Titlebook: Advances in Databases and Information Systems; 17th East European C Barbara Catania,Giovanna Guerrini,Jaroslav Pokorný Conference proceedin

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https://doi.org/10.1007/978-1-4613-1255-0uring equipment and the widespread adoption of social networking platforms as means for sharing media content online. Due to the very large amounts of multimedia content, notably images, produced and shared online by people, a very important requirement for multimedia indexing approaches pertains to

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G. Khoo,F. Falciani,M. Al-Rubeaieducible subset of attributes that preserves the quality of approximation of a complete decision table. For a decision table a single reduct or many reducts may exist. In typical processing one reduct is selected for the subsequent generation of decision rules, while others can be discarded. Yet whe

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Center for Cell and Gene Therapy (CAGT)tagged social media, linking a location to a set of features or attributes. Mining frequent sets of discrete attributes to discover interesting patterns and rules of attribute usage in such data sets is an important data mining task..In this work we extend the frequent itemset mining framework to mo

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https://doi.org/10.1007/b102110del and efficiently query and match linked open data, primarily represented as RDF graphs from RDF facts and associated ontological frameworks. Interestingly, little work has yet been conducted to discover interesting patterns from such data..In this paper, we present an approach that aims at discov

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- Molecular and Cellular Therapeutics (MCT)er of commodity machines. A significant share of typical MR jobs involves standard database-style queries, where it becomes cumbersome to specify . and . functions from scratch. To overcome this burden, higher-level languages such as HiveQL, PigLatin, and JAQL have been proposed to allow the automat

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