conference on Data Engineering, pages 598--605, 1989.

....The Model Building algorithm computes the algebraic aggregate functions to be used by the Test Result Computation algorithm to detect spatial outliers. We compared three different data page clustering schemes: the ConnectivityClustered Access Method (CCAM) 24] Z ordering [18] and Cell tree [6]. Other parameters of interest were the size of the memory buffer, the buffering strategies, the memory block size (page size) and the number of neighbors. The experimental measures for the Model Building procedure and the Test Result Computation procedures are the CE value and I O cost. ....

....can 24 be used to facilitate a search. Figure 8(a) shows an example of using Z order as the page clustering method and B tree as the primary index for accessing the data file. The nodes of different partitions are (0,1,3,4) 5,6,7,8) 9,11,12,13) and (14,15,18,26) Cell Tree: A Cell tree [6] is a height balanced tree. Each cell tree node corresponds not necessarily to a rectangular box but to a convex polyhedron. A cell tree restricts polyhedra to partitions of a BSP (Binary Space Partitioning) in order to avoid overlaps among sibling polyhedra. Each cell tree node corresponds to one ....

O. Gunther. The Design of the Cell Tree: An Object-Oriented Index Structure for Geometric Databases. In Proc. 5th Intl. Conference on Data Engineering, Feb. 1989.

....data pages using di erent clustering strategies and page sizes. These data pages were then processed to generate the global distribution or sampling distribution, depending on the size of the data sets. We compared di erent data page clustering schemes: CCAM [13] Z ordering [9] and Cell tree [4]. Other parameters of interest were the size of the memory bu er, the bu ering strategies, the memory block size(page size) and the number of neighbors. The measures of our experiments were the CRR values and I O cost for each outlier detection procedure. Clustering method Page Size Sets of ....

....points. The Z order of a coordinate (x,y) is computed by interweaving the bits in the binary representation of the two values. Alternatively, Hilbert ordering may be used. A conventional one dimensional primary index (e.g. B tree) can be used to facilitate the search. Cell Tree: A cell tree [4] is a height balanced tree. Each cell tree node corresponds, not necessarily to a rectangular box, but to a convex polyhedron. A cell tree restricts polyhedra to partitions of a BSP(Binary Space Partitioning) to avoid overlaps among sibling polyhedra. Each cell tree node corresponds to one disk ....

O. Gunther. The Design of the Cell Tree: An Object-Oriented Index Structure for Geometric Databases. In Proc. 5th Intl. Conference on Data Engineering, Feb. 1989.

....data pages using different clustering strategies and page sizes. These data pages were then precessed to generate the global distribution or sampling distribution, depending on the size of the data sets. We compared different data page clustering schemes CCAM [16] Z ordering [11] and Celltree [5]. Other parameters of interest were the size of the memory buffer, the buffering strategies, the memory block size(page size) and the number of neighbors. The measures of our experiments are the CRR values and I O cost for each outlier detection procedures. Clustering method Page Size Sets ....

....the points. The Z order of a coordinate (x,y) is computed by interweaving the bits in the binary representation of the two values. Alternatively, Hilbert ordering may be used. A conventional one dimensional primary index (e.g. B tree) can be used to facilitate search. Cell Tree: A Cell tree [5] is a height balanced tree. Each cell tree node corresponds, not necessarily to a rectangular box, but to a convex polyhedron. A cell tree restricts polyhedra to partitions of a BSP(Binary Space Partitioning) to avoid overlaps among sibling polyhedra. Each cell tree node corresponds to one disk ....

O. Gunther. The Design of the Cell Tree: An Object-Oriented Index Structure for Geometric Databases. In Proc. 5th Intl. Conference on Data Engineering, Feb. 1989.

.... the case of representing the same polygon by its minimum enclosing rectangle (e.g. the R tree [17] An alternative way is to represent a spatial object by more than one entity inside the data structure, e.g. by partitioning the spatial object into a collection of convex polygons (the cell tree [15]) a collection of square blocks (the quadtree [21] or a collection of rectangles (the R tree [12] In some of these data structures a spatial object is represented by its internal region, i.e. based on the spatial occupancy of the object. Examples of data structures that make use of this ....

O. Gunther. The design of the cell tree: an objectoriented index structure for geometric databases. In Proc. of the 5th IEEE Intl. Conf. on Data Engr., pp. 598--605, Los Angeles, Feb. 1989.

....cases. These structures are not efficient when mapped to external memory. However, the practical need for I O support has led to the development of a large number of external data structures, which have good average case behavior for common problems but fail to be efficient in the worst case sense [68, 69, 86, 95, 103, 111, 114, 115, 117]. Recently some progress has been made on the construction of external two dimensional range searching structures with good worst case performance. In Figure 3.8 the different special cases of general two dimensional range searching are shown. As discussed in [79] it is easy to realize that the ....

....that do not have good theoretical worst case update and query I O bounds, but do have good average case behavior for common problems. Such methods include the grid file [95] various quad trees [114, 115] z orders [103] and other space filling curves, k d B tress [111] hB trees [86] cell trees [68], and various R trees [69, 117] The worst case performance of these data structures is much worse than the optimal bounds achievable for dynamic external 1 dimensional range searching using B trees (see [79] for a complete reference on the field) Recently some progress has been made on the ....

O. Gunther. The design of the cell tree: An object-oriented index structure for geometric databases. In Proc. of the fifth Int. Conf. on Data Engineering, pages 598--605, 1989.

....that do not have good theoretical worstcase update and query I O bounds, but do have good average case behavior for common problems. Such methods include the grid file [29] various quad trees [38, 39] z orders [31] and other space filling curves, k d B tress [36] hB trees [26] cell trees [17], and various R trees [18, 40] The worstcase performance of these data structures is much worse than the optimal bounds achievable for dynamic external 1 dimensional range searching using B trees (see [23] for a complete reference on the field) Recently some progress has been made on the ....

O. Gunther. The design of the cell tree: An object-oriented index structure for geometric databases. In Proc. of the fifth Int. Conf. on Data Engineering, pages 598--605, 1989.

....large sets of multi dimensional (spatial) data, such as points or regions, is of crucial importance in several applications, including Spatial, Image or Multimedia Database Systems. In recent years, several data structures have been developed for point [Niev84, Free87, Henr89] and non point [Gutt84, Sell87, Gunt89, Beck90] spatial objects. All these indexing methods use several heuristics to index spatial data efficiently. The large number of spatial data structures proposed indicate that, today, research in this field should turn to the development of powerful analytical models that predict the performance of a ....

....locality. Buckets divide the work space in disjoint or overlapping bucket regions. Disjointness of bucket regions is a rule for point data structures, such as Grid file [Niev84] and variants, but for non point data structures it is the exception of the rule (R tree [Sell87] and Cell tree [Gunt89] are examples of this type) Most non point data structures, such as R tree [Gutt84] and variants based on the 3 original method, support overlapping bucket regions in order to avoid duplicates which grow up the tree structure. Non point objects are usually represented by their Minimum Bounding ....

O. Gunther, "The Design of the Cell Tree: An Object-Oriented Index Structure for Geometric Databases", Proceedings of the IEEE 5th Data Engineering Conference, 1989. 16

...., 1 i k, intersects with exactly one disjoint region. These rectangles are represented in a file and may be organized by any multidimensional PAM, like the grid file [Niev84] or the K DB tree [Robi81] Examples of SAMs using the clipping technique are the R tree [Sell87] and the Cell tree [Gunt89]. Since these structures avoid overlapping regions the search operation is very efficient. Additionally, the properties of the underlying PAM are inherited. However, a drawback is obviously the significant data replication, which increases the index size and degrades the performance of insertion ....

O. Gunther: `The Design of the Cell-tree: An Object-Oriented Index Structure for Geometric Databases', Proceedings of the IEEE 5th Conference on Data Engineering, Los Angeles, California, 598-605, 1989.

....and associate the two polygons with the children of v. This idea is the same as in binary space partition trees [133, 214] Again, one can construct a B tree on this recursive partitioning scheme to reduce the number of disk accesses. The resulting structure called cell trees is studied in [140, 141]. All the data structures described in this section construct a recursive partition of the space. There are other data structures (of which the R tree is perhaps the most famous example) that construct a hierarchical cover of the space. We will discuss some of these data structures in the next ....

O. Gunther, The design of the cell tree: An object oriented index structure for geometric data bases, Proc. 5th IEEE Internat. Conf. on Data Engineering, 1989, pp. 598--605.

....the records in a page, and either one may be chosen. We refer to the above approach as a balance based split policy for Grid file. A connectivitybased policy for the Grid file uses the connectivity information and chooses the split dimension which has a higher WCRR. 5.2. 2 Cell Tree The cell tree [16, 17] is a height balanced tree. Each cell tree node corresponds, not necessarily to a rectangular box, but to a convex polyhedron. The cell tree restricts the polyhedra to be partitions of a BSP (binary space partitioning) to avoid overlaps among sibling polyhedra. Each cell tree node corresponds to ....

O. Gunther. "The Design of the Cell Tree: An Object-Oriented Index Structure for Geometric Databases". In Proc. 5th Intl Conference on Data Engineering, Feb. 1989.

....cases. These structures are not e#cient when mapped to external memory. However, the practical need for I O support has led to the development of a large number of external data structures, which have good average case behavior for common problems but fail to be e#cient in the worst case sense [68, 69, 86, 95, 103, 111, 114, 115, 117]. Recently some progress has been made on the construction of external two dimensional range searching structures with good worst case performance. In Figure 3.8 the di#erent special cases of general two dimensional range searching are shown. As discussed in [79] it is easy to realize that the ....

....that do not have good theoretical worst case update and query I O bounds, but do have good average case behavior for common problems. Such methods include the grid file [95] various quad trees [114, 115] z orders [103] and other space filling curves, k d B tress [111] hB trees [86] cell trees [68], and various R trees [69, 117] The worst case performance of these data structures is much worse than the optimal bounds achievable for dynamic external 1 dimensional range searching using B trees (see [79] for a complete reference on the field) Recently some progress has been made on the ....

O. Gunther. The design of the cell tree: An object-oriented index structure for geometric databases. In Proc. of the fifth Int. Conf. on Data Engineering, pages 598--605, 1989.

....for 2 dimensional range searching and its special cases (see [7] for a detailed survey) Most of these algorithms are not efficient when mapped to secondary storage. However, the practical need for good I O support has led to the development of a large number of empirical external data structures[15,16,21,23,24,28,29,30,32] which do not have good theoretical worst case bounds but have good average case behavior for common spatial database problems. The worst case performance of these data structures is much worse than the optimal bounds achievable for dynamic external 1 dimensional range searching using B ....

O. Gunther, "The Design of the Cell Tree: An Object-Oriented Index Structure for Geometric Databases," Proc. of the fifth Int. Conf. on Data Engineering (1989), 598--605.

....p B) Theta O( p B) and range searching is inefficient. That is, we would read O(t= p B) disk blocks to report t points on a straight line. Several data structures have been proposed in the literature to handle region data. These include the R tree [17] the R tree [35] the cell tree [16] and many others. These data structures are not directly applicable to point data. However, they can deal with one dimensional range data and hence are relevant to our problem. All of them are based on the recursive decomposition of space using heuristics and cannot offer the worst case guarantees ....

O. Gunther, "The Design of the Cell Tree: An Object-Oriented Index Structure for Geometric Databases," Proc. of the Fifth Int. Conf. on Data Engineering (1989).

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Gunther, O. The Design of the Cell Tree: An Object-Oriented Index Structure for Geometric Databases. Proc. 5th International Conference on Data Engineering, 1989, pp. 508-605.

No context found.

O. Gunther. The Design of the Cell Tree: An Object-Oriented Index Structure for Geometric Databases. In Proc. Fifth IEEE International Conference on Data Engineering, Los Angeles, CA, USA, February 1989.

No context found.

O. Gunther. The Design of the Cell Tree: An Object-Oriented Index Structure for Geometric Databases. In Proc. 5th Intl. Conference on Data Engineering, Feb. 1989.

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