收益管理中基于大数据仓库的需求无约束估计：框架与挑战

doi:10.11959/j.issn.2096-0271.2017031

大数据 ›› 2017, Vol. 3 ›› Issue (3): 68-83.doi: 10.11959/j.issn.2096-0271.2017031

收益管理中基于大数据仓库的需求无约束估计：框架与挑战

郭鹏

贵阳学院经济管理学院，贵州贵阳 550005

出版日期:2017-05-20 发布日期:2017-05-31
作者简介:郭鹏（1984-），男，博士，贵阳学院经济管理学院讲师，主要研究方向为收益管理理论与方法、需求无约束估计和预测、系统仿真优化、大数据分析。2006年起至今，从事有关收益管理、系统仿真优化方面的研究，作为第一作者在《Advances in Operations Research》《系统工程理论与实践》《系统科学与数学》《数理统计与管理》《计算机仿真》等期刊上发表十余篇论文，主持和参与了多项课题：2010—2014年参与完成了国家自然科学基金委员会重大项目（No.71090402）；2015年主持并完成了贵阳市科协软科学研究项目（No. 2015B23），同年主持了国家社会科学基金一般项目（No. 15BGL198）；2016年主持了贵州省教育厅高校人文社会科学研究自筹项目（No. 2016ZC021）、贵阳市科学技术协会软科学研究项目（No. 2016A01）和贵阳学院院级科研项目（No. GYXY[2016]24）。
基金资助:
国家社会科学基金资助项目(15BGL198);贵州省教育厅高校人文社会科学研究基金资助项目(2016ZC021);贵阳市科学技术协会软科学研究基金资助项目(2016A01)

Demand unconstraining estimation based on big data warehouse in revenue management systems:framework and challenges

Peng GUO

School of Economics and Management,Guiyang University,Guiyang 550005,China

Online:2017-05-20 Published:2017-05-31
Supported by:
The National Social Science Foundation of China(15BGL198);The Colleges ＆ Universities Humanities and Social Sciences Research Project of Educational Commission of Guizhou Province of China(2016ZC021);Soft Science Research Project of Guiyang Association of Science and Technology(2016A01)

摘要/Abstract

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。

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

Abstract:

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.

Key words: revenue management, demand forecasting, unconstraining estimation, data warehouse, big data, business intelligence, sentiment analysis

中图分类号:

郭鹏. 收益管理中基于大数据仓库的需求无约束估计：框架与挑战[J]. 大数据, 2017, 3(3): 68-83.

Peng GUO. Demand unconstraining estimation based on big data warehouse in revenue management systems:framework and challenges[J]. Big Data Research, 2017, 3(3): 68-83.

图/表 3

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收益管理中基于大数据仓库的需求无约束估计：框架与挑战

Demand unconstraining estimation based on big data warehouse in revenue management systems:framework and challenges

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