Predictive Business Analytics for Incomplete Data

商业分析是指对相关数据进行持续迭代探索和调查，以改善公司业绩的过程。商业分析依赖于商业洞察和专业知识、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性分析，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市预测、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，预测分析通常使用传统的统计、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，现实生活中的商业数据通常包含遗漏的值。例如，当直接从客户那里收集数据时，许多客户可能出于隐私考虑而不愿提供所有信息。在研究文献中，有缺失值的数据被称为不完整数据。这个项目的主要目标是为不完整的数据开发新的预测分析方法。这些提出的方法将通过各种商业应用进行研究和验证。这个项目的结果将对商业分析的理论和应用做出重大贡献。我们预计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。

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.