DeWitt, D.J., Kabra, N., Luo, J., Patel, J.M., Yu, J.B.: Client-Server Paradise. Proc. the 20  Home/Search   Document Details and Download   Summary   Related Articles   Check

....The efficiency of the techniques presented is demonstrated using typical queries on large multidimensional data volumes. 1 Introduction Arrays of arbitrary size and dimensionality appear in a large variety of database application fields, e.g. medical imaging, geographic information systems [7], scientific simulations, etc. Recently, integration of an application domain independent and of a generic type constructor for such Multidimensional Discrete Data (MDD) into Database Management Systems (DBMS) has received growing attention. Current scientific contributions in this area mainly ....

DeWitt, D.J., Kabra, N., Luo, J., Patel, J., Yu, J.: Client Server Paradise. In Proc. of the Int. Conf. on Very Large Data Bases (VLDB), Santiago, Chile, 1994

..... 35 10 Conclusions 35 1 Introduction Object data models [1, 5, 36, 4] have been used to model numerous applications ranging from multimedia applications[11, 12] financial risk applications[10] and logistics and supply chain management applications [2] weather applications [14] as well as many others. Many of these applications naturally need to represent and manipulate both time and uncertainty. We first consider a transportation logistics application [8, 2] A commercial package delivery company (such as UPS, Fedex, DHL, etc. has detailed statistical information on ....

D.J. Dewitt, N. Kabra, J. Luo, J. Patel and J. Yu. Client-Server Paradise. In Proceedings 20th VLDB Conference, Santiago, Chile, pages 558--569.

....properly, this would lead to many I O accesses before getting the required query answer. Compare this to the B tree, that in most cases has a height of 2 3 levels, and to the R tree [24] and its variants, the R tree [4] and the R tree [43] that play an important role as spatial database indexes [6, 11, 38]. The second problem is the implementation effort of building indexes. Hard wiring the implementation of a full fledged index structure with the appropriate concurrency and recovery mechanisms into the database engine is a non trivial process. Repeating this process for each spatial tree that can ....

D. J. DeWitt, N. Kabra, J. Luo, J. M. Patel, and J.-B. Yu. Client-Server Paradise. In VLDB'

....reorganization a node fuse constitutes. Constructing an R tree using repeated insertion takes O(N log B N) I Os and does not necessarily result in a good tree in terms of query performance. Therefore several sorting based O( N B log M=B N B ) I O construction algorithms have been proposed [130, 99, 69, 108, 40]. Several of these algorithms produce an R tree with practically better query performance than an R tree built by repeated insertion. Still, no better than a linear worst case query I O bound has been proven for any of them. Very recently, however, de Berg et al. 68] and Agarwal et al. 11] ....

D. J. DeWitt, N. Kabra, J. Luo, J. M. Patel, and J.-B. Yu. Client-server paradise. In Proc. International Conf. on Very Large Databases, pages 558--569, 1994.

.... arrays, why not use these arrays as a storage structure for multi dimensional data sets, and use specialized processing algorithms on these arrays to 1 answer multi dimensional queries To begin to answer this question, we have implemented multi dimensional arrays in the Paradise [DKLPY94] object relational database system, and tested their effectiveness for multi dimensional query workloads. In previous work [ZDN97] we investigated the performance of multi dimensional arrays for the specialized OLAP compute the cube function. In this paper we consider the performance of arrays ....

....identifies a cell in the array in the ADT. Figure 2 shows a small 2 D OLAP Array ADT object in detail. The OLAP Array ADT is built on top of the Paradise multi dimensional array type. The Paradise multidimensional array uses tiling (also called chunking) to make array access more efficient [DKLPY94]. Storing large arrays on disk in row major or column major order may not be efficient because cells that are logically adjacent in the array can be far apart on disk. Tiling, on the other hand, breaks an n dimensional array into n dimensional tiles and stores each tile as a SHORE [CDFHM94] large ....

D. J. DeWitt, N. Kabra, J. Luo, J.M. Patel, and J. Yu. "Client-Server Paradise". In Proceedings of the 20th VLDB Conference, Santiago, Chile, 1994

....Internet applications are presented in section 5.1. 5. THE SPATIAL DATABASE SYSTEM The last component of the architecture presented in section 1 is the spatial database system. Instead of spatial database systems also geographical information systems (GIS) or special geo servers (Friebe, 1999; DeWitt et al., 1994; Brinkhoff et al., 1994b) can be used for storing and retrieving spatial data. These systems may use a spatial and or a relational database system. In order to design a spatial database system, which handles queries from Internet applications efficiently, we have to consider the queries required ....

DeWitt, D.J., Kabra, N., Luo, J., Patel, J.M., and Yu, J.-B. (1994). "Client-Server Paradise", Proceedings 20th International Conference of Very Large Data Bases, 1994, Santiago, Chile, 558-569.

....the process of constructing an external data structure. Since bulk loading an index using repeated insertion is often highly non efficient [4] the development of specialized bulk loading algorithms has received a lot of attention recently. Most work on bulk loading has concentrated on the R tree [35, 24, 17, 30, 41, 13, 16]. Although not optimal, relatively efficient algorithms can often be obtained by constructing an index level by level. 1.2 Our results. In Section 2 of this paper, we define a class of linear space trees for indexing a set of points in R d . These so called wp trees generalize known internal ....

D. J. DeWitt, N. Kabra, J. M. Patel, and J.-B. Yu. Client-server Paradise. In Proc. 19th Intl. Conf. on Very Large Databases, pages 558--569, 1994.

....storage of compressed raster images and a spatial index. The prime example for such a system is the client server 1 This is a technical term for merging and gluing images together. 1057 7149 00 10.00 2000 IEEE 358 IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 9, NO. 3, MARCH 2000 Paradise system [6], a database system designed for GIS type applications. Special care has been taken in Paradise to make processing of satellite image data efficient. For this purpose, a raster image is partitioned into tiles; they form the basic processing units for indexing and for compression. It is argued that ....

D. J. DeWitt, N. Kabra, J. Luo, J. M. Patel, and J.-B. Yu, "Client--server paradise," in Proc. 20th VLDB Conf., 1994, pp. 558--569.

....in section 5.1. 5. THE SPATIAL DATABASE SYSTEM The last component of the architecture presented in section 1 is the spatial database system. Instead of spatial database systems (or on the top of a database system) also geographical information systems (GIS) or special geo servers (Friebe, 1999; DeWitt et al., 1994; Brinkhoff et al., 1994b) can be used for storing and retrieving spatial data. In order to design a spatial database system, which handles queries from Internet applications efficiently, we have to consider the queries required by the applications and to discuss their impacts on the database ....

DeWitt, D.J., Kabra, N., Luo, J., Patel, J.M., Yu, J.-B. (1994). "Client-Server Paradise", Proceedings 20th International Conference of Very Large Data Bases, 1994, Santiago, Chile, 558569.

....and raster is captured by a single datamodel. Queries containing both thematic and geometric primitives can be formulated in one language, and be optimized globally. Object relational systems that extend their data and query model with GIS types and primitives are currently available [19, 5]. Storage and querying of geographic data in an extensible database system still poses severe performance challenges to current database technology. Data stored in a GIS is typically complex of nature Parts of this work were supported by SION grant no. 612 23 431 (long polygons with topological ....

.... and National Sequoia 2000 Benchmarks To demonstrate the feasibility and effectiveness of our approach, we decided to run both the Regional and National Sequoia 2000 Storage Benchmarks [18] Results on the Regional benchmark have been published for both research and commercial GIS database servers [5]. Our implementation of the National benchmark sets a new mile stone for further developments in this area. The implementation of the Monet GIS datatypes makes extensive use of MEL modules. Several modules were implemented to provide for the necessary primitives and search accelerators. The ....

[Article contains additional citation context not shown here]

David J. DeWitt, Navin Kabra, Jun Luo, Jignesh M. Patel, and Jie-Bing Yu. Client-server Paradise. In Proceedings of the 20th VLDB Conference, Santiago, Chile., pages 558--569, September 1994.

....structures for original objects and their decomposed components. In the existing indexing structures, the most popular R tree is selected for storing MBRs. Our indexing structure, however, can be easily extended to other R tree variations. Several spatial database systems such as Paradise[21] and GENESYS[22] already use the R tree as their basic indexing structure. Because the cost of implementing a new indexing structure can be more expensive than the cost of extending an already existing one, adding new features to the existing indexing structure is an excellent alternative. ....

D. J. Dewitt, N. Kabra, J. Luo, J. M. Patel, and J. Yu, "Client-Server Paradise," Proc. of the 20th Int. Conf. on Very Large Data Bases, 1994, pp. 1-12.

....size of the manipulated spatial data sets and the fact that performing a large number of single operations one at a time is simply too inefficient to be of practical use. The most common bulk operation is to create an index for a given data set from scratch often called bulk loading [14]. Supported in part by the U.S. Army Research Office through MURI grantDAAH04 96 1 0013 and by the National Science Foundation through ESS grant EIA 9870734. Part of this work was done while a visiting scholar at Duke University. Supported in part by the U.S. Army Research ....

....rebalancing, have been proposed [15, 26, 9, 18] Bulk loading an R tree with N rectangles using the naive method of repeated insertion takes O(N log B N ) I Os, which has been recognized to be abysmally slow. Several bulk loading algorithms using O( N B log M=B N B )I Oshavebeenproposed [24, 17, 14, 20, 28, 10]. These algorithms are more than a factor of B faster than the repeated insertion algorithm. Most of the proposed algorithms [24, 17, 14,20] work in the same basic way# the input rectangles are sorted according to some global one dimensional criterion (suchasx coordinate [24] the Hilbert value of ....

[Article contains additional citation context not shown here]

D. J. DeWitt, N. Kabra, J. Luo, J. M. Patel, and J.-B. Yu. Client-server paradise. In Proc. 20th Intl. Conf. on Very Large Databases, pages 558--569, 1994.

.... both point and spatial data (data with spatial extents) and is the only multidimensional index 2 structure known to have been incorporated as an access method into a commercial data management system [11] It has also been implemented as a part of the Paradise parallel data management system [4]. Despite the importance of spatial access methods to emerging database applications, and the requirement to address the phantom problem in order to provide transactional access to data using these data structures, surprisingly little research exists on providing phantom protection to retrievals ....

D. J. DeWitt., N. Kabra, J. Luo, J. M. Patel, and J. B. Yu. Client server paradise. In Proceedings of VLDB, September 1994.

.... variants can be used for indexing both point and spatial data and is the only multidimensional index structure known to have been incorporated as an access method into a commercial data management system [9] It has also been implemented as a part of the Paradise parallel data management system [2]. Although we are addressing the problem in the context of R trees, the approach developed in this paper can be applied to other tree based multidimensional access methods as well. Despite the importance of multidimensional access methods to emerging database applications, and the requirement to ....

D. J. DeWitt., N. Kabra, J. Luo, J. M. Patel, and J. B. Yu. Client server paradise. In Proceedings of VLDB, September 1994.

....number of required pages but linear to the number of new tuples. Additionally, the query performance is of crucial interest, depending on clustering and page utilization. When loading indexes, clustering and page utilization might depend on the insertion order of tuples or the data distribution [5], e.g. k d B Trees [19] cannot guarantee a certain page filling degree. But this is not desirable. So far there has been already some work on bulk loading of multidimensional index structures. Various papers exist on this subject for other multidimensional index structures, e.g. for R Trees ....

D. J. DeWitt, N. Kabra, J. Luo, J. M. Patel, and J.-B. Yu. Client-Server Paradise. In J. B. Bocca, M. Jarke, and C. Zaniolo, editors, VLDB'94, Proceedings of 20th International Conference on Very Large Data Bases, September 12-15, 1994, Santiago de Chile, Chile, pages 558--569. Morgan Kaufmann, 1994.

....associated points fall within a given multi dimensional box in the dataset s underlying attribute space. The increasing importance of such datasets has been recognized by the database community and several research and commercial systems have been developed for managing and or visualizing them [4, 8, 13]. These systems, however, focus on lineage management, retrieval and visualization of multi dimensional datasets. They provide little or no support for analyzing or processing these datasets the assumption is that these operations are too application specific to warrant common support. As a ....

D. J. DeWitt, N. Kabra, J. Luo, J. M. Patel, and J.-B. Yu. Client--server Paradise. In Proceedings of the 20th VLDB Conference, pages 558--569. Morgan Kaufmann Publishers, Inc., 1994.

....into tables in a RDBMS language, e.g. SQL3. Conventional databases do not support constructs for spatial data types. The present DBMS with object extensions made it possible to develop various GIS related products on top of the extensions, for example ESRI s Spatial Database Engine [12] Paradise [10]. This approach has several advantages, the major one being that the GIS developer can take advantage of the functionality provided by these object relational databases and can build complex geographic analysis functions. Users also find that this approach allows them to more readily use their ....

D.J.Dewitt, N.Karba, J.Luo, and J.M.Patel. Client server paradise. In In Proceedings of the 20th Int. Conference on Very Large Databases, 1994.

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DeWitt, D.J., Kabra, N., Luo, J., Patel, J.M., Yu, J.B.: Client-Server Paradise. Proc. the 20

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D. DeWitt, N. Kabra, J. Luo, J. Patel and J. Yu. "Client Server Paradise," Proc. of the 20 th VLDB Conference, Santiago, Chile, 1994.

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D. J. DeWitt, N. Kabra, J. Luo, J. M. Patel, and J.-B. Yu. Client-Server Paradise. In VLDB, pages 558--569, Santiago, Chile, 1994.

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D.J. DeWitt, N. Kabra, J. Luo, J.M. Patel and J.-B. Yu: "Client-Server Paradise", Proceedings 20th VLDB Conference, pp.558-569, Santiago, Chile, 1994.

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D.J.Dewitt, N.Karba, J.Luo, and J.M.Patel. Client server paradise. In In Proceedings of the 20th Int. Conference on Very Large Databases, 1994. S. Shekhar et al.

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D. J. DeWitt, N. Kabra, J. Luo, J. M. Patel, and J.-B. Yu. Client-server paradise. In Proc. International Conference on Very Large Databases, pages 558--569, 1994.

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David J. DeWitt, Navin Kabra, Jun Luo, Jignesh M. Patel, and Jie-Bing Yu. Client-server paradise. In VLDB'94, Proceedings of 20th International Conference on Very Large Data Bases, 1994.

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D. J. DeWitt, N. Kabra, J. Luo, J. M. Patel, and J. B. Yu. Client-server paradise. In 20th Intl. Conf. on Very Large Data Bases (VLDB), Santiago, Chile, 1994.

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