• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Journal of advances in management research >Sampling-based estimation method for parameter estimation in big data business era
【24h】

Sampling-based estimation method for parameter estimation in big data business era

机译：大数据业务时代参数估计的基于采样的估计方法

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Purpose - This paper aims to present sample-based estimation methodologies to compute the confidence interval for the mean size of the content of material communicated on the digital social media platform in presence of volume, velocity and variety. Confidence interval acts as a tool of machine learning and managerial decision-making for coping up big data. Design/methodology/approach - Random sample-based sampling design methodology is adapted and mean square error is computed on the data set. Confidence intervals are calculated using the simulation over multiple data sets. The smallest length confidence interval is the selection approach for the most efficient in the scenario of big data.Findings - Resultants of computations herein help to forecast the future need of web-space at data-centers for anticipation, efficient management, developing a machine learning algorithm for predicting better quality of service to users. Finding supports to develop control limits as an alert system for better use of resources (memory space) at data centers. Suggested methodologies are efficient enough for future prediction in big data setup.Practical implications - In IT sector, the startup with the establishment of data centers is the current trend of business. Findings herein may help to develop a forecasting system and alert system for optimal decision-making in the enhancement and share of the business.Originality/value - The contribution is an original piece of thought, idea and analysis, deriving motivation from references appended.
机译：目的 - 本文旨在呈现基于样品的估计方法，以计算在体积，速度和品种的存在下在数字社交媒体平台上传达的材料含量的平均尺寸的置信区间。置信区间作为机器学习的工具和管理决策，用于应对大数据。设计/方法/方法 - 基于随机的基于样品的采样设计方法，并在数据集上计算均方误差。使用多个数据集的模拟计算置信区间。最小的长度置信区间是大数据场景中最有效的选择方法预测用户更好地服务质量的算法。寻找支持以制定控制限制作为警报系统，以便更好地使用数据中心的资源（内存空间）。建议的方法是效率足以让未来的大数据设置中的预测。实际意义 - 在IT部门中，与建立数据中心的启动是当前业务趋势。这里的结果可以有助于开发预测系统和警报系统，以获得业务的增强和份额的最佳决策.解/价值 - 贡献是一个原始的思想，想法和分析，从附加的参考资料中导出动机。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号