Predictive Business Analytics for Incomplete Data

针对不完整数据的预测业务分析

• 批准号: RGPIN-2019-05787
• 负责人: Wang, Hai
• 金额: $ 1.56万
• 依托单位: Saint Mary's University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=753458
• 关键词: Predictive; Business; Analytics; Incomplete; Data

Business analytics refers to the process of continuous iterative exploration and investigation of relevant data to improve a company's performance. Business analytics relies on business insights and expert knowledge, analytics methodologies and technologies, as well as a huge amount of relevant data. A common type of business analytics is predictive analytics which involves exploiting business data to make predictions. Popular applications of predictive analytics include stock market prediction, customer lead generation, marketing campaign prediction, product bundling. The proposed project will focus on the theories and applications of predictive analytics. Currently, predictive analytics typically employs traditional statistics, data mining, artificial intelligence and machine learning techniques for analyzing data. Many of these techniques generally require that there is no missing values in the data. However, real-life business data often contain missing values. For example, when data are collected from customers directly, many customers may be unwilling to provide all information because of privacy concerns. In the research literature, data with missing values are referred to as "incomplete data". The primary objective of this project is to develop new predictive analytics methods for incomplete data. These proposed methods will be studied and validated through various business applications. Results from this project will significantly contribute toward the theories and applications of business analytics. We expect that the research results will be presented at international conferences and published in journals in the fields of computer science and business administration.

业务分析是指对相关数据进行持续迭代探索和调查以提高公司绩效的过程。业务分析依赖于业务洞察力和专业知识、分析方法和技术以及大量相关数据。一种常见的业务分析类型是预测分析，它涉及利用业务数据进行预测。预测分析的流行应用包括股票市场预测，客户线索生成，营销活动预测，产品捆绑。拟议的项目将侧重于预测分析的理论和应用。目前，预测分析通常采用传统的统计、数据挖掘、人工智能和机器学习技术来分析数据。这些技术中的许多通常要求数据中没有缺失值。然而，现实生活中的业务数据通常包含缺失值。例如，当直接从客户收集数据时，许多客户可能由于隐私问题而不愿意提供所有信息。在研究文献中，含有缺失值的数据被称为“不完整数据”。 该项目的主要目标是为不完整数据开发新的预测分析方法。这些方法将通过各种业务应用进行研究和验证。该项目的成果将对商业分析的理论和应用做出重大贡献。我们希望研究成果将在国际会议上发表，并在计算机科学和工商管理领域的期刊上发表。