• Journal of Information Technology Applications and Management
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• v.21 no.4
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• pp.51-64
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• 2014

The purpose of operating the main homepage of University is to provide the related information about University resources to site visitors. In this study, we analyze website browsing patterns and extract characteristics of users in order to improve its utilization. The access log files to main homepage were used to analyze the browsing patterns and converted to process log files adaptable to a process mining tool, ProM. Finally we provide useful information about user friendly homepage design and suggest plans for improving its utilization to website operators.

• Journal of the Korean Society for information Management
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• v.27 no.4
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• pp.109-130
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• 2010

The purpose of this study is to identify th patterns in the news reporters' information seeking behaviors by observing their web activities. For this purpose, transaction logs collected from 23 news reporters were analyzed. Web tracking software was installed to collect the data from their PCs, and a total of 39,860 web logs were collected in two weeks. Start and end pattern of sessions, transitional pattern by step, sequence rule model was analyzed and the pattern of Internet use was compared with the general public. the analysis of pattern derived a web information seeking behavior modes that consists of four types of behaviors: fact-checking browsing, fact-checking search, investigative browsing and investigative search.

• Journal of Information Science Theory and Practice
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• v.8 no.1
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• pp.45-55
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• 2020

The purpose of this work was to study library website users' actions by tracking their behavior, determining popular content, and identifying browsing patterns and subsequent improvement of access to popular content. The study of behavior models and the use of web analytics has led to the emergence of solutions that improve the usability and functionality of the State Public Scientific-Technological Library of the Siberian Branch of the Russian Academy of Sciences (SPSTL SB RAS) website. These are: identifying user tasks as they are developed, conducting user testing to better understand the event. tracking data and collecting additional data to verify the effectiveness of the changes made. Examining data on the duration of the session and the number of visits will help determine the goals of user visits and develop new recommendations. Usability analysis and testing will make it possible to compare the data obtained using web analytics and the perception of the library site by the users themselves. Recommendations are offered to libraries on the use of data on the real behavior of the target audience of the library website to improve access to library resources and services, increase their relevance and improve information services.

• Journal of Computing Science and Engineering
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• v.5 no.4
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• pp.338-345
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• 2011

Nowadays mobile devices are used for various applications such as making voice/video calls, browsing the Internet, listening to music etc. The average battery consumption of each of these activities and the length of time a user spends on each one determines the battery lifetime of a mobile device. Previous methods have provided predictions of battery lifetime using a static battery consumption rate that does not consider user characteristics. This paper proposes an approach to predict a mobile device's available battery lifetime based on usage patterns. Because every user has a different pattern of voice calls, data communication, and video call usage, we can use such usage patterns for personalized prediction of battery lifetime. Firstly, we define one or more states that affect battery consumption. Then, we record time-series log data related to battery consumption and the use time of each state. We calculate the average battery consumption rate for each state and determine the usage pattern based on the time-series data. Finally, we predict the available battery time based on the average battery consumption rate for each state and the usage pattern. We also present the experimental trials used to validate our approach in the real world.

• Journal of Korea Society of Industrial Information Systems
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• v.14 no.3
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• pp.98-105
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• 2009

It is important for firms to help customers find the products or information they need in order to increase sales and promote return visits to their websites. Hence, the presentation of product information is very important in e-commerce websites. In this research, we study how disposition styles can influence browsing patterns. The test results show that people are inclined to use feature information paths in the vertical disposition style and product information paths in the horizontal disposition style. With vertical disposition, users are more likely to follow feature information paths which may help them focus on comparisons across products.

• Journal of Information Processing Systems
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• v.12 no.3
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• pp.436-454
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• 2016

This research presents the battery discharge rate models for the energy consumption of mobile phone batteries based on machine learning by taking into account three usage patterns of the phone: the standby state, video playing, and web browsing. We present the experimental design methodology for collecting data, preprocessing, model construction, and parameter selections. The data is collected based on the HTC One X hardware platform. We considered various setting factors, such as Bluetooth, brightness, 3G, GPS, Wi-Fi, and Sync. The battery levels for each possible state vector were measured, and then we constructed the battery prediction model using different regression functions based on the collected data. The accuracy of the constructed models using the multi-layer perceptron (MLP) and the support vector machine (SVM) were compared using varying kernel functions. Various parameters for MLP and SVM were considered. The measurement of prediction efficiency was done by the mean absolute error (MAE) and the root mean squared error (RMSE). The experiments showed that the MLP with linear regression performs well overall, while the SVM with the polynomial kernel function based on the linear regression gives a low MAE and RMSE. As a result, we were able to demonstrate how to apply the derived model to predict the remaining battery charge.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06c
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• pp.283-285
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• 2012

2009년 7.7 DDoS 공격을 기점으로 DDOS 공격에 HTTP-GET 프로토콜이 공격에 주로 사용되고 있다. 본 논문에서는 클라이언트에서 개인사용자의 웹 브라우징 패턴을 분석함으로써 HTTP-GET 공격을 탐지하는 방법을 제안한다. 웹 브라우징 패턴 분석에는 Markov Model을 사용하여 사용자의 정상적인 행동패턴을 계산하고, 공격을 탐지하는데 사용한다. 제안한 방법은 클라이언트에서 개인사용자에 대한 개별적인 웹 브라우징 패턴을 분석하기 때문에 서버에서보다 계산량이 적으며, 클라이언트 레벨에서 DDoS 공격을 조기에 탐지/차단함으로써 서버에서의 DDoS 공격 탐지에 의한 부하를 줄일 수 있다.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.11a
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• pp.30-32
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• 2011

현재 컴퓨터 시스템에서 대표적인 성능 저하가 발생되는 부분은 블록 I/O 시스템이다. 안드로이드와 같은 모바일 장치 또한 위와 같은 성능 이슈를 가지고 있다. 본 논문에서는 리눅스 블록 레이어의 I/O를 tracing 해주는 blktrace를 안드로이드 장치에 적용하여 SQLite를 사용하는 Web Browsing 시의 I/O 패턴 분석과 성능 개선 방안을 제시 한다.

• Journal of the Korean Society for information Management
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• v.33 no.1
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• pp.225-245
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• 2016

The purpose of this study is to enhance the efficiency of library space usage through analyzing users' behavior in an academic library. Diary methodology was applied for collecting users' activities in the library. 108 students participated in this study, but 84 students submitted their diaries. Among them, 14 participants were interviewed to identify barriers and their needs in using the library. The findings suggest rearranging IT devices to increase use rate of those devices, making plans for reusing the space where it is rarely used, and improving the accessibility of information through browsing in the bookstack area. Additionally, time analysis of users' activities was used to analyze students' behavior in the library.

• Proceedings of the Korea Information Processing Society Conference
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• 2000.10a
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• pp.305-308
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• 2000

웹사이트 설계자의 주관적 판단에 의한 정적 하이퍼텍스트 링킹은 모든 사용자들에게 동일한 링크를 제공한다는 단점을 가지고 있다. 이러한 문제점을 개선하고, 각 사용자들의 브라우징 패턴에 적합한 웹 문서들을 동적 링크로 제공해주기 위한 여러 동적 링킹 시스템들이 제안되었다. 그러나 대부분의 동적 링킹 시스템들은 사용자의 현재 브라우징 패턴과 가장 유사한 패턴 정보만을 이용해 동적 링크를 제공하기 때문에 연관성이 없는 웹 문서들에 대한 링크를 수시로 제공한다는 또 다른 문제를 지니고 있다. 본 논문에서는 데이터 마이닝의 한 응용 분야인 웹 마이닝 기법을 이용하여 웹 서버의 로그파일로부터 사용자들의 브라우징 패턴을 분석해내고, 다차원 데이터 집합에 적합한 Association Rule Hypergraph Partitioning(ARHP) 알고리즘을 이용하여 서로 연관성이 있는 웹 문서들을 분류한다. 사용자 브라우징 패턴 정보로부터 사용자에게 추천해줄 1차 링크 집합을 생성하고, 연관 웹 문서 정보를 이용하여 2차 링크 집합을 생성한다. 그리고 두 링크 집합에 공통으로 포함된 링크 집합만을 사용자에게 동적으로 추천해줌으로써 사용자가 보다 편리하고 정확하게 웹사이트를 브라우징 할 수 있도록 하는 동적 링킹 시스템을 제안한다.