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Attribute Record

The Experts below are selected from a list of 18 Experts worldwide ranked by ideXlab platform

J. Srivastava – 1st expert on this subject based on the ideXlab platform

  • ICDE – Performance evaluation of grid based multi-Attribute Record declustering methods
    Proceedings of 1994 IEEE 10th International Conference on Data Engineering, 1994
    Co-Authors: B. Himatsingka, J. Srivastava

    Abstract:

    We focus on multi-Attribute declustering methods which are based on some type of grid-based partitioning of the data space. Theoretical results are derived which show that no declustering method can be strictly optimal for range queries if the number of disks is greater than 5. A detailed performance evaluation is carried out to see how various declustering schemes perform under a wide range of query and database scenarios (both relative to each other and to the optimal). Parameters that are varied include shape and size of queries, database size, number of Attributes and the number of disks. The results show that information about common queries on a relation is very important and ought to be used in deciding the declustering for it, and that this is especially crucial for small queries. Also, there is no clear winner, and as such parallel database systems must support a number of declustering methods. >

  • Performance evaluation of grid based multi-Attribute Record declustering methods
    Proceedings of 1994 IEEE 10th International Conference on Data Engineering, 1994
    Co-Authors: B. Himatsingka, J. Srivastava

    Abstract:

    We focus on multi-Attribute declustering methods which are based on some type of grid-based partitioning of the data space. Theoretical results are derived which show that no declustering method can be strictly optimal for range queries if the number of disks is greater than 5. A detailed performance evaluation is carried out to see how various declustering schemes perform under a wide range of query and database scenarios (both relative to each other and to the optimal). Parameters that are varied include shape and size of queries, database size, number of Attributes and the number of disks. The results show that information about common queries on a relation is very important and ought to be used in deciding the declustering for it, and that this is especially crucial for small queries. Also, there is no clear winner, and as such parallel database systems must support a number of declustering methods.

B. Himatsingka – 2nd expert on this subject based on the ideXlab platform

  • ICDE – Performance evaluation of grid based multi-Attribute Record declustering methods
    Proceedings of 1994 IEEE 10th International Conference on Data Engineering, 1994
    Co-Authors: B. Himatsingka, J. Srivastava

    Abstract:

    We focus on multi-Attribute declustering methods which are based on some type of grid-based partitioning of the data space. Theoretical results are derived which show that no declustering method can be strictly optimal for range queries if the number of disks is greater than 5. A detailed performance evaluation is carried out to see how various declustering schemes perform under a wide range of query and database scenarios (both relative to each other and to the optimal). Parameters that are varied include shape and size of queries, database size, number of Attributes and the number of disks. The results show that information about common queries on a relation is very important and ought to be used in deciding the declustering for it, and that this is especially crucial for small queries. Also, there is no clear winner, and as such parallel database systems must support a number of declustering methods. >

  • Performance evaluation of grid based multi-Attribute Record declustering methods
    Proceedings of 1994 IEEE 10th International Conference on Data Engineering, 1994
    Co-Authors: B. Himatsingka, J. Srivastava

    Abstract:

    We focus on multi-Attribute declustering methods which are based on some type of grid-based partitioning of the data space. Theoretical results are derived which show that no declustering method can be strictly optimal for range queries if the number of disks is greater than 5. A detailed performance evaluation is carried out to see how various declustering schemes perform under a wide range of query and database scenarios (both relative to each other and to the optimal). Parameters that are varied include shape and size of queries, database size, number of Attributes and the number of disks. The results show that information about common queries on a relation is very important and ought to be used in deciding the declustering for it, and that this is especially crucial for small queries. Also, there is no clear winner, and as such parallel database systems must support a number of declustering methods.

Peng Zhen – 3rd expert on this subject based on the ideXlab platform

  • Applications of GIS-based cable resource management system in power communication network
    Electric Power, 2020
    Co-Authors: Peng Zhen

    Abstract:

    In traditional cable resource management,the Attribute Record forms such as tower coordinates,connected equipments and way of installation are mainly updated manually.The daily maintenance workload is high and the accuracy cannot be guaranteed.By using the superiority in the spatial data management,a Geographic Information System(GIS) based cable resource management system is established.The system can not only achieve the basic function of data management such as fiber network spatial and relevant Attribute data,but also support the comprehensive data analysis for the operation and maintenance management and network planning.The system has the ability to implement the cable resources lean management,improve the utilization rate of fiber optic cables,and improve the operation and maintenance level.