Using Bi-clustering Algorithm for Analyzing Online Users Activity in a Virtual Campus

Xhafa, Fatos; Caballé, Santi; Barolli, Leonard; Molina, A.; Miho, Rozeta

doi:10.1109/incos.2010.15

Cited by 10 publications

(6 citation statements)

References 21 publications

(18 reference statements)

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“…In our future work, we would like to compare the results of the data mining methods employed in this work with that of a bi-clustering algorithm initially studied in [23]. This approach is also useful to extract relevant knowledge about user activity for other purposes beyond navigation patterns, such as identifying activities performed by students as well as to study time parameters related to such activities.…”

Section: Discussionmentioning

confidence: 99%

“…not for real learning needs). Yet, the lack of sufficient computational resources is the main obstacle to process large amounts of data in real time and hence in real situations this processing tends to be done off-line in order to avoid harming the performance of the logging application, but as it takes place after the completion of the learning activity has less impact on it [6,23].…”

Section: Related Workmentioning

confidence: 99%

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Distributed-based massive processing of activity logs for efficient user modeling in a Virtual Campus

Caballé

Xhafa

2013

Cluster Comput

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This paper reports on a multi-fold approach for the building of user models based on the identification of navigation patterns in a virtual campus, allowing for adapting the campus' usability to the actual learners' needs, thus resulting in a great stimulation of the learning experience. However, user modeling in this context implies a constant processing and analysis of user interaction data during longterm learning activities, which produces huge amounts of valuable data stored typically in server log files. Due to the large or very large size of log files generated daily, the massive processing is a foremost step in extracting useful information. To this end, this work studies, first, the viability of processing large log data files of a real Virtual Campus using different distributed infrastructures. More precisely, we study the time performance of massive processing of daily log files implemented following the master-slave paradigm and evaluated using Cluster Computing and PlanetLab platforms. The study reveals the complexity and challenges of massive processing in the big data era, such as the need to carefully tune the log file processing in terms of chunk log data size to be processed at slave nodes as well as the bottleneck in processing in truly geographically distributed infrastructures due to the overhead caused by the communication time among the master and slave nodes. Then, an application of the massive processing approach resulting in log data processed and stored in a well-structured format is

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Distributed-based massive processing of activity logs for efficient user modeling in a Virtual Campus

Caballé

Xhafa

2013

Cluster Comput

Self Cite

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“…Additionally, |S k | denotes the cardinality of S k , i.e., the number of samples in bi-cluster Given a data matrix A, the bi-clustering problem is to design algorithms to find bi-clusters B {B k : k 1, 2, /, K} of it, i.e., a sub-set of matrices of A such that samples (rows S k ) of each bicluster B k exhibit some similar behavior under the corresponding features (columns, F k ). In other words, the bi-clustering problem is to identify a set of bi-clusters B k =(S k , F k ) such that each bicluster B k satisfies some specific characteristics of homogeneity (Xhafa et al, 2011).…”

Section: Bi-clustering: Definitions and Problem Formulationmentioning

confidence: 99%

An enhanced adaptive Bi-clustering algorithm through building a shielding complex sub-matrix

Tang

Yin

et al. 2022

Front. Genet.

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Bi-clustering refers to the task of finding sub-matrices (indexed by a group of columns and a group of rows) within a matrix of data such that the elements of each sub-matrix (data and features) are related in a particular way, for instance, that they are similar with respect to some metric. In this paper, after analyzing the well-known Cheng and Church bi-clustering algorithm which has been proved to be an effective tool for mining co-expressed genes. However, Cheng and Church bi-clustering algorithm and summarizing its limitations (such as interference of random numbers in the greedy strategy; ignoring overlapping bi-clusters), we propose a novel enhancement of the adaptive bi-clustering algorithm, where a shielding complex sub-matrix is constructed to shield the bi-clusters that have been obtained and to discover the overlapping bi-clusters. In the shielding complex sub-matrix, the imaginary and the real parts are used to shield and extend the new bi-clusters, respectively, and to form a series of optimal bi-clusters. To assure that the obtained bi-clusters have no effect on the bi-clusters already produced, a unit impulse signal is introduced to adaptively detect and shield the constructed bi-clusters. Meanwhile, to effectively shield the null data (zero-size data), another unit impulse signal is set for adaptive detecting and shielding. In addition, we add a shielding factor to adjust the mean squared residue score of the rows (or columns), which contains the shielded data of the sub-matrix, to decide whether to retain them or not. We offer a thorough analysis of the developed scheme. The experimental results are in agreement with the theoretical analysis. The results obtained on a publicly available real microarray dataset show the enhancement of the bi-clusters performance thanks to the proposed method.

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“…Adopting other datamining techniques, the authors of [28] proposed the use of a biclustering algorithm to extract information from the daily online activities of virtual campus users. The results showed that the knowledge extracted from log files with statistical measures helped to provide better usability and adaption to user preferences.…”

Section: Evaluation Factors and Artificial Intelligencementioning

confidence: 99%

User Experience Evaluation Using Mouse Tracking and Artificial Intelligence

et al. 2019

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Business platform models frequently require continuous adaptation and agility to allow new experiences to be created and delivered to customers. To understand user behavior in online systems, researchers have taken advantage of a combination of traditional and recently developed analysis techniques. Earlier studies have shown that user behavior monitoring data, as obtained by mouse tracking, can be utilized to improve user experience (UX). Many mouse-tracking solutions exist; however, the vast majority is proprietary, and open-source packages do not provide the resources and data needed to support UX research. Thus, this paper presents: 1) the development of an interaction monitoring application titled Artificial Intelligence and Mouse Tracking-based User eXperience Tool (AIMT-UXT); 2) the validation of the tool in a case study conducted on the Website of the Brazilian Federal Revenue Service (BFR); 3) the definition of a new relationship pattern of variables that determine user behavior; 4) the construction of a fuzzy inference system for measuring user performance using the defined variables and the data captured in the case study; and 5) the application of a clustering algorithm to complement the analysis. A comparison of the results of the applied quantitative methodologies indicates that the developed framework was able to infer UX scores similar to those reported by users in questionnaires.

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Using Bi-clustering Algorithm for Analyzing Online Users Activity in a Virtual Campus

Cited by 10 publications

References 21 publications

Distributed-based massive processing of activity logs for efficient user modeling in a Virtual Campus

Distributed-based massive processing of activity logs for efficient user modeling in a Virtual Campus

An enhanced adaptive Bi-clustering algorithm through building a shielding complex sub-matrix

User Experience Evaluation Using Mouse Tracking and Artificial Intelligence

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