The Experts below are selected from a list of 10089 Experts worldwide ranked by ideXlab platform

Peter Baumann - One of the best experts on this subject based on the ideXlab platform.

  • Hierarchical Storage Support and Management for Large-Scale Multidimensional Array Database Management Systems
    2010
    Co-Authors: Bernd Reiner, Gabriele Höfling, Karl Hahn, Peter Baumann
    Abstract:

    Large-scale scientific experiments or simulation programs often generate large amounts of Multidimensional data. Data volume may reach hundreds of terabytes (up to petabytes). In the present and the near future, the only practicable way for storing such large volumes of Multidimensional data are tertiary storage systems. But commercial (Multidimensional) Database systems are optimized for performance with primary and secondary memory access. So tertiary storage memory is only in an insufficient way supported for storing or retrieval of Multidimensional array data. To combine the advantages of both techniques, storing large amounts of data on tertiary storage media and optimizing data access for retrieval with Multidimensional Database management systems is the intention of this paper. We introduce concepts for efficient hierarchical storage support and management for large-scale Multidimensional array Database management systems and their integration into the commercial array Database management system RasDaMan.

  • The Multidimensional Database system RasDaMan
    ACM SIGMOD Record, 1998
    Co-Authors: Peter Baumann, A. Dehmel, R. Ritsch, Pedro Furtado, N. Widmann
    Abstract:

    RasDaMan is a universal — i.e., domain-independent — array DBMS for Multidimensional arrays of arbitrary size and structure. A declarative, SQL-based array query language offers flexible retrieval and manipulation. Efficient server-based query evaluation is enabled by an intelligent optimizer and a streamlined storage architecture based on flexible array tiling and compression. RasDaMan is being used in several international projects for the management of geo and healthcare data of various dimensionality.

Karl Hahn - One of the best experts on this subject based on the ideXlab platform.

  • Hierarchical Storage Support and Management for Large-Scale Multidimensional Array Database Management Systems
    2010
    Co-Authors: Bernd Reiner, Gabriele Höfling, Karl Hahn, Peter Baumann
    Abstract:

    Large-scale scientific experiments or simulation programs often generate large amounts of Multidimensional data. Data volume may reach hundreds of terabytes (up to petabytes). In the present and the near future, the only practicable way for storing such large volumes of Multidimensional data are tertiary storage systems. But commercial (Multidimensional) Database systems are optimized for performance with primary and secondary memory access. So tertiary storage memory is only in an insufficient way supported for storing or retrieval of Multidimensional array data. To combine the advantages of both techniques, storing large amounts of data on tertiary storage media and optimizing data access for retrieval with Multidimensional Database management systems is the intention of this paper. We introduce concepts for efficient hierarchical storage support and management for large-scale Multidimensional array Database management systems and their integration into the commercial array Database management system RasDaMan.

  • tertiary storage support for large scale Multidimensional array Database management systems
    2002
    Co-Authors: Bernd Reiner, Karl Hahn
    Abstract:

    Many large-scale scientific domains often generate huge amounts (hundreds of terabytes) of Multidimensional data. The only practicable way for storing such large volumes of Multidimensional data is a tertiary storage system. Unfortunately in commercial Multidimensional Database Management Systems (DBMS) the access is optimized for performance with primary and secondary memory. Tertiary storage memory is not or only in an insufficient way supported for storing or retrieval of Multidimensional array data. The intention of this paper is, to combine the advantage of both techniques, storing large amounts of data on tertiary storage media and realizing efficient data access for retrieval with the commercial Multidimensional array DBMS RasDaMan.

Bernd Reiner - One of the best experts on this subject based on the ideXlab platform.

  • Hierarchical Storage Support and Management for Large-Scale Multidimensional Array Database Management Systems
    2010
    Co-Authors: Bernd Reiner, Gabriele Höfling, Karl Hahn, Peter Baumann
    Abstract:

    Large-scale scientific experiments or simulation programs often generate large amounts of Multidimensional data. Data volume may reach hundreds of terabytes (up to petabytes). In the present and the near future, the only practicable way for storing such large volumes of Multidimensional data are tertiary storage systems. But commercial (Multidimensional) Database systems are optimized for performance with primary and secondary memory access. So tertiary storage memory is only in an insufficient way supported for storing or retrieval of Multidimensional array data. To combine the advantages of both techniques, storing large amounts of data on tertiary storage media and optimizing data access for retrieval with Multidimensional Database management systems is the intention of this paper. We introduce concepts for efficient hierarchical storage support and management for large-scale Multidimensional array Database management systems and their integration into the commercial array Database management system RasDaMan.

  • tertiary storage support for large scale Multidimensional array Database management systems
    2002
    Co-Authors: Bernd Reiner, Karl Hahn
    Abstract:

    Many large-scale scientific domains often generate huge amounts (hundreds of terabytes) of Multidimensional data. The only practicable way for storing such large volumes of Multidimensional data is a tertiary storage system. Unfortunately in commercial Multidimensional Database Management Systems (DBMS) the access is optimized for performance with primary and secondary memory. Tertiary storage memory is not or only in an insufficient way supported for storing or retrieval of Multidimensional array data. The intention of this paper is, to combine the advantage of both techniques, storing large amounts of data on tertiary storage media and realizing efficient data access for retrieval with the commercial Multidimensional array DBMS RasDaMan.

Gottfried Vossen - One of the best experts on this subject based on the ideXlab platform.

  • Multidimensional normal forms for data warehouse design
    Information Systems, 2003
    Co-Authors: Jens Lechtenbörger, Gottfried Vossen
    Abstract:

    A data warehouse is an integrated and time-varying collection of data derived from operational data and primarily used in strategic decision making by means of OLAP techniques. Although it is generally agreed that warehouse design is a non-trivial problem and that Multidimensional data models as well as star or snowflake schemata are relevant in this context, there exist neither methods for deriving such a schema from an operational Database nor measures for evaluating a warehouse schema. In this paper, a sequence of Multidimensional normal forms is established that allow reasoning about the quality of conceptual data warehouse schemata in a rigorous manner. These normal forms address traditional Database design objectives such as faithfulness, completeness, and freedom of redundancies as well as the notion of summarizability, which is specific to Multidimensional Database schemata.

Ivan Vrana - One of the best experts on this subject based on the ideXlab platform.

  • software algorithm of em olap tool design of olap Database for econometric application
    Computer Science On-line Conference, 2019
    Co-Authors: Jan Tyrychtr, Martin Pelikan, Ivan Vrana
    Abstract:

    The in-field econometric analysis with specific OLAP technology is of benefit to non-econometric analysts for efficient analysis. Integration of the transformation of the econometric model (TEM) method with the software tool is useful for designing a Multidimensional Database. This research presents the design prototype of such software and its integration with an in-field TEM method to implement Database specific for OLAP by a star schema. The prototype of the EM-OLAP Tool software was designed by a simple click-and-play menu using a graphical user interface and optimized to adapt the Database design requirements. An algorithm for transformation of econometric models was developed to design a Multidimensional schema of Database according the TEM method.

  • em olap framework econometric model transformation method for olap design in intelligence systems
    Web Intelligence, 2018
    Co-Authors: Jan Tyrychtr, Martin Pelikan, Hana Stikova, Ivan Vrana
    Abstract:

    Econometrics is currently one of the most popular approaches to economic analysis. To better support advances in these areas as much as possible, it is necessary to apply econometric problems to econometric intelligent systems. The article describes an econometric OLAP framework that supports the design of a Multidimensional Database to secure econometric analyses to increase the effectiveness of the development of econometric intelligent systems. The first part of the article consists of the creation of formal rules for the new transformation of the econometric model (TEM) method for the econometric model transformation of Multidimensional schema through the use of mathematical notation. In the proposed TEM method, the authors pay attention to the measurement of quality and understandability of the Multidimensional schema, and compare the proposed method with the original TEM-CM method. In the second part of the article, the authors create a Multidimensional Database prototype according to the new TEM method and design an OLAP application for econometric analysis.