Ranking-Based Business Information Processing: Applications to Business Solutions and e-Commerce Systems
Mao Chen, Jakka Sairamesh · 2006
Extracting crucial information in high volume business data efficiently are critical for enterprises to make timely business decisions and adapt accordingly. This paper proposes a novel ranking-based system that applies knowledge models and utility functions. In a case study for monitoring and analyzing automotive failures in aftermarket services, we shed a light on our ranking mechanism that combines objective business metrics and "subjective" domain knowledge. Our experiments using real-world data demonstrate that our methodology is capable of capturing macro view about business performance issues from a small but important fraction of information.