摘要
现有需求无约束估计方法均为基于公司内部数据仓库中所获需求信息而开发,在当前基于大数据分析的激烈竞争市场环境中,无法满足收益管理系统日益增长的实时需求预测和优化决策分析需要。为了实时、动态地同时获取并分析内部和外部数据资源中有关每位顾客的无约束需求数据,包括结构化和非结构化的信息,提出了以面向收益管理需求无约束估计为主题的大数据仓库框架,并据此讨论了无约束需求知识挖掘以及需求无约束估计商务智能分析工具开发应用过程中面临的各项挑战。
In order to achieve and exam the unconstrained demand data related to each customer in real time and dynamically from both internal and external data resources, including structured and unstructured information, a big data warehouse framework for demand unconstraining estimation in revenue management was proposed. After that, the challenges in unconstrained demand knowledge mining, as well as the process of development and application of business intelligence analysis tools for demand unconstraining estimation were discussed.
作者
郭鹏
GUO Peng(School of Economics and Management, Guiyang University, Guiyang 550005, Chin)
出处
《大数据》
2017年第3期68-83,共16页
Big Data Research
基金
国家社会科学基金资助项目(No.15BGL198)
贵州省教育厅高校人文社会科学研究基金资助项目(No.2016ZC021)
贵阳市科学技术协会软科学研究基金资助项目(No.2016A01)~~
关键词
收益管理
需求预测
无约束估计
数据仓库
大数据
商务智能
情感分析
revenue management, demand forecasting, unconstraining estimation, data warehouse, big data, business intelligence,sentiment analysis