Titlebook: Database and Expert Systems Applications; 29th International C Sven Hartmann,Hui Ma,Roland R. Wagner Conference proceedings 2018 Springer N

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GARUM: A Semantic Similarity Measure Based on Machine Learning and Entity Characteristicsent domains, e.g., networks of proteins and media news. In the experimental study, GARUM exhibits higher correlation with gold standards than studied existing approaches. Thus, these results suggest that similarity measures should not consider . in isolation; contrary, combinations of these characte

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Knowledge Graphs for Semantically Integrating Cyber-Physical Systems . on a benchmark of AutomationML documents describing CPS components from various perspectives. Results suggest that . enables not only the semantic integration of the descriptions of CPS components, but also allows for preserving the individual characterization of these components.

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Conference proceedings 2018ion; data warehouses and recommender systems; data streams; information networks and algorithms; database system architecture and performance; novel database solutions; graph querying and databases; learning; emerging applications;data mining; privacy; and text processing.

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Asymptotic Normality and Exponential Boundsof the entities in an RDF dataset and their privacy regulations. Furthermore, BOUNCER implements query decomposition and optimization techniques able to identify query plans over RDF datasets that not only contain the relevant entities to answer a query, but that are also regulated by policies that

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Asymptotic Normality and Exponential Boundsy. By using in-memory processing to handle the attribute selection procedure, we significantly reduce the processing time required. We evaluated the effectiveness of our proposed approach with an enriched dataset drawn from multiple real-world data sources, and augmented with synthetic values genera

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Continuum Analysis of Biological Systemsdivided into several subsets called grids, and the aggregated values for each grid are precomputed. This technique improves query processing performance by reducing the amount of scanned data. We evaluated the efficiency of the proposed method by comparing its performance with current state-of-the-a

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Continuum Analysis of Biological Systemsper proposes RWRDoc, an RWR (random walk with restart)-based representation learning method which learns representations of entities by weighted combinations of minimal representations of whole reachable entities w.r.t. RWR. Comprehensive experiments on diverse applications (such as ad-hoc entity se

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