Ranking-based business information processing: applications to business solutions and eCommerce systems

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.