User Behavior Analysis by Cross-Domain Log Data Fusion

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 multidimensional 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.

Users’ Satisfaction with Internet Service Performance-Based on User Log

“…To this end, four KPIs (Distance, Session Time, Upload and Download) were therefore put forward as input variables and three (NASIP Address -Network Access Server Internet Protocols Address, network, and causes of termination) evaluators variables to analyze the level of users' satisfaction from users' log items -data. About 65,000 log items, which more than 120 users generated, were collected within three months following best practice as demonstrated in Tao et al [15] . These log items are the activities of users.…”

“…Users' logs are ALoIU and are also known as event log files or Internet access log files [13][14][15] . It has been relied on as a method to reveal the searching behavior of users [15] , learn about users' events [11] , discover patterns and the characteristics of Internet users' behavior after investigation [14] , and so on.…”

“…Users' logs are ALoIU and are also known as event log files or Internet access log files [13][14][15] . It has been relied on as a method to reveal the searching behavior of users [15] , learn about users' events [11] , discover patterns and the characteristics of Internet users' behavior after investigation [14] , and so on. Some other information, which can be learned from users' logs, includes usage profiles, user engagements, knowledge of what is being searched, and the discovery of implicit linkages and usage patterns [14,15] .…”

“…It has been relied on as a method to reveal the searching behavior of users [15] , learn about users' events [11] , discover patterns and the characteristics of Internet users' behavior after investigation [14] , and so on. Some other information, which can be learned from users' logs, includes usage profiles, user engagements, knowledge of what is being searched, and the discovery of implicit linkages and usage patterns [14,15] . However, to the best of our knowledge, the users' log of a university's Internet service has not been analyzed to identify the presence or absence of users' satisfaction based on Key Performance Indicators (KPIs).…”

Entity Relationship Modeling for IoT Data Fusion Driven by Dynamic Detecting Probe

“…e logs contain correlations among the data, but they are generated by manipulating the data, and only part of the data are involved compared to the overall data, leading to a lack of completeness, and their analysis is unable to explore the correlations that exist in all the data. In this paper, we offer a proposal for extracting the data association information in logs and using it as a basis for matching entity associations in multisource data to construct entity associations in enterprise data space; this approach builds on the feature that logs contain association information between data [19].…”