The Experts below are selected from a list of 153 Experts worldwide ranked by ideXlab platform

Ladjel Bellatreche - One of the best experts on this subject based on the ideXlab platform.

  • ADBIS - Static and incremental selection of multi-Table indexes for very large join queries
    Advances in Databases and Information Systems, 2012
    Co-Authors: Rima Bouchakri, Ladjel Bellatreche, Khaled-walid Hidouci
    Abstract:

    Multi-Table indexes boost the performance of extremely large databases by reducing the cost of joins involving several Tables. The bitmap join indexes ($\mathcal{B}\mathcal{J}\mathcal{I}$) are one of the most popular examples of this category of indexes. They are well adapted for point and range queries. Note that the selection of multi-Table indexes is more difficult than the mono-Table indexes, considered as the pioneer of database optimisation problems. The few studies dealing with the $\mathcal{B}\mathcal{J}\mathcal{I}$ selection problem in the context of relational data warehouses have three main limitations: (i) they consider $\mathcal{B}\mathcal{J}\mathcal{I}$ defined on only two Tables (a fact Table and a Dimension Table) by the use of one or several attributes of that Dimension Table, (ii) they use simple greedy algorithms to pick the right indexes and (iii) their algorithms are static. In this paper, we propose genetic algorithms for selecting $\mathcal{B}\mathcal{J}\mathcal{I}$ using a large number of attributes belonging to n (≥2) Dimension Tables in the static and incremental ways. Intensive experiments are conducted to show the efficiency of our proposal.

  • Dimension Table selection strategies to referential partition a fact Table of relational data warehouses
    2012
    Co-Authors: Ladjel Bellatreche
    Abstract:

    Enterprise wide data warehouses are becoming increasingly adopted as the main source and underlying infrastructure for business intelligence (BI) solutions. Note that a data warehouse can be viewed as an integration system, where data sources are duplicated in the same repository. Data warehouses are designed to handle the queries required to discover trends and critical factors are called Online Analytical Processing (OLAP) systems. Examples of an OLAP query are: Amazon (www.amazon.com) company analyzes purchases by its customers to come up with an individual screen with products of likely interest to the customer. Analysts at Wal-Mart (www.walmart.com) look for items with increasing sales in some city. Star schemes or their variants are usually used to model warehouse applications. They are composed of thousand of Dimension Tables and multiple fact Tables [15, 18]. Figure 2.1 shows an example of star schema of the widely-known data warehouse benchmark APB-1 release II [21]. Here, the fact Table Sales is joint to the following four Dimension Tables: Product, Customer, Time, Channel. Star queries are typically executed against the warehouse. Queries running on such applications contain a large number of costly joins, selections and aggregations. They are called mega queries [24]. To optimize these queries, the use of advanced optimization techniques is necessary.

  • Dimension Table driven approach to referential partition relational data warehouses
    Data Warehousing and OLAP, 2009
    Co-Authors: Ladjel Bellatreche, Komla Yamavo Woameno
    Abstract:

    Most of business intelligence applications use data warehousing solutions. The star schema or its variants modelling these applications are usually composed of hundreds of Dimension Tables and multiple huge fact Tables. Referential horizontal partitioning is one of physical design techniques adapted to optimize queries posed over these schemes. In referential partitioning, a fact Table can inherit the fragmentation characteristics from Dimension Table(s). Most of the existing works done on referential partitioning start from a bag containing selection predicates defined on Dimension Tables, partition each one based on its predicates and finally propagate their fragmentation schemes to the fact Table. This procedure gives all Dimension Tables the same probability to partition the fact Table which is not always true. In order to ensure a high performance of the most costly queries, the identification of relevant Dimension Table(s) to referential partition a fact Table is a crucial issue that should be addressed. In this paper, we first study the complexity of the problem of selecting Dimension Table(s) used to partition a fact Table. Secondly, we present strategies to perform their selection. Finally, to validate of our proposal, we conduct intensive experimental studies using a mathematical cost model and the obtained results are verified on Oracle11G DBMS.

  • DOLAP - Dimension Table driven approach to referential partition relational data warehouses
    Proceeding of the ACM twelfth international workshop on Data warehousing and OLAP - DOLAP '09, 2009
    Co-Authors: Ladjel Bellatreche, Komla Yamavo Woameno
    Abstract:

    Most of business intelligence applications use data warehousing solutions. The star schema or its variants modelling these applications are usually composed of hundreds of Dimension Tables and multiple huge fact Tables. Referential horizontal partitioning is one of physical design techniques adapted to optimize queries posed over these schemes. In referential partitioning, a fact Table can inherit the fragmentation characteristics from Dimension Table(s). Most of the existing works done on referential partitioning start from a bag containing selection predicates defined on Dimension Tables, partition each one based on its predicates and finally propagate their fragmentation schemes to the fact Table. This procedure gives all Dimension Tables the same probability to partition the fact Table which is not always true. In order to ensure a high performance of the most costly queries, the identification of relevant Dimension Table(s) to referential partition a fact Table is a crucial issue that should be addressed. In this paper, we first study the complexity of the problem of selecting Dimension Table(s) used to partition a fact Table. Secondly, we present strategies to perform their selection. Finally, to validate of our proposal, we conduct intensive experimental studies using a mathematical cost model and the obtained results are verified on Oracle11G DBMS.

Komla Yamavo Woameno - One of the best experts on this subject based on the ideXlab platform.

  • Dimension Table driven approach to referential partition relational data warehouses
    Data Warehousing and OLAP, 2009
    Co-Authors: Ladjel Bellatreche, Komla Yamavo Woameno
    Abstract:

    Most of business intelligence applications use data warehousing solutions. The star schema or its variants modelling these applications are usually composed of hundreds of Dimension Tables and multiple huge fact Tables. Referential horizontal partitioning is one of physical design techniques adapted to optimize queries posed over these schemes. In referential partitioning, a fact Table can inherit the fragmentation characteristics from Dimension Table(s). Most of the existing works done on referential partitioning start from a bag containing selection predicates defined on Dimension Tables, partition each one based on its predicates and finally propagate their fragmentation schemes to the fact Table. This procedure gives all Dimension Tables the same probability to partition the fact Table which is not always true. In order to ensure a high performance of the most costly queries, the identification of relevant Dimension Table(s) to referential partition a fact Table is a crucial issue that should be addressed. In this paper, we first study the complexity of the problem of selecting Dimension Table(s) used to partition a fact Table. Secondly, we present strategies to perform their selection. Finally, to validate of our proposal, we conduct intensive experimental studies using a mathematical cost model and the obtained results are verified on Oracle11G DBMS.

  • DOLAP - Dimension Table driven approach to referential partition relational data warehouses
    Proceeding of the ACM twelfth international workshop on Data warehousing and OLAP - DOLAP '09, 2009
    Co-Authors: Ladjel Bellatreche, Komla Yamavo Woameno
    Abstract:

    Most of business intelligence applications use data warehousing solutions. The star schema or its variants modelling these applications are usually composed of hundreds of Dimension Tables and multiple huge fact Tables. Referential horizontal partitioning is one of physical design techniques adapted to optimize queries posed over these schemes. In referential partitioning, a fact Table can inherit the fragmentation characteristics from Dimension Table(s). Most of the existing works done on referential partitioning start from a bag containing selection predicates defined on Dimension Tables, partition each one based on its predicates and finally propagate their fragmentation schemes to the fact Table. This procedure gives all Dimension Tables the same probability to partition the fact Table which is not always true. In order to ensure a high performance of the most costly queries, the identification of relevant Dimension Table(s) to referential partition a fact Table is a crucial issue that should be addressed. In this paper, we first study the complexity of the problem of selecting Dimension Table(s) used to partition a fact Table. Secondly, we present strategies to perform their selection. Finally, to validate of our proposal, we conduct intensive experimental studies using a mathematical cost model and the obtained results are verified on Oracle11G DBMS.

Lijun Zhang - One of the best experts on this subject based on the ideXlab platform.

  • [ Specification Table ] [ Dimension Table ]
    IEEE Transactions on Antennas and Propagation, 2014
    Co-Authors: Kunsun Eom, Peter De Maagt, Andrea Vallecchi, Javier R De Luis, Senior Student Member, Dan Sievenpiper, Ramón Gonzalo, Filippo Capolino, Hiroyuki Arai, Lijun Zhang
    Abstract:

    The document that should appear here is not currently available. IEEE Xplore® is working to obtain a replacement PDF. That PDF will be posted as soon as it is available. We regret any inconvenience in the meantime.

Manuel Wilson Hernandez - One of the best experts on this subject based on the ideXlab platform.

  • SCCC - Performance comparison slowly changing Dimensions using model relational and object-relational
    2015 34th International Conference of the Chilean Computer Science Society (SCCC), 2015
    Co-Authors: Angelica Urrutia Sepulveda, Rodrigo Cofre Loyola, Manuel Wilson Hernandez
    Abstract:

    Data warehouses are designed with a multiDimensional structure based on fact and Dimension Tables, oriented towards indicator systems that inform decision making. The most frequent loads associated to this kind of system are performed on the fact Table, assuming that all Dimensions are common or time-independent, which can lead to serious consequences in terms of the integrity and completeness of the obtained information. A number of proposals have been made on how to update the Dimension Table, calling this process Slowly Changing Dimensions (SCD), focused on relational data design. This article focuses on proposing strategies to improve the response time from queries associated to a data warehouse design for Dimension Tables than change, comparing SCD designs in relational and object-relational databases. Finally, a case is presented that allows to compare the performance from both designs.

  • Performance comparison slowly changing Dimensions using model relational and object-relational
    2015 34th International Conference of the Chilean Computer Science Society (SCCC), 2015
    Co-Authors: Angelica Urrutia Sepulveda, Rodrigo Cofre Loyola, Manuel Wilson Hernandez
    Abstract:

    Data warehouses are designed with a multiDimensional structure based on fact and Dimension Tables, oriented towards indicator systems that inform decision making. The most frequent loads associated to this kind of system are performed on the fact Table, assuming that all Dimensions are common or time-independent, which can lead to serious consequences in terms of the integrity and completeness of the obtained information. A number of proposals have been made on how to update the Dimension Table, calling this process Slowly Changing Dimensions (SCD), focused on relational data design. This article focuses on proposing strategies to improve the response time from queries associated to a data warehouse design for Dimension Tables than change, comparing SCD designs in relational and object-relational databases. Finally, a case is presented that allows to compare the performance from both designs.

Kunsun Eom - One of the best experts on this subject based on the ideXlab platform.

  • [ Specification Table ] [ Dimension Table ]
    IEEE Transactions on Antennas and Propagation, 2014
    Co-Authors: Kunsun Eom, Peter De Maagt, Andrea Vallecchi, Javier R De Luis, Senior Student Member, Dan Sievenpiper, Ramón Gonzalo, Filippo Capolino, Hiroyuki Arai, Lijun Zhang
    Abstract:

    The document that should appear here is not currently available. IEEE Xplore® is working to obtain a replacement PDF. That PDF will be posted as soon as it is available. We regret any inconvenience in the meantime.