• MALSORI
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• no.58
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• pp.101-117
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• 2006

The development of efficient multimodal interfaces and fusion algorithms requires knowledge of usage patterns that show how people use multiple modalities. We analyzed multimodal usage patterns for TV-guide application scenarios (or tasks). In order to collect usage patterns, we implemented a multimodal usage pattern collection system having two input modalities: speech and touch-gesture. Fifty-four subjects participated in our study. Analysis of the collected usage patterns shows a positive correlation between the task type and multimodal usage patterns. In addition, we analyzed the timing between speech-utterances and their corresponding touch-gestures that shows the touch-gesture occurring time interval relative to the duration of speech utterance. We believe that, for developing efficient multimodal fusion algorithms on an application, the multimodal usage pattern analysis for the given application, similar to our work for TV guide application, have to be done in advance.

• Journal of Information Technology Applications and Management
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• v.21 no.4
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• pp.127-139
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• 2014

This study presents a logging method to trace the usage patterns of existing smartphone apps. The actual smartphone app itself, not a specially developed similar app with usage logging, would be used best for the experiment of observing the usage patterns. For this purpose, we used a method of injecting logging codes into existing smartphone app. Using this method, we conducted an experiment to trace usage patterns of a commercial IPTV app, and found that the method is very useful for acquiring detail usage log without influencing participants.

• The KIPS Transactions:PartB
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• v.9B no.4
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• pp.389-390
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• 2002

It is important that acquire information about if customer has some habit in electronic commerce application of internet base that led in recommendation service for customer in dynamic web contents supply. Collaborative filtering that has been used as a standard approach to Web personalization can not get rapidly user's preference change due to static user profiles and has shortcomings such as reliance on user ratings, lack of scalability, and poor performance in the high-dimensional data. In order to overcome this drawbacks, Web usage mining has been prevalent. Web usage mining is a technique that discovers patterns from We usage data logged to server. Specially. a technique that discovers Web usage patterns and clusters patterns is used. However, the discovery of patterns using Afriori algorithm creates many useless patterns. In this paper, the enhanced method for the construction of dynamic user profiles using validated Web usage patterns is proposed. First, to discover patterns Apriori is used and in order to create clusters for user profiles, ARHP algorithm is chosen. Before creating clusters using discovered patterns, validation that removes useless patterns by Dempster-Shafer theory is performed. And user profiles are created dynamically based on current user sessions for Web personalization.

• Journal of the Korean housing association
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• v.10 no.4
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• pp.137-146
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• 1999

The purpose of this study is to analyze the behavioral patterns and furniture usage in An-Bang of apartment. For this purpose, a survey was conducted by 215 housewives who lives in Taegu by the size of floor space, 20-49 Pyong. The data were analyzed by using frequencies, percentage, mean, factor analyzing, χ²-test, cluster analyzing. The results of the study are as follows: 1. The major behavioral patterns in an-bang is sleeping, make-up. fitting, but can't be neglected family-gathered and guest meeting. 2. The patterns of furniture usage are grouped into four clusters and principle furniture arrangement is floor type. 3. Behavioral patterns and furniture usage related with factors.

• Journal of the Korean Society of Fashion and Beauty
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• v.4 no.2 s.8
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• pp.50-64
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• 2006

This study was performed the analysis on the final usage and the pattern type of Jacquard fabrics through the surveyed data from the domestic and foreign textile fashion magazines[Book Moda, Fashion biz, Vogue] during recent 5 years(2000-2004). The result of this study can be summarized as follows. 1 Jacquard fabric mostly applies to apparel followed by accessory, interior decoration and bedding. As classified by uses, in apparel, it applies to One-pieces most followed by jackets and coats. In interior decoration, Slipcover uses Jacquard fabric most and then cushions, curtains and carpets. In accessory, neck ties use it most and then hand bags, hats and mufflers. In bedding, Jacquard fabric evenly applies to sheets, pillows, blankets and coverlet. 2. As classified by uses above, flowered patterns apply to each use most followed by geometric patterns, abstract patterns, ethnic patterns, art patterns and animal patterns. In addition, flowered patterns apply to apparel most and then interior decoration, accessory and bedding. Geometric patterns apply to apparel, accessory, interior decoration and bedding orderly. It is researched that abstract patterns and ethnic patterns also apply to apparel partially.

• Korean Management Science Review
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• v.34 no.3
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• pp.43-60
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• 2017

This study empirically examined the differences in degrees of product or process innovations according to the activation forms of all knowledge management (KM) processes (i.e., socialization, internalization, externalization, and combination), which are influenced by the usage patterns of management control systems (MCS)(i.e., interactive and diagnostic usage patterns). We empirically investigated and identified the links among usage patterns of MCS, the activation forms of KM processes, and the kinds of innovation promoted. Under high competitive conditions, it was found that the interactive usage of MCS is relatively more preferred and enhanced. However, when environmental uncertainty is high, it was shown that the diagnostic use of MCS is more emphasized. Thus, it is evident that the use patterns of MCS are determined by environmental conditions. From the results of this study, it was suggested that under high interactive use of MCS, the activation of socialization and internalization is more enhanced than the facilitation of externalization. It was also observed that when both interactive and diagnostic usage of MCS are high, KM processes are more activated and strengthened. The results indicated that under high activation of KM processes, product innovation as well as process innovation are more frequently occurred. Finally, the results of this study suggested that according to the levels of innovation openness, major innovations are more frequently occurred and promoted than minor innovations.

• 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.

• Korean Journal of Childcare and Education
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• v.17 no.3
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• pp.65-87
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• 2021

Objective: This study aimed to examine the effects of maternal monitoring, shared activities with children, maternal education-oriented behavior, and allowing children to own smart-phones on smart media usage patterns based on smart-phone usage time and purposes among elementary school children. Methods: The participants were 1,315 second-grade elementary school children from the 9th wave of PSKC. Latent profile analysis and the three-step estimation approach were used to examine the determinants of the latent profile and the effects of maternal parenting on the profile. Results: Four latent profiles were identified: 'High-level usage & Entertaining oriented,' 'Moderate-level usage & Social/entertaining oriented,' 'Moderate-level usage & Learning oriented,' and 'Low-level usage.' Additionally, results showed that each profile can be predicted by maternal monitoring, education-oriented behavior, and permitting children to own smart-phones. Conclusion/Implications: Our outcomes suggested that it would be necessary to understand the smart media usage patterns of elementary school children, considering both the amount of time spent with smart media and purposes of uses. Further, it is helpful for mothers to monitor children's daily activities, support their educational activities, and take the role of gatekeeper for smart media as a way of appropriate guidance for their children's use of smart media.

• Communications for Statistical Applications and Methods
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• v.8 no.1
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• pp.271-279
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• 2001

Sequential pattern discovery is one of main interests in web usage mining. the technique of sequential pattern discovery attempts to find inter-session patterns such that the presence of a set of items is followed by another item in a time-ordered set of server sessions. In this paper, a tree-based sequential pattern finding method is proposed in order to discover navigation patterns in server sessions. At each learning process, the suggested method learns about the navigation patterns per server session and summarized into the modified Rymon's tree.

• The Journal of Bigdata
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• v.2 no.2
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• pp.105-114
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• 2017

Today, many services are supported on the web sites. Analysis of usage patterns of web site visitors is very important to optimize the use and efficiency of the web sites. In this study, analysis of usage patterns and comparative analysis of user groups were conducted by analyzing web access log provided by BPI Challenge 2016. This data provides access logs to the web site in the IT system of a Dutch Employee Insurance Agency (UWV). The customer information, and the click data describing the customers' behavior when using the agency's web site. In this study, we use process mining techniques to analyze the usage patterns of customers and the characteristics of customer groups, and ultimately improve the service quality of customers using web services.