1,341 research outputs found

    View Selection in Semantic Web Databases

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    We consider the setting of a Semantic Web database, containing both explicit data encoded in RDF triples, and implicit data, implied by the RDF semantics. Based on a query workload, we address the problem of selecting a set of views to be materialized in the database, minimizing a combination of query processing, view storage, and view maintenance costs. Starting from an existing relational view selection method, we devise new algorithms for recommending view sets, and show that they scale significantly beyond the existing relational ones when adapted to the RDF context. To account for implicit triples in query answers, we propose a novel RDF query reformulation algorithm and an innovative way of incorporating it into view selection in order to avoid a combinatorial explosion in the complexity of the selection process. The interest of our techniques is demonstrated through a set of experiments.Comment: VLDB201

    An approximate approach for the joint problem of level of repair analysis and spare parts stocking

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    For the spare parts stocking problem, generally METRIC type methods are used in the context of capital goods. A decision is assumed on which components to discard and which to repair upon failure, and where to perform repairs. In the military world, this decision is taken explicitly using the level of repair analysis (LORA). Since the LORA does not consider the availability of the capital goods, solving the LORA and spare parts stocking problems sequentially may lead to suboptimal solutions. Therefore, we propose an iterative algorithm. We compare its performance with that of the sequential approach and a recently proposed, so-called integrated algorithm that finds optimal solutions for twoechelon, single-indenture problems. On a set of such problems, the iterative algorithm turns out to be close to optimal. On a set of multi-echelon, multi-indenture problems, the iterative approach achieves a cost reduction of 3%on average (35%at maximum) as compared to the sequential approach. Its costs are only 0.6 % more than those of the integrated algorithm on average (5 % at maximum). Considering that the integrated algorithm may take a long time without guaranteeing optimality, we believe that the iterative algorithm is a good approach. This result is further strengthened in a case study, which has convinced Thales Nederland to start using the principles behind our algorithm

    Comparison of chemical clustering methods using graph- and fingerprint-based similarity measures

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    This paper compares several published methods for clustering chemical structures, using both graph- and fingerprint-based similarity measures. The clusterings from each method were compared to determine the degree of cluster overlap. Each method was also evaluated on how well it grouped structures into clusters possessing a non-trivial substructural commonality. The methods which employ adjustable parameters were tested to determine the stability of each parameter for datasets of varying size and composition. Our experiments suggest that both graph- and fingerprint-based similarity measures can be used effectively for generating chemical clusterings; it is also suggested that the CAST and Yin–Chen methods, suggested recently for the clustering of gene expression patterns, may also prove effective for the clustering of 2D chemical structures
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