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Titlebook: Beyond Databases, Architectures and Structures. Towards Efficient Solutions for Data Analysis and Kn; 13th International C Stanis?aw Koziel

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期刊全稱Beyond Databases, Architectures and Structures. Towards Efficient Solutions for Data Analysis and Kn
期刊簡稱13th International C
影響因子2023Stanis?aw Kozielski,Dariusz Mrozek,Daniel Kostrzew
視頻videohttp://file.papertrans.cn/186/185162/185162.mp4
發(fā)行地址Includes supplementary material: .Includes supplementary material:
學(xué)科分類Communications in Computer and Information Science
圖書封面Titlebook: Beyond Databases, Architectures and Structures. Towards Efficient Solutions for Data Analysis and Kn; 13th International C Stanis?aw Koziel
影響因子This book constitutes the refereed proceedings of the 13th International Conference entitled Beyond Databases, Architectures and Structures, BDAS 2017, held in Ustroń, Poland, in May/June 2017..It consists of 44 carefully reviewed papers selected from 118 submissions. The papers are organized in topical sections, namely big data and cloud computing; artificial intelligence, data mining and knowledge?discovery;?architectures, structures and algorithms for efficient?data processing;?text mining, natural language processing,?ontologies and semantic web;?bioinformatics and biological data analysis;?industrial applications;?data mining tools, optimization and compression..
Pindex Conference proceedings 2017
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Integrating Map-Reduce and Stream-Processing for Efficiency (MRSP)se (Map-Reduce or not) to keep results available on the fly avoiding high query execution times, and, this way leaving the DW servers more available to process other heavy tasks (e.g. data mining)..In this work, we propose the integration of Stream Processing and Map-Reduce (MRSP) for better query a
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Data Mining - A Tool for Migration Stock Prediction The second goal is to assess these models and to identify those aspects that have most influence in the decision-making process. The result is based on the data completed by more than 100 persons from Rennes.
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International Handbook of Educational Changeers offer a mechanism of auto scaling which dynamically adjusts the number of virtual server instances, on which given system is running, according to some basic resource-based metrics like CPU utilization. In this work, we propose a model of auto scaling which is based on timing statistics: a high
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