The Experts below are selected from a list of 21108 Experts worldwide ranked by ideXlab platform

Calisto Zuzarte - One of the best experts on this subject based on the ideXlab platform.

  • Recommending XML physical Designs for XML databases
    The VLDB Journal, 2013
    Co-Authors: Iman Elghandour, Daniel C. Zilio, Ashraf Aboulnaga, Calisto Zuzarte
    Abstract:

    Database systems employ physical structures such as indexes and materialized views to improve query performance, potentially by orders of magnitude. It is therefore important for a database administrator to choose the appropriate configuration of these physical structures for a given database. XML database systems are increasingly being used to manage semi-structured data, and XML support has been added to commercial database systems. In this paper, we address the problem of automatic physical Design for XML databases, which is the process of automatically selecting the best set of physical structures for a database and a query workload. We focus on recommending two types of physical structures: XML indexes and relational materialized views of XML data. We present a Design Advisor for recommending XML indexes, one for recommending materialized views, and an integrated Design Advisor that recommends both indexes and materialized views. A key characteristic of our Advisors is that they are tightly coupled with the query optimizer of the database system, and they rely on the optimizer for enumerating and evaluating physical Designs. We have implemented our Advisors in a prototype version of IBM DB2 V9, and we experimentally demonstrate the effectiveness of their recommendations using this implementation.

  • recommending materialized views and indexes with the ibm db2 Design Advisor
    International Conference on Autonomic Computing, 2004
    Co-Authors: Daniel C. Zilio, Calisto Zuzarte, Sam Lightstone, Guy M Lohman, R J Cochrane, Hamid Pirahesh, Latha S Colby, Jarek Gryz, E Alton, Gary Valentin
    Abstract:

    Materialized views (MVs) and indexes both significantly speed query processing in database systems, but consume disk space and need to be maintained when updates occur. Choosing the best set of MVs and indexes to create depends upon the workload, the database, and many other factors, which makes the decision intractable for humans and computationally challenging for computer algorithms. Even heuristic-based algorithms can be impractical in real systems. In this paper, we present an advanced tool that uses the query optimizer itself to both suggest and evaluate candidate MVs and indexes, and a simple, practical, and effective algorithm for rapidly finding good solutions even for large workloads. The algorithm trades off the cost for updates and storing each MV or index against its benefit to queries in the workload. The tool autonomically captures the workload, database, and system information, optionally permits sampling of candidate MVs to better estimate their size, and exploits multi-query optimization to construct candidate MVs that will benefit many queries, over which their maintenance cost can then be amortized cost-effectively. We describe the Design of the system and present initial experiments that confirm the quality of its results on a database and workload drawn from a real customer database.

  • ICAC - Recommending materialized views and indexes with the IBM DB2 Design Advisor
    International Conference on Autonomic Computing 2004. Proceedings., 1
    Co-Authors: Daniel C. Zilio, Calisto Zuzarte, Sam Lightstone, Guy M Lohman, R J Cochrane, Hamid Pirahesh, Latha S Colby, Jarek Gryz, E Alton
    Abstract:

    Materialized views (MVs) and indexes both significantly speed query processing in database systems, but consume disk space and need to be maintained when updates occur. Choosing the best set of MVs and indexes to create depends upon the workload, the database, and many other factors, which makes the decision intractable for humans and computationally challenging for computer algorithms. Even heuristic-based algorithms can be impractical in real systems. In this paper, we present an advanced tool that uses the query optimizer itself to both suggest and evaluate candidate MVs and indexes, and a simple, practical, and effective algorithm for rapidly finding good solutions even for large workloads. The algorithm trades off the cost for updates and storing each MV or index against its benefit to queries in the workload. The tool autonomically captures the workload, database, and system information, optionally permits sampling of candidate MVs to better estimate their size, and exploits multi-query optimization to construct candidate MVs that will benefit many queries, over which their maintenance cost can then be amortized cost-effectively. We describe the Design of the system and present initial experiments that confirm the quality of its results on a database and workload drawn from a real customer database.

Daniel C. Zilio - One of the best experts on this subject based on the ideXlab platform.

  • Recommending XML physical Designs for XML databases
    The VLDB Journal, 2013
    Co-Authors: Iman Elghandour, Daniel C. Zilio, Ashraf Aboulnaga, Calisto Zuzarte
    Abstract:

    Database systems employ physical structures such as indexes and materialized views to improve query performance, potentially by orders of magnitude. It is therefore important for a database administrator to choose the appropriate configuration of these physical structures for a given database. XML database systems are increasingly being used to manage semi-structured data, and XML support has been added to commercial database systems. In this paper, we address the problem of automatic physical Design for XML databases, which is the process of automatically selecting the best set of physical structures for a database and a query workload. We focus on recommending two types of physical structures: XML indexes and relational materialized views of XML data. We present a Design Advisor for recommending XML indexes, one for recommending materialized views, and an integrated Design Advisor that recommends both indexes and materialized views. A key characteristic of our Advisors is that they are tightly coupled with the query optimizer of the database system, and they rely on the optimizer for enumerating and evaluating physical Designs. We have implemented our Advisors in a prototype version of IBM DB2 V9, and we experimentally demonstrate the effectiveness of their recommendations using this implementation.

  • db2 Design Advisor integrated automatic physical database Design
    Very Large Data Bases, 2004
    Co-Authors: Daniel C. Zilio, Jun Rao, Sam Lightstone, Guy M Lohman, Adam J Storm, Christian Marcelo Garciaarellano, Scott Fadden
    Abstract:

    The DB2 Design Advisor in IBM® DB2® Universal DatabaseTM (DB2 UDB) Version 8.2 for Linux®, UNIX® and Windows® is a tool that, for a given workload, automatically recommends physical Design features that are any subset of indexes, materialized query tables (also called materialized views), shared-nothing database partitionings, and multidimensional clustering of tables. Our work is the very first industrial-strength tool that covers the Design of as many as four different features, a significant advance to existing tools, which support no more than just indexes and materialized views. Building such a tool is challenging, because of not only the large search space introduced by the interactions among features, but also the extensibility needed by the tool to support additional features in the future. We adopt a novel "hybrid" approach in the Design Advisor that allows us to take important interdependencies into account as well as to encapsulate Design features as separate components to lower the reengineering cost. The Design Advisor also features a built-in module that automatically reduces the given workload, and therefore provides great scalability for the tool. Our experimental results demonstrate that our tool can quickly provide good physical Design recommendations that satisfy users' requirements.

  • VLDB - DB2 Design Advisor: integrated automatic physical database Design
    Proceedings 2004 VLDB Conference, 2004
    Co-Authors: Daniel C. Zilio, Jun Rao, Sam Lightstone, Guy M Lohman, Adam J Storm, Christian Garcia-arellano, Scott Fadden
    Abstract:

    The DB2 Design Advisor in IBM® DB2® Universal DatabaseTM (DB2 UDB) Version 8.2 for Linux®, UNIX® and Windows® is a tool that, for a given workload, automatically recommends physical Design features that are any subset of indexes, materialized query tables (also called materialized views), shared-nothing database partitionings, and multidimensional clustering of tables. Our work is the very first industrial-strength tool that covers the Design of as many as four different features, a significant advance to existing tools, which support no more than just indexes and materialized views. Building such a tool is challenging, because of not only the large search space introduced by the interactions among features, but also the extensibility needed by the tool to support additional features in the future. We adopt a novel "hybrid" approach in the Design Advisor that allows us to take important interdependencies into account as well as to encapsulate Design features as separate components to lower the reengineering cost. The Design Advisor also features a built-in module that automatically reduces the given workload, and therefore provides great scalability for the tool. Our experimental results demonstrate that our tool can quickly provide good physical Design recommendations that satisfy users' requirements.

  • recommending materialized views and indexes with the ibm db2 Design Advisor
    International Conference on Autonomic Computing, 2004
    Co-Authors: Daniel C. Zilio, Calisto Zuzarte, Sam Lightstone, Guy M Lohman, R J Cochrane, Hamid Pirahesh, Latha S Colby, Jarek Gryz, E Alton, Gary Valentin
    Abstract:

    Materialized views (MVs) and indexes both significantly speed query processing in database systems, but consume disk space and need to be maintained when updates occur. Choosing the best set of MVs and indexes to create depends upon the workload, the database, and many other factors, which makes the decision intractable for humans and computationally challenging for computer algorithms. Even heuristic-based algorithms can be impractical in real systems. In this paper, we present an advanced tool that uses the query optimizer itself to both suggest and evaluate candidate MVs and indexes, and a simple, practical, and effective algorithm for rapidly finding good solutions even for large workloads. The algorithm trades off the cost for updates and storing each MV or index against its benefit to queries in the workload. The tool autonomically captures the workload, database, and system information, optionally permits sampling of candidate MVs to better estimate their size, and exploits multi-query optimization to construct candidate MVs that will benefit many queries, over which their maintenance cost can then be amortized cost-effectively. We describe the Design of the system and present initial experiments that confirm the quality of its results on a database and workload drawn from a real customer database.

  • ICAC - Recommending materialized views and indexes with the IBM DB2 Design Advisor
    International Conference on Autonomic Computing 2004. Proceedings., 1
    Co-Authors: Daniel C. Zilio, Calisto Zuzarte, Sam Lightstone, Guy M Lohman, R J Cochrane, Hamid Pirahesh, Latha S Colby, Jarek Gryz, E Alton
    Abstract:

    Materialized views (MVs) and indexes both significantly speed query processing in database systems, but consume disk space and need to be maintained when updates occur. Choosing the best set of MVs and indexes to create depends upon the workload, the database, and many other factors, which makes the decision intractable for humans and computationally challenging for computer algorithms. Even heuristic-based algorithms can be impractical in real systems. In this paper, we present an advanced tool that uses the query optimizer itself to both suggest and evaluate candidate MVs and indexes, and a simple, practical, and effective algorithm for rapidly finding good solutions even for large workloads. The algorithm trades off the cost for updates and storing each MV or index against its benefit to queries in the workload. The tool autonomically captures the workload, database, and system information, optionally permits sampling of candidate MVs to better estimate their size, and exploits multi-query optimization to construct candidate MVs that will benefit many queries, over which their maintenance cost can then be amortized cost-effectively. We describe the Design of the system and present initial experiments that confirm the quality of its results on a database and workload drawn from a real customer database.

E Alton - One of the best experts on this subject based on the ideXlab platform.

  • recommending materialized views and indexes with the ibm db2 Design Advisor
    International Conference on Autonomic Computing, 2004
    Co-Authors: Daniel C. Zilio, Calisto Zuzarte, Sam Lightstone, Guy M Lohman, R J Cochrane, Hamid Pirahesh, Latha S Colby, Jarek Gryz, E Alton, Gary Valentin
    Abstract:

    Materialized views (MVs) and indexes both significantly speed query processing in database systems, but consume disk space and need to be maintained when updates occur. Choosing the best set of MVs and indexes to create depends upon the workload, the database, and many other factors, which makes the decision intractable for humans and computationally challenging for computer algorithms. Even heuristic-based algorithms can be impractical in real systems. In this paper, we present an advanced tool that uses the query optimizer itself to both suggest and evaluate candidate MVs and indexes, and a simple, practical, and effective algorithm for rapidly finding good solutions even for large workloads. The algorithm trades off the cost for updates and storing each MV or index against its benefit to queries in the workload. The tool autonomically captures the workload, database, and system information, optionally permits sampling of candidate MVs to better estimate their size, and exploits multi-query optimization to construct candidate MVs that will benefit many queries, over which their maintenance cost can then be amortized cost-effectively. We describe the Design of the system and present initial experiments that confirm the quality of its results on a database and workload drawn from a real customer database.

  • ICAC - Recommending materialized views and indexes with the IBM DB2 Design Advisor
    International Conference on Autonomic Computing 2004. Proceedings., 1
    Co-Authors: Daniel C. Zilio, Calisto Zuzarte, Sam Lightstone, Guy M Lohman, R J Cochrane, Hamid Pirahesh, Latha S Colby, Jarek Gryz, E Alton
    Abstract:

    Materialized views (MVs) and indexes both significantly speed query processing in database systems, but consume disk space and need to be maintained when updates occur. Choosing the best set of MVs and indexes to create depends upon the workload, the database, and many other factors, which makes the decision intractable for humans and computationally challenging for computer algorithms. Even heuristic-based algorithms can be impractical in real systems. In this paper, we present an advanced tool that uses the query optimizer itself to both suggest and evaluate candidate MVs and indexes, and a simple, practical, and effective algorithm for rapidly finding good solutions even for large workloads. The algorithm trades off the cost for updates and storing each MV or index against its benefit to queries in the workload. The tool autonomically captures the workload, database, and system information, optionally permits sampling of candidate MVs to better estimate their size, and exploits multi-query optimization to construct candidate MVs that will benefit many queries, over which their maintenance cost can then be amortized cost-effectively. We describe the Design of the system and present initial experiments that confirm the quality of its results on a database and workload drawn from a real customer database.

Sam Lightstone - One of the best experts on this subject based on the ideXlab platform.

  • db2 Design Advisor integrated automatic physical database Design
    Very Large Data Bases, 2004
    Co-Authors: Daniel C. Zilio, Jun Rao, Sam Lightstone, Guy M Lohman, Adam J Storm, Christian Marcelo Garciaarellano, Scott Fadden
    Abstract:

    The DB2 Design Advisor in IBM® DB2® Universal DatabaseTM (DB2 UDB) Version 8.2 for Linux®, UNIX® and Windows® is a tool that, for a given workload, automatically recommends physical Design features that are any subset of indexes, materialized query tables (also called materialized views), shared-nothing database partitionings, and multidimensional clustering of tables. Our work is the very first industrial-strength tool that covers the Design of as many as four different features, a significant advance to existing tools, which support no more than just indexes and materialized views. Building such a tool is challenging, because of not only the large search space introduced by the interactions among features, but also the extensibility needed by the tool to support additional features in the future. We adopt a novel "hybrid" approach in the Design Advisor that allows us to take important interdependencies into account as well as to encapsulate Design features as separate components to lower the reengineering cost. The Design Advisor also features a built-in module that automatically reduces the given workload, and therefore provides great scalability for the tool. Our experimental results demonstrate that our tool can quickly provide good physical Design recommendations that satisfy users' requirements.

  • VLDB - DB2 Design Advisor: integrated automatic physical database Design
    Proceedings 2004 VLDB Conference, 2004
    Co-Authors: Daniel C. Zilio, Jun Rao, Sam Lightstone, Guy M Lohman, Adam J Storm, Christian Garcia-arellano, Scott Fadden
    Abstract:

    The DB2 Design Advisor in IBM® DB2® Universal DatabaseTM (DB2 UDB) Version 8.2 for Linux®, UNIX® and Windows® is a tool that, for a given workload, automatically recommends physical Design features that are any subset of indexes, materialized query tables (also called materialized views), shared-nothing database partitionings, and multidimensional clustering of tables. Our work is the very first industrial-strength tool that covers the Design of as many as four different features, a significant advance to existing tools, which support no more than just indexes and materialized views. Building such a tool is challenging, because of not only the large search space introduced by the interactions among features, but also the extensibility needed by the tool to support additional features in the future. We adopt a novel "hybrid" approach in the Design Advisor that allows us to take important interdependencies into account as well as to encapsulate Design features as separate components to lower the reengineering cost. The Design Advisor also features a built-in module that automatically reduces the given workload, and therefore provides great scalability for the tool. Our experimental results demonstrate that our tool can quickly provide good physical Design recommendations that satisfy users' requirements.

  • recommending materialized views and indexes with the ibm db2 Design Advisor
    International Conference on Autonomic Computing, 2004
    Co-Authors: Daniel C. Zilio, Calisto Zuzarte, Sam Lightstone, Guy M Lohman, R J Cochrane, Hamid Pirahesh, Latha S Colby, Jarek Gryz, E Alton, Gary Valentin
    Abstract:

    Materialized views (MVs) and indexes both significantly speed query processing in database systems, but consume disk space and need to be maintained when updates occur. Choosing the best set of MVs and indexes to create depends upon the workload, the database, and many other factors, which makes the decision intractable for humans and computationally challenging for computer algorithms. Even heuristic-based algorithms can be impractical in real systems. In this paper, we present an advanced tool that uses the query optimizer itself to both suggest and evaluate candidate MVs and indexes, and a simple, practical, and effective algorithm for rapidly finding good solutions even for large workloads. The algorithm trades off the cost for updates and storing each MV or index against its benefit to queries in the workload. The tool autonomically captures the workload, database, and system information, optionally permits sampling of candidate MVs to better estimate their size, and exploits multi-query optimization to construct candidate MVs that will benefit many queries, over which their maintenance cost can then be amortized cost-effectively. We describe the Design of the system and present initial experiments that confirm the quality of its results on a database and workload drawn from a real customer database.

  • ICAC - Recommending materialized views and indexes with the IBM DB2 Design Advisor
    International Conference on Autonomic Computing 2004. Proceedings., 1
    Co-Authors: Daniel C. Zilio, Calisto Zuzarte, Sam Lightstone, Guy M Lohman, R J Cochrane, Hamid Pirahesh, Latha S Colby, Jarek Gryz, E Alton
    Abstract:

    Materialized views (MVs) and indexes both significantly speed query processing in database systems, but consume disk space and need to be maintained when updates occur. Choosing the best set of MVs and indexes to create depends upon the workload, the database, and many other factors, which makes the decision intractable for humans and computationally challenging for computer algorithms. Even heuristic-based algorithms can be impractical in real systems. In this paper, we present an advanced tool that uses the query optimizer itself to both suggest and evaluate candidate MVs and indexes, and a simple, practical, and effective algorithm for rapidly finding good solutions even for large workloads. The algorithm trades off the cost for updates and storing each MV or index against its benefit to queries in the workload. The tool autonomically captures the workload, database, and system information, optionally permits sampling of candidate MVs to better estimate their size, and exploits multi-query optimization to construct candidate MVs that will benefit many queries, over which their maintenance cost can then be amortized cost-effectively. We describe the Design of the system and present initial experiments that confirm the quality of its results on a database and workload drawn from a real customer database.

Guy M Lohman - One of the best experts on this subject based on the ideXlab platform.

  • db2 Design Advisor integrated automatic physical database Design
    Very Large Data Bases, 2004
    Co-Authors: Daniel C. Zilio, Jun Rao, Sam Lightstone, Guy M Lohman, Adam J Storm, Christian Marcelo Garciaarellano, Scott Fadden
    Abstract:

    The DB2 Design Advisor in IBM® DB2® Universal DatabaseTM (DB2 UDB) Version 8.2 for Linux®, UNIX® and Windows® is a tool that, for a given workload, automatically recommends physical Design features that are any subset of indexes, materialized query tables (also called materialized views), shared-nothing database partitionings, and multidimensional clustering of tables. Our work is the very first industrial-strength tool that covers the Design of as many as four different features, a significant advance to existing tools, which support no more than just indexes and materialized views. Building such a tool is challenging, because of not only the large search space introduced by the interactions among features, but also the extensibility needed by the tool to support additional features in the future. We adopt a novel "hybrid" approach in the Design Advisor that allows us to take important interdependencies into account as well as to encapsulate Design features as separate components to lower the reengineering cost. The Design Advisor also features a built-in module that automatically reduces the given workload, and therefore provides great scalability for the tool. Our experimental results demonstrate that our tool can quickly provide good physical Design recommendations that satisfy users' requirements.

  • VLDB - DB2 Design Advisor: integrated automatic physical database Design
    Proceedings 2004 VLDB Conference, 2004
    Co-Authors: Daniel C. Zilio, Jun Rao, Sam Lightstone, Guy M Lohman, Adam J Storm, Christian Garcia-arellano, Scott Fadden
    Abstract:

    The DB2 Design Advisor in IBM® DB2® Universal DatabaseTM (DB2 UDB) Version 8.2 for Linux®, UNIX® and Windows® is a tool that, for a given workload, automatically recommends physical Design features that are any subset of indexes, materialized query tables (also called materialized views), shared-nothing database partitionings, and multidimensional clustering of tables. Our work is the very first industrial-strength tool that covers the Design of as many as four different features, a significant advance to existing tools, which support no more than just indexes and materialized views. Building such a tool is challenging, because of not only the large search space introduced by the interactions among features, but also the extensibility needed by the tool to support additional features in the future. We adopt a novel "hybrid" approach in the Design Advisor that allows us to take important interdependencies into account as well as to encapsulate Design features as separate components to lower the reengineering cost. The Design Advisor also features a built-in module that automatically reduces the given workload, and therefore provides great scalability for the tool. Our experimental results demonstrate that our tool can quickly provide good physical Design recommendations that satisfy users' requirements.

  • recommending materialized views and indexes with the ibm db2 Design Advisor
    International Conference on Autonomic Computing, 2004
    Co-Authors: Daniel C. Zilio, Calisto Zuzarte, Sam Lightstone, Guy M Lohman, R J Cochrane, Hamid Pirahesh, Latha S Colby, Jarek Gryz, E Alton, Gary Valentin
    Abstract:

    Materialized views (MVs) and indexes both significantly speed query processing in database systems, but consume disk space and need to be maintained when updates occur. Choosing the best set of MVs and indexes to create depends upon the workload, the database, and many other factors, which makes the decision intractable for humans and computationally challenging for computer algorithms. Even heuristic-based algorithms can be impractical in real systems. In this paper, we present an advanced tool that uses the query optimizer itself to both suggest and evaluate candidate MVs and indexes, and a simple, practical, and effective algorithm for rapidly finding good solutions even for large workloads. The algorithm trades off the cost for updates and storing each MV or index against its benefit to queries in the workload. The tool autonomically captures the workload, database, and system information, optionally permits sampling of candidate MVs to better estimate their size, and exploits multi-query optimization to construct candidate MVs that will benefit many queries, over which their maintenance cost can then be amortized cost-effectively. We describe the Design of the system and present initial experiments that confirm the quality of its results on a database and workload drawn from a real customer database.

  • ICAC - Recommending materialized views and indexes with the IBM DB2 Design Advisor
    International Conference on Autonomic Computing 2004. Proceedings., 1
    Co-Authors: Daniel C. Zilio, Calisto Zuzarte, Sam Lightstone, Guy M Lohman, R J Cochrane, Hamid Pirahesh, Latha S Colby, Jarek Gryz, E Alton
    Abstract:

    Materialized views (MVs) and indexes both significantly speed query processing in database systems, but consume disk space and need to be maintained when updates occur. Choosing the best set of MVs and indexes to create depends upon the workload, the database, and many other factors, which makes the decision intractable for humans and computationally challenging for computer algorithms. Even heuristic-based algorithms can be impractical in real systems. In this paper, we present an advanced tool that uses the query optimizer itself to both suggest and evaluate candidate MVs and indexes, and a simple, practical, and effective algorithm for rapidly finding good solutions even for large workloads. The algorithm trades off the cost for updates and storing each MV or index against its benefit to queries in the workload. The tool autonomically captures the workload, database, and system information, optionally permits sampling of candidate MVs to better estimate their size, and exploits multi-query optimization to construct candidate MVs that will benefit many queries, over which their maintenance cost can then be amortized cost-effectively. We describe the Design of the system and present initial experiments that confirm the quality of its results on a database and workload drawn from a real customer database.