Journal Article10.1016/J.IS.2018.02.010
Computational methods and optimizations for containment and complementarity in web data cubes
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TL;DR: This paper defines three types of relationships between multidimensional observations, namely full containment, partial containment and complementarity, and proposes four methods for efficient and scalable computation of these relationships, and conducts an extensive experimental evaluation over both real and synthetic datasets.
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About: This article is published in Information Systems. The article was published on 01 Jun 2018. The article focuses on the topics: Disparate system & RDF.
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TL;DR: In this paper , the authors propose a technique for merging CSs based on their hierarchical structure, which employs a lattice to capture the hierarchical relationships between CSs and merges dense CSs with their ancestors.
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Data Service API Design for Data Analytics
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•Proceedings Article
Hierarchical Property Set Merging for SPARQL Query Optimization.
Marios Meimaris,George Papastefanatos,Panos Vassiliadis +2 more
- 01 Jan 2020
TL;DR: This paper introduces a novel technique, for merging CSs based on their hierarchical structure, which employs a lattice to capture the hierarchical relationships between CSs, identifies dense CSs and merges denseCSs with their ancestors, thus reducing the size of the CSs as well as the links between them.
Managing, querying and analyzing big data on the web
Marios Meimaris,Μάριος Μεϊμάρης +1 more
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TL;DR: The RDF’s Resource Description Framework aims to provide real-time information about products and services available to buyers and sellers through a cataloguing and evaluation system.
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References
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Franz Baader,Diego Calvanese,Deborah L. McGuinness,Daniele Nardi,Peter F. Patel-Schneider +4 more
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TL;DR: The Description Logic Handbook as mentioned in this paper provides a thorough account of the subject, covering all aspects of research in this field, namely: theory, implementation, and applications, and can also be used for self-study or as a reference for knowledge representation and artificial intelligence courses.
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X-means: Extending K-means with Efficient Estimation of the Number of Clusters
Dan Pelleg,Andrew W. Moore +1 more
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TL;DR: A new algorithm is introduced that eeciently, searches the space of cluster locations and number of clusters to optimize the Bayesian Information Criterion (BIC) or the Akaike Information Criteria (AIC) measure.
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TL;DR: In this article, a lattice framework is used to express dependencies among views and greedy algorithms are presented to determine a good set of views to materialize, with a small constant factor of optimal.
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