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    • 4. 发明授权
    • Integrated distributed query processor for data grids
    • 用于数据网格的集成分布式查询处理器
    • US08103656B2
    • 2012-01-24
    • US12389473
    • 2009-02-20
    • Srikumar KrishnamoorthyAvdhoot Kishore SaplePrahalad Haldhoderi Achutharao
    • Srikumar KrishnamoorthyAvdhoot Kishore SaplePrahalad Haldhoderi Achutharao
    • G06F7/00
    • G06F17/30463G06F17/30545
    • A method for processing a distributed query in a network of computational resources is provided. The method includes receiving a user-defined freshness criterion and a distributed query from a user. The user-defined data freshness criterion is based on the version of the distributed query results desired by the user. An integrated cost model is formulated to optimize the execution of the distributed query. The integrated cost model is based on one or more integrated cost model factors. Thereafter, an objective function is constructed, based on the processing cost for each of the one or more copies of the one or more relations, and a data transmission cost for the transfer of the one or more copies of the one or more relations from a first to a second computational resource. Subsequently, an optimal solution of the objective function is calculated by using one or more heuristic approaches.
    • 提供了一种在计算资源网络中处理分布式查询的方法。 该方法包括从用户接收用户定义的新鲜度标准和分布式查询。 用户定义的数据新鲜度标准基于用户期望的分布式查询结果的版本。 制定综合成本模型来优化分布式查询的执行。 综合成本模型基于一个或多个综合成本模型因素。 此后,基于一个或多个关系的一个或多个副本的处理成本构建目标函数,以及用于从一个或多个关系的一个或多个关系的一个或多个副本的转移的数据传输成本 首先到第二个计算资源。 随后,通过使用一种或多种启发式方法来计算目标函数的最优解。
    • 6. 发明申请
    • ALLOCATING RESOURCES FOR PARALLEL EXECUTION OF QUERY PLANS
    • 分配资源并行执行查询计划
    • US20090254916A1
    • 2009-10-08
    • US12192061
    • 2008-08-14
    • Sumit Kumar BoseSrikumar KrishnamoorthyNilesh Dilip Ranade
    • Sumit Kumar BoseSrikumar KrishnamoorthyNilesh Dilip Ranade
    • G06F9/50
    • G06F9/5066G06F17/30477
    • Computing resources can be assigned to sub-plans within a query plan to effect parallel execution of the query plan. For example, computing resources in a grid can be represented by nodes, and a shortest path technique can be applied to allocate machines to the sub-plans. Computing resources can be provisionally allocated as the query plan is divided into query plan segments containing one or more sub-plans. Based on provisional allocations to the segments, the computing resources can then be allocated to the sub-plans within respective segments. Multiprocessor computing resources can be supported. The techniques can account for data locality. Both pipelined and partitioned parallelism can be addressed. Described techniques can be particularly suited for efficient execution of bushy query plans in a grid environment. Parallel processing will reduce the overall response time of the query.
    • 计算资源可以分配给查询计划中的子计划,以实现查询计划的并行执行。 例如,网格中的计算资源可以由节点表示,并且可以应用最短路径技术来将子机分配给子计划。 可以临时分配计算资源,因为查询计划被划分为包含一个或多个子计划的查询计划段。 基于对段的临时分配,然后可以将计算资源分配给各个段内的子计划。 可以支持多处理器计算资源。 这些技术可以解释数据的局部性。 可以解决流水线和分区并行性问题。 描述的技术可以特别适合于在网格环境中有效执行浓密的查询计划。 并行处理将减少查询的整体响应时间。