User Behavior Analysis by Cross-Domain Log Data Fusion

Modern mass customization production allows user interaction activities to be distributed in the full product life cycle via multiple information systems. Investigating user behaviors across the boundaries of different domains helps to deeply integrate isolated fragmental profiles into a comprehensive one, and therefore can provide multi-dimensional, high-quality and valuable services. However, traditional user behavior analysis models are based on individual user profile information derived from separate domains, such as requirement analysis, design, supply chain, logistics, marketing,
etc.
, which have not considered the whole complexity of mass customization manufacturing. In this paper, we introduce the concept of multi-dimensional semantic activity space, where user behavior features are merged and represented as combined vectors. User behavior patterns are discovered by mining action data extracted from log files in different subsystems in the corresponding domains. We also identify distinct categories of user behaviors in various modules and subsystems in the context of an intelligent manufacturing environment. Experiment results show a strong indication that the proposed approach can be applied to reveal variations in typical behavioral aspects of cross-domain participants, in terms of patterns in resource access, operation tasks, performance assessment,
etc.