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Titlebook: Enabling Real-Time Business Intelligence; 5th International Wo Malu Castellanos,Umeshwar Dayal,Wolfgang Lehner Conference proceedings 2012

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Microsoft SQL Server Parallel Data Warehouse: Architecture Overview,sing (MPP), share nothing, scale-out version of SQL Server focused on data warehousing workloads. The product is packaged as a database appliance built on industry standard hardware. We present an overview of the main software and hardware components of the system and an example illustrating its query processing approach.
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Shakuntala Gupta Edward,Navin Sabharwalexes, Blink always performs simple, “brute force” scans of the entire data mart in parallel on all nodes, without using any indexes or materialized views, and without any query optimizer to choose among them. The Blink technology has thus far been incorporated into two products: (1) an accelerator a
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Introducing Networking and Remoting,, a widely used DCP platform. Our contributions include optimized data coherence management along the memory hierarchy, and flexibly configurable page buffering policies for accommodating various workloads. Our preliminary experiments show that the proposed system has the potential to achieve scalab
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Conference proceedings 2012ment, and the optimization of complex ETL workflows..The volume contains 6 research papers, which have been carefully reviewed and selected from 12 submissions, plus the 3 keynotes presented at the workshop. The topics cover all stages of the business intelligence cycle, including capturing of real-
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,Blink: Not Your Father’s Database!,exes, Blink always performs simple, “brute force” scans of the entire data mart in parallel on all nodes, without using any indexes or materialized views, and without any query optimizer to choose among them. The Blink technology has thus far been incorporated into two products: (1) an accelerator a
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Relax and Let the Database Do the Partitioning Online,per, we present .: a self-tuning data store which rather than keeping the DBA in the loop, monitors the current workload and partitions the data automatically at checkpoint time intervals — without human intervention. This allows . to gradually adapt the partitions to best fit the observed query wor
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Adaptive Processing of Multi-Criteria Decision Support Queries,ing a static one at compile time. This is achieved by adaptively partitioning the input data driven by the feedback of the results already generated. Second, Adaptive-SKIN incrementally build the output space, containing the final skyline, without generating a single join result. Our approach select
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