报告题目：Data Acquisition for Business Analytics
报 告 人：李晓白（UMASS Lowell商学院教授）、大连理工大学海天学者特聘教授
报告摘要：Data acquisition is an important task in data-driven business analytics. This work studies data acquisition problem concerning both data quality and acquisition cost. We propose using a divide-and-conquer technique to characterize the empirical distribution of the target population. We explore the idea of using auctions to acquire customer data and analyze the acquisition cost. We formulate the acquisition problem as an optimization problem that maximizes the utility of the acquired data while satisfying acquisition budget. For descriptive analytics, we derive the analytical form of the solution for the optimization problem, which finds a set of records that best represent the target population. For predictive analytics with linear regression, the objective is to minimize prediction error. We translate the optimization problem to a mathematical programming problem that can be solved efficiently. An experimental study has been conducted to demonstrate the effectiveness of our approach.
报告人简介：Dr. Xiaobai Li is a Professor of Information Systems in the Department of Operations and Information Systems at the University of Massachusetts Lowell, USA. Li’s research focuses on data mining and analytics, data privacy, and information economics. He has received funding for his research from National Institutes of Health (NIH) and National Science Foundation (NSF). His work has appeared in Management Science, Information Systems Research, MIS Quarterly, Operations Research, IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Systems, Man, and Cybernetics, Communications of the ACM, INFORMS Journal on Computing, Decision Support Systems, and European Journal of Operational Research, among others.