Unsupervised Clickstream Clustering for User Behavior Analysis

Wang, Gang; Zhang, Xinyi; Tang, Shiliang; Zheng, Haitao; Zhao, Ben Y.

doi:10.1145/2858036.2858107

Cited by 188 publications

(126 citation statements)

References 33 publications

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“…From the perspectives of the system behavior, Wang [14] pointed out that it is vitally important to understand user behaviors in online services and further proposed an unsupervised system based on the click traffic to check the modes of user behaviors. Luo [15] used fuzzy Petri nets to represent the fuzzy production rules, and performed a state analysis of power systems by an iterative computation of matrices.…”

Section: Related Workmentioning

confidence: 99%

Behavior Reconstruction Models for Large-scale Network Service Systems

Zhang

Liu

Wang

et al. 2018

Peer-to-Peer Netw. Appl.

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In large-scale network service systems, the phenomenon of instantaneous gathering of a large number of users can cause system abnormality, whenever the load imposed by the user behaviors does not match the system load. This paper proposes a behavior reconstruction model for large-scale network service systems integrated with Petri net reconstruction methodology, for the purpose of achieving load balancing in the system under increasing number of users. Based on the features of the user interaction behavior sequence, the behavioral load balancing model defines a user behavior membership function. Then, a random fuzzy Petri net with delay is presented to control the user behavior reconstruction. Experiments conducted by considering various changes in the number of user behaviors and their distribution in unit time demonstrate that the proposed methodology can effectively trigger the reconstructed model to balance the system load when the system load exceeds the defined warning point.

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Section: Related Workmentioning

confidence: 99%

Behavior Reconstruction Models for Large-scale Network Service Systems

Zhang

Liu

Wang

et al. 2018

Peer-to-Peer Netw. Appl.

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“…The software vendors are also interested in their customer behaviour to understand how end-users use the application. For this purpose, user behaviour knowledge is collected from analysing the users interaction with the web-browser while accessing the application in the form of clickstreams (Pachidi et al, 2014;Wang et al, 2016). …”

Section: Saas Software Developmentmentioning

confidence: 99%

“…From the literature exploration, we see that the idea of monitoring user behaviour is to understand how users interact with the application and this is mainly done through analysing the clickstreams (Pachidi et al, 2014;Wang et al, 2016;Banerjee and Ghosh, 2001;Bucklin and Sismeiro, 2009). The authors (Cito et al, 2015) provide a high-level taxonomy of types of operation data: 1.…”

Section: Usage Data In Cloudmentioning

confidence: 99%

A Usage-based Data Extraction Framework for Cloud-based Application - An Human-Computer Interaction Approach

Kesavulu

Helfert

Bezbradica

2017

Proceedings of the International Conference on Computer-Human Interaction Research and Applications

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Abstract:Features or functionalities provided by cloud-based applications are accessed by users through various interfaces such as web browser, mobile app, and command line interface. Yet for monitoring cloud-based applications, software developers and researchers have focused on web browsers. Software updates are provided for such applications based on the data acquired from the cloud monitoring components but usage data of the cloud application features are difficult to extract in a cloud environment as the usage data is spread across the interfaces on the front-end and the back-end. In this paper, we focus on the usage of the cloud application features from the user perspective and how to extract these data in a cloud environment. We define six criteria for the user-level usage data, analyse the existing usage data extraction techniques and propose a usage data extraction framework adhering to the defined criteria.

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“…Plenty of methods have been proposed to extract user behavior from various data sources [4,5,6,7]. In this work, we have conducted two researches: supervised learning and unsupervised learning.…”

Section: Related Workmentioning

confidence: 99%

Detecting Anomalous User Behavior in Database

Chen¹,

Ai²,

Wei³

et al. 2017

Proceedings of the 7th International Conference on Computer Engineering and Networks — PoS(CENet2017)

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In order to protect vital data in today's internet environment and prevent misuse, especially insider abuse by valid users, we propose a novel two-step detecting approach to distinguish potential misuse behaviour (namely anomalous user behaviour) from normal behaviour. First, we capture the access patterns of users by using association rules. Then, based on the patterns and users' sequential behaviour, we try to deter anomalous user behaviour by leveraging the logistic regression model. Experimental results on real dataset indicate that our method can get a better result and outperform two state-of-the-art method. The proposed two-step detecting approach can effectively detect anomalous user behaviour from the log data generated by operation and maintenance staffs.

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Unsupervised Clickstream Clustering for User Behavior Analysis

Cited by 188 publications

References 33 publications

Behavior Reconstruction Models for Large-scale Network Service Systems

Behavior Reconstruction Models for Large-scale Network Service Systems

A Usage-based Data Extraction Framework for Cloud-based Application - An Human-Computer Interaction Approach

Detecting Anomalous User Behavior in Database

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