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Christian Koncilia - One of the best experts on this subject based on the ideXlab platform.

  • changes of Dimension Data in temporal Data warehouses
    Data Warehousing and Knowledge Discovery, 2001
    Co-Authors: Johann Eder, Christian Koncilia
    Abstract:

    Time is one of the Dimensions we frequently find in Data warehouses allowing comparisons of Data in different periods. In current multi-Dimensional Data warehouse technology changes of Dimension Data cannot be represented adequately since all Dimensions are (implicitly) considered as orthogonal. We propose an extension of the multi-Dimensional Data model employed in Data warehouses allowing to cope correctly with changes in Dimension Data: a temporal multi-Dimensional Data model allows the registration of temporal versions of Dimension Data. Mappings are provided to transfer Data between different temporal versions of the instances of Dimensions and enable the system to correctly answer queries spanning multiple periods and thus different versions of Dimension Data.

  • DaWaK - Changes of Dimension Data in Temporal Data Warehouses
    Data Warehousing and Knowledge Discovery, 2001
    Co-Authors: Johann Eder, Christian Koncilia
    Abstract:

    Time is one of the Dimensions we frequently find in Data warehouses allowing comparisons of Data in different periods. In current multi-Dimensional Data warehouse technology changes of Dimension Data cannot be represented adequately since all Dimensions are (implicitly) considered as orthogonal. We propose an extension of the multi-Dimensional Data model employed in Data warehouses allowing to cope correctly with changes in Dimension Data: a temporal multi-Dimensional Data model allows the registration of temporal versions of Dimension Data. Mappings are provided to transfer Data between different temporal versions of the instances of Dimensions and enable the system to correctly answer queries spanning multiple periods and thus different versions of Dimension Data.

  • Evolution of Dimension Data in Temporal Data Warehouses
    2000
    Co-Authors: Johann Eder, Christian Koncilia
    Abstract:

    Multi-Dimensional analysis is one of the most important applications of Data warehouses, giving the possibility to aggregate and compare Data along Dimensions relevant in the application domain. Typically time is one of the Dimensions we nd in Data warehouses allowing comparisons of di erent periods. The instances of Dimensions, however, change over time - countries unite and separate, products emerge and vanish, organizational structures evolve. In current Data warehouse technology these changes cannot be represented adequately since all Dimensions are (implicitly) considered as orthogonal, putting heavy restrictions on the validity of OLAP queries spanning several periods. We propose an extension of the multi-Dimensional Data model employed in Data warehouses allowing to cope correctly with changes in Dimension Data: a temporal multi-Dimensional Data model allows the registration of temporal versions of Dimension Data. Mappings are provided to transfer Data between di erent temporal versions and enable the system to correctly answer queries spanning multiple periods and thus di erent versions of Dimension Data.

Johann Eder - One of the best experts on this subject based on the ideXlab platform.

  • changes of Dimension Data in temporal Data warehouses
    Data Warehousing and Knowledge Discovery, 2001
    Co-Authors: Johann Eder, Christian Koncilia
    Abstract:

    Time is one of the Dimensions we frequently find in Data warehouses allowing comparisons of Data in different periods. In current multi-Dimensional Data warehouse technology changes of Dimension Data cannot be represented adequately since all Dimensions are (implicitly) considered as orthogonal. We propose an extension of the multi-Dimensional Data model employed in Data warehouses allowing to cope correctly with changes in Dimension Data: a temporal multi-Dimensional Data model allows the registration of temporal versions of Dimension Data. Mappings are provided to transfer Data between different temporal versions of the instances of Dimensions and enable the system to correctly answer queries spanning multiple periods and thus different versions of Dimension Data.

  • DaWaK - Changes of Dimension Data in Temporal Data Warehouses
    Data Warehousing and Knowledge Discovery, 2001
    Co-Authors: Johann Eder, Christian Koncilia
    Abstract:

    Time is one of the Dimensions we frequently find in Data warehouses allowing comparisons of Data in different periods. In current multi-Dimensional Data warehouse technology changes of Dimension Data cannot be represented adequately since all Dimensions are (implicitly) considered as orthogonal. We propose an extension of the multi-Dimensional Data model employed in Data warehouses allowing to cope correctly with changes in Dimension Data: a temporal multi-Dimensional Data model allows the registration of temporal versions of Dimension Data. Mappings are provided to transfer Data between different temporal versions of the instances of Dimensions and enable the system to correctly answer queries spanning multiple periods and thus different versions of Dimension Data.

  • Evolution of Dimension Data in Temporal Data Warehouses
    2000
    Co-Authors: Johann Eder, Christian Koncilia
    Abstract:

    Multi-Dimensional analysis is one of the most important applications of Data warehouses, giving the possibility to aggregate and compare Data along Dimensions relevant in the application domain. Typically time is one of the Dimensions we nd in Data warehouses allowing comparisons of di erent periods. The instances of Dimensions, however, change over time - countries unite and separate, products emerge and vanish, organizational structures evolve. In current Data warehouse technology these changes cannot be represented adequately since all Dimensions are (implicitly) considered as orthogonal, putting heavy restrictions on the validity of OLAP queries spanning several periods. We propose an extension of the multi-Dimensional Data model employed in Data warehouses allowing to cope correctly with changes in Dimension Data: a temporal multi-Dimensional Data model allows the registration of temporal versions of Dimension Data. Mappings are provided to transfer Data between di erent temporal versions and enable the system to correctly answer queries spanning multiple periods and thus di erent versions of Dimension Data.

Il-yeol Song - One of the best experts on this subject based on the ideXlab platform.

  • XML-OLAP : A multiDimensional analysis framework for XML warehouses
    Lecture Notes in Computer Science, 2020
    Co-Authors: Byung-kwon Park, Il-yeol Song
    Abstract:

    Recently, a large number of XML documents are available on the Internet. This trend motivated many researchers to analyze them multi-Dimensionally in the same way as relational Data. In this paper, we propose a new framework for multiDimensional analysis of XML documents, which we call XML-OLAP. We base XML-OLAP on XML warehouses where every fact Data as well as Dimension Data are stored as XML documents. We build XML cubes from XML warehouses. We propose a new multiDimensional expression language for XML cubes, which we call XML-MDX. XML-MDX statements target XML cubes and use XQuery expressions to designate the measure Data. They specify text mining operators for aggregating text constituting the measure Data. We evaluate XML-OLAP by applying it to a U.S. patent XML warehouse. We use XML-MDX queries, which demonstrate that XML-OLAP is effective for multi-Dimensionally analyzing the U.S. patents.

  • DaWaK - XML-OLAP: a multiDimensional analysis framework for XML warehouses
    Data Warehousing and Knowledge Discovery, 2005
    Co-Authors: Byung-kwon Park, Il-yeol Song
    Abstract:

    Recently, a large number of XML documents are available on the Internet. This trend motivated many researchers to analyze them multi-Dimensionally in the same way as relational Data. In this paper, we propose a new framework for multiDimensional analysis of XML documents, which we call XML-OLAP. We base XML-OLAP on XML warehouses where every fact Data as well as Dimension Data are stored as XML documents. We build XML cubes from XML warehouses. We propose a new multiDimensional expression language for XML cubes, which we call XML-MDX. XML-MDX statements target XML cubes and use XQuery expressions to designate the measure Data. They specify text mining operators for aggregating text constituting the measure Data. We evaluate XML-OLAP by applying it to a U.S. patent XML warehouse. We use XML-MDX queries, which demonstrate that XML-OLAP is effective for multi-Dimensionally analyzing the U.S. patents.

Byung-kwon Park - One of the best experts on this subject based on the ideXlab platform.

  • XML-OLAP : A multiDimensional analysis framework for XML warehouses
    Lecture Notes in Computer Science, 2020
    Co-Authors: Byung-kwon Park, Il-yeol Song
    Abstract:

    Recently, a large number of XML documents are available on the Internet. This trend motivated many researchers to analyze them multi-Dimensionally in the same way as relational Data. In this paper, we propose a new framework for multiDimensional analysis of XML documents, which we call XML-OLAP. We base XML-OLAP on XML warehouses where every fact Data as well as Dimension Data are stored as XML documents. We build XML cubes from XML warehouses. We propose a new multiDimensional expression language for XML cubes, which we call XML-MDX. XML-MDX statements target XML cubes and use XQuery expressions to designate the measure Data. They specify text mining operators for aggregating text constituting the measure Data. We evaluate XML-OLAP by applying it to a U.S. patent XML warehouse. We use XML-MDX queries, which demonstrate that XML-OLAP is effective for multi-Dimensionally analyzing the U.S. patents.

  • DaWaK - XML-OLAP: a multiDimensional analysis framework for XML warehouses
    Data Warehousing and Knowledge Discovery, 2005
    Co-Authors: Byung-kwon Park, Il-yeol Song
    Abstract:

    Recently, a large number of XML documents are available on the Internet. This trend motivated many researchers to analyze them multi-Dimensionally in the same way as relational Data. In this paper, we propose a new framework for multiDimensional analysis of XML documents, which we call XML-OLAP. We base XML-OLAP on XML warehouses where every fact Data as well as Dimension Data are stored as XML documents. We build XML cubes from XML warehouses. We propose a new multiDimensional expression language for XML cubes, which we call XML-MDX. XML-MDX statements target XML cubes and use XQuery expressions to designate the measure Data. They specify text mining operators for aggregating text constituting the measure Data. We evaluate XML-OLAP by applying it to a U.S. patent XML warehouse. We use XML-MDX queries, which demonstrate that XML-OLAP is effective for multi-Dimensionally analyzing the U.S. patents.

Tetsuo Endoh - One of the best experts on this subject based on the ideXlab platform.

  • An MTJ-based nonvolatile associative memory architecture with intelligent power-saving scheme for high-speed low-power recognition applications
    2013 IEEE International Symposium on Circuits and Systems (ISCAS), 2013
    Co-Authors: Tadashi Shibata, Tetsuo Endoh
    Abstract:

    A nonvolatile associative memory architecture based on the Magnetic Tunnel Junction (MTJ) devices has been proposed for high-speed low-power recognition. In order to reduce the power dissipation without sacrificing the speed performance, an intelligent power-saving scheme has been developed taking the advantage of non-volatility of MTJ devices. The power lines of 4-Transistor 2-MTJ nonvolatile memory cells are controlled by not only word line signals but also the internal power control signals supplied from the Data-mask/power-gating units to only activate the currently accessed memory elements. The proof-of-concept chip for 128-Dimension Data vectors has been designed under a 90-nm 5-metal CMOS/MTJ hybrid technology, and the chip operation at 100MHz has been verified by SPICE simulation. Compared to the conventional 6T-SRAM architecture, the proposed architecture achieves the higher speed and up to 97% power reduction. Moreover, this architecture is also proved to be particularly suitable for the applications with higher Dimension Data vectors.