49 research outputs found

    Interoperability framework for supporting information-based assistance in the factory

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    The aim of this paper is to propose new interoperability solution, based on Info-Engine framework and web services technology to support data exchange and extraction from PLM system, specially the Windchill tool. This solution will be implemented as a connector module of more generic framework, named Digital Factory Assistant (DFA). The DFA framework aims to provide factory workers by a set of knowledge and information based decision support to improve their activity performance

    On Simplifying Integrated Physical Database Design

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    Yet Another Algorithms for Selecting Bitmap Join Indexes

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    Algebra-Based Approach for Incremental Data Warehouse Partitioning

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    Agent Based Dynamic Data Storage and Distribution in Data Warehouses

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    Effectively and Efficiently Designing and Querying Parallel Relational Data Warehouses on Heterogeneous Database Clusters: The F&A Approach

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    In this paper, a comprehensive methodology for designing and querying Parallel Rational Data Warehouses (PRDW) over database clusters, called Fragmentation & Allocation (F&A) is proposed. F&A assumes that cluster nodes are heterogeneous in processing power and storage capacity, contrary to traditional design approaches that assume that cluster nodes are instead homogeneous, and fragmentation and allocation phases are performed in a simultaneous manner. In classical approaches, two different cost models are used to perform fragmentation and allocation, separately, whereas F&A makes use of one cost model that considers fragmentation and allocation parameters simultaneously. Therefore, according to the F&A methodology proposed, the allocation phase/decision is done at fragmentation. At the fragmentation phase, F&A uses two well-known algorithms, namely Hill Climbing (HC) and Genetic Algorithm (GA), which the authors adapt to the main PRDW design problem over heterogeneous database clusters, as these algorithms are capable of taking into account the heterogeneous characteristics of the reference application scenario. At the allocation phase, F&A introduces an innovative matrix-based formalism capable of capturing the interactions among fragments, input queries, and cluster node characteristics, driving the data allocation task accordingly, and a related affinity-based algorithm, called F&A-ALLOC. Finally, their proposal is experimentally assessed and validated against the widely-known data warehouse benchmark APB-1 release II
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