Abstract
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.
Highlights
In the modern manufacturing ecosystem, user behavior analysis plays an essential role in capturing user-specific information, expanding cognition and enhancing the ability to provide customized user experiences
In this contribution, we introduced a novel approach for building user behavior profile models from distributed and heterogeneous information systems in a mass customization environment
This paper focuses on modeling the semantic activity space, where similar features are mapped into an integrated and comprehensive user profile
Summary
In the modern manufacturing ecosystem, user behavior analysis plays an essential role in capturing user-specific information, expanding cognition and enhancing the ability to provide customized user experiences. Mass customization [1], referring to the capability to produce customized goods for individual end-users or a mass-market, has become one of the most crucial factors in business competition. To fulfill user requirements with high diversity and heterogeneity, it is necessary to classify customers into different categories and create a group or an individual user profile. Accurate and comprehensive profiles help to allocate the resources, make strategic choices, and provide personalized services. A single-faceted, incomplete and ambiguous user profile, which does not reflect overall aspects, often becomes the cause of inefficient resource allocation and ambiguous confusion of user requirements.
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