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A NoSQL Database Approach for Modeling Heterogeneous and Semi-Structured Information

机译:一种用于建模异构和半结构化信息的NoSQL数据库方法

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Nowadays there is a growing need for collecting and processing data from different sources in heterogeneous and semistructured formats. Scientists and companies are strongly urged to find a way for extracting knowledge out of them. In this paper, we present a NoSQL database approach for modeling heterogeneous and semi-structured information in both software architecture and data modeling aspects. We built a robust analytics framework by integrating Apache Spark with Apache Cassandra and in following utilize data mining techniques for presenting a model capable of predicting the relationship between tourist arrivals and nights spent in Greece. The proposed model puts to use a constructed dataset both from the Hellenic Statistical Authority and Eurostat. The evaluation shows that the proposed data model, used for fitting the current dataset, predicts tourist behaviour with high accuracy.
机译:如今,越来越需要以异构和半结构化格式从不同来源收集和处理数据。强烈敦促科学家和公司找到一种从中提取知识的方法。在本文中,我们提出了一种NoSQL数据库方法,用于在软件体系结构和数据建模方面对异构和半结构化信息进行建模。我们通过将Apache Spark与Apache Cassandra集成在一起,并随后利用数据挖掘技术提出了一个模型,该模型可以预测游客到访和在希腊度过的夜晚之间的关系,从而构建了一个强大的分析框架。提议的模型使用了来自希腊统计局和欧盟统计局的构建数据集。评估表明,所提出的数据模型用于拟合当前数据集,可高度准确地预测旅游者的行为。

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