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Titlebook: Beyond Databases, Architectures and Structures. Facing the Challenges of Data Proliferation and Grow; 14th International C Stanis?aw Koziel

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發(fā)表于 2025-3-21 18:20:10 | 只看該作者 |倒序瀏覽 |閱讀模式
期刊全稱Beyond Databases, Architectures and Structures. Facing the Challenges of Data Proliferation and Grow
期刊簡稱14th International C
影響因子2023Stanis?aw Kozielski,Dariusz Mrozek,Daniel Kostrzew
視頻videohttp://file.papertrans.cn/186/185160/185160.mp4
學(xué)科分類Communications in Computer and Information Science
圖書封面Titlebook: Beyond Databases, Architectures and Structures. Facing the Challenges of Data Proliferation and Grow; 14th International C Stanis?aw Koziel
影響因子This book constitutes the refereed proceedings of the 14th International Conference entitled Beyond Databases, Architectures and Structures, BDAS 2018, held in Poznań, Poland, in September 2018, during the IFIP World Computer Congress..It consists of 38 carefully reviewed papers selected from 102 submissions. The papers are organized in topical sections, namely?big data and cloud computing; architectures, structures and algorithms for efficient?data processing;?artificial intelligence, data mining and knowledge?discovery;?text mining, natural language processing,?ontologies and semantic web;?image analysis and multimedia mining.?.
Pindex Conference proceedings 2018
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The Use of Distributed Data Storage and Processing Systems in Bioinformatic Data Analysisnt premises of cancer occurrence. In this paper a set of data mining tasks is defined that joins the observed genes mutation with the specific cancer type observation. Due to the high computational complexity of this kind of data a Hadoop ecosystem cluster was developed to perform the required calcu
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SIMD Acceleration for Main-Memory Index Structures – A Surveyhese index structures, different approaches are presented by several authors, including horizontal vectorization with SIMD and efficient cache-line usage..In this work, we compare the adapted index structures Seg-Tree/Trie, FAST, VAST, and ART and evaluate the usage of SIMD within these. We extract
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OpenMP as an Efficient Method to Parallelize Code with Dense Synchronization efficiency of the parallel computational model with shared memory, when dense synchronization is required. As our experimental evaluation shows, contemporary CPUs assisted with OpenMP library perform well in case of such tasks. We also present evidence that OpenMP is easy to learn and use.
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