大数据建模工具箱综述

2016 International Conference on Military Communications and Information Systems (ICMCIS) Pub Date : 2016-05-23 DOI:10.1109/ICMCIS.2016.7496565

Hadi Hashem, D. Ranc

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引用次数: 3

摘要

建模工具和操作符帮助用户/开发人员识别序列顶部的处理字段，并仅将与请求结果相关的数据发送到计算模块。剩下的数据是不相关的，它会减慢处理速度。目前最大的挑战是在减少计算时间和成本的情况下获得高质量的处理结果。处理顺序必须在顶部进行检查，以便我们可以添加一个或多个建模工具。现有的处理模型没有考虑到这方面的问题，而是一味追求较高的计算性能，从而增加了计算时间和成本。在本文中，我们为您提供了大数据的主要建模工具的研究。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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A review of modeling toolbox for BigData

Modeling tools and operators help the user / developer to identify the processing field on the top of the sequence and to send into the computing module only the data related to the requested result. The remaining data is not relevant and it will slow down the processing. The biggest challenge nowadays is to get high quality processing results with a reduced computing time and costs. The processing sequence must be reviewed on the top, so that we could add one or more modeling tools. The existing processing models do not take in consideration this aspect and focus on getting high calculation performances which will increase the computing time and costs. In this paper we provide you a study of the main modeling tools for BigData.

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2016 International Conference on Military Communications and Information Systems (ICMCIS)

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