• Korean journal of communication and information
• /
• v.46
• /
• pp.485-510
• /
• 2009

Since the inception of Internet Protocol TV's real-time service on January, 2009, technological challenges and lack of content diversity of IPTV have been cited as problem areas. While past researches focused on IPTV's technological, industrial and policy facets, this research surveyed the viewers' viewing and usage patterns, and the level of service satisfaction among actual audiences of MegaTV, SKBroadband, and LGTV since January. The results show that subscription fee and content types were the two dominant determinants in audience's choice of a particular IPTV company. The high users of IPTV were on average aged between 30-39, and women tended to be heavier users than men. The surveyed audience also regarded IPTV to be more similar to the Internet than any other medium such as Cable TV and Satellite TV. IPTV audience cited 'lifestyle service' and 'additional service' offerings to be highly relevant to their usage and satisfaction; interactive service and educational service also showed high correlation to usage and satisfaction. The most watched contents on IPTV were domestic TV series, followed by foreign movies and variety shows. 'Fees for additional features,' 'lack of real-time terrestrial TV service', and 'lack of content variety' were the major areas of concern for IPTV viewers. They answered that the monthly fee was the most important factor in the selection of IPTV. Use of pay-per-view contents and add-on interactive service fees were also seen as problematic. With regards to future usage, the IPTV audience revealed that they were very sensitive and reluctant to pay for additional services. While existing researches concentrated on IPTV's technological problems and lack of content diversity, this study illuminated the more pragmatical side of the viewers, namely, the importance of price in audience's selection of an IPTV service provider. From these results, it is recommended that, before doing anything else, the service providers try to meet the audience's expected price points in order to garner the full potential of IPTV and the attendant mass audience.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.16 no.2
• /
• pp.164-171
• /
• 2006

Recently Digital multimedia broadcasting (DMB) has been available as a commercial service. The users sometimes have difficulty in finding their preferred multimedia contents and need to spend a lot of searching time finding them. They are even very likely to miss their preferred contents while searching for them. In order to solve the problem, we need a method for recommendation users preferred only minimum information. We propose an algorithm and a system for recommending users' preferred contents using preference transition probability from user's usage history. The system includes four agents: a client manager agent, a monitoring agent, a learning agent, and a recommendation agent. The client manager agent interacts and coordinates with the other modules, the monitoring agent gathers usage data for analyzing the user's preference of the contents, the learning agent cleans the gathered usage data and modeling with state transition matrix over time, and the recommendation agent recommends the user's preferred contents by analyzing the cleaned usage data. In the recommendation agent, we developed the recommendation algorithm using a user's preference transition probability for the contents. The prototype of the proposed system is designed and implemented on the WIPI(Wireless Internet Platform for Interoperability). The experimental results show that the recommendation algorithm using a user's preference transition probability can provide better performances than a conventional method.

• Informatization Policy
• /
• v.25 no.4
• /
• pp.65-83
• /
• 2018

This study examined the effects of smartphone usage types and excessive expectation for smartphone on adolescents' smartphone overdependence. It utilized the data of 3,454 adolescents (1,758 males and 1,696 females), aged between 13 and 19 from a smartphone overdependence survey of the National Information Society Agency. As a result of the hierarchical multiple regression analysis, age and household income had a negative effect on smartphone overdependence. Weekly smartphone usage time positively affected the serious consequences (sub-factor of smartphone overdependence). Among the different types of smartphone usage, information seeking, SNS, gaming, and the excessive expectation for smartphones had positive impact on smartphone overdependence. Smartphone usage for leisure seeking did not affect smartphone overdependence, but it negatively influenced serious consequences. Messenger use did not affect smartphone overdependence, but the use had a positive effect on salience, and a negative effect on serious consequences. The results of this study suggest that it is necessary to identify the types of smartphone use as well as the usage time to understand the smartphone dependence of adolescents. In addition, it seems necessary to recognize and correct the irrational excessive expectation for smartphone in order to prevent dependence and implement effective interventions.

• Communications for Statistical Applications and Methods
• /
• v.7 no.2
• /
• pp.605-616
• /
• 2000

Three primary interests frequently raised by mortgage companies are introduced and the corresponding statistical approaches for the default probability in mortgage companies are examined. Statistical models considered in this paper are time series, logistic regression, decision tree, neural network, and discrete time models. Usage of the models is illustrated using an artificially modified data set and the corresponding models are evaluated in appropriate manners.

• Journal of Digital Convergence
• /
• v.15 no.11
• /
• pp.433-440
• /
• 2017

The present study explored the impact of personality and self-identity on online information acquisition, information transmission and the usage of SNSs. Participants were 180 students, SNS users, from a university in Korea. Among the participants, 171 students completed all surveys. As a results, personality traits such as openness, extraversion, agreeableness, neuroticism were positively related to the SNS usage and information behaviors. Social identity orientation was associated with time spent of SNS and the number of friends whereas collective identity orientation was related to information acquisition. As a result of multiple regression, time spent, openness, agreeableness, collective identity were significant predictors of information acquisition. The number of SNS in use, time spent of SNS, openness, neuroticism were predictors of information transmission. However, as the predictor of information transmission, neuroticism showed inverse relationship with information transmission.

• Environmental and Resource Economics Review
• /
• v.25 no.2
• /
• pp.141-178
• /
• 2016

This paper, using AMR (Automatic Meter Reading) electricity data accurately measured in real time, analyses the characteristics and patterns of temperature effect on the industrial electricity usage. For this goal, the paper constructs and estimates a model which captures the properties of AMR time series including long-term trends, mid-term temperature effects, and short-term special day effects. Based on the estimated temperature response function and the temperature effect, we categorize the whole industry into two groups: one group with sharp temperature effect and the other with weak temperature effect. Furthermore, the industry group with sharp temperature effect is classified into a summer peak industry group and a winter peak industry group, based on the estimates of the temperature response function. These empirical results carry practical policy implications on the real time electricity demand management.

• Journal of Digital Contents Society
• /
• v.18 no.4
• /
• pp.747-751
• /
• 2017

The log has variable informations that are important and necessary to manage a network when accessed to network servers. These informations are used to reduce a cost and efficient manage a network through the meaningful prediction information extraction from the amount of user access. And, the network manager can instantly monitor the status of CPU, memory, disk usage ratio on network using the SNMP. In this paper, firstly, we have accumulated and analysed the 6 network logs and extracted the informations that used to predict the amount of user access. And then, we experimented the prediction simulation with the time series analysis such as moving average method and exponential smoothing. Secondly, we have simulated the usage ration of CPU, memory, and disk using Xian SNMP simulator and extracted the OID for the time series prediction of CPU, memory, and disk usage ration. And then, we presented the visual result of the variable experiments through the Excel and R programming language.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2016.05a
• /
• pp.531-532
• /
• 2016

Carpal tunnel syndrome, carpal tunnel becomes part of a narrower median nerve pain occurs pressed. How a stable wrist stretch for the prevention of carpal tunnel syndrome is required, it should life guides to fit the user is provided. In this research, users themselves recognize the risk of carpal tunnel syndrome, to be able to stretch, it implements the application. Application, based on the usage time set by the user, executes the alarm and lock. At this time, the user may utilize arduino and inertia sensor, it is possible to wrist stretch, to analyze usage patterns, it is possible to provide a medical information and life guides to suit the user. Data using Bluetooth 4.0 communication has been to meet the runaway. In order to evaluate the mounted system, targeting four subjects, the time and the touch frequency of usage of the 3-day smartphone measured, as a result of the questionnaire, it reduces the frequency of use of smartphones, a positive it was possible to prove the effect.

• The Journal of the Korea Contents Association
• /
• v.21 no.10
• /
• pp.217-226
• /
• 2021

Based on the theory of use and satisfaction, this study investigated the strength of TikTok usage motives (entertainment, information-seeking, interaction, self-expression, and escape) and the effect of each motive on users' adherence (frequency of use, time of use). investigated. After conducting a survey on TikTok Chinese users and removing insincere answers, 101 survey results were analyzed. The analysis results are as follows: First, the intensity of motivation for using TikTok was in the following order: entertainment-seeking, information-seeking, interaction, self-expression, and escape. Among them, the motives of pursuit of entertainment and escape were higher in females than in males. And the motives of pursuit of entertainment and escape were higher in the case of women than in the case of men. Second, as a result of analyzing the effect on user adhesion, the influence of the users' entertainment pursuit motive, interaction motive, and escape motive was significant for the frequency of use, and the influence of interaction was high. Third, the influence of entertainment-seeking motives and escape motives was found to be significant for use time. This means that users often use TikTok for entertainment, interaction, and escapism, and longer TikTok usage time for entertainment or escapism.

• Journal of Software Assessment and Valuation
• /
• v.16 no.1
• /
• pp.81-88
• /
• 2020

Various methods of differentiating and servicing digital content for individual users have been studied. Searching for association rules is a very useful way to discover individual preferences in digital content services. The Apriori algorithm is useful as an association rule extractor using frequent itemsets. However, the Apriori algorithm is not suitable for application to an actual content service because it considers only the reference count of each content. In this paper, we propose a new algorithm based on the Apriori that searches association rules by using purchase history and usage-time for each item. The proposed algorithm utilizes the usage time with the weight value according to purchase items. Thus, it is possible to extract the exact preference of the actual user. We implement the proposed algorithm and verify the performance through the actual data presented in the actual content service system.