• Asia pacific journal of information systems
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• v.17 no.4
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• pp.1-29
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• 2007

The brand-based online community recently plays an important roles for consumers to facilitates searching and sharing information among them. Firms often find such a brand community as a critical channel to gain collective intelligence for developing new ideas and products. As a new web platform such as web 2.0 has been introduced, consumers could more easily participate in the new social networks created by sharing mutual value and belief among themselves. Accordingly firms began to recognize potentials of online brand assets and pay attention to the importance of online brand community loyalty. Previous research related to online community tends to focus on identifying the antecedents of community loyalty and their subsequent impacts on brand. They, however, tend to neglect the importance of individual characteristics of online community users. As integrating the fragmented variables with an individual characteristics, therefore, this study reexamined the impacts of interactivity, information, reward, and personalization services provided by an online brand community on the sense of community, community loyalty, and brand attitude. Also, this study investigated how users' individual characteristics(need for cognition: NFC) can play moderating roles among the variables identified in the previous research. A field survey was administrated and 671 valid samples were collected. In order to test the hypothesis we conducted the multi-sample structural equation modeling(MSEM) between two groups(a group with high vs. a group with low level of NFC). Results show that previously identified variables such as interactivity, information, reward, and personalization services have significant effects on the sense of community as previous research demonstrated. Subsequently, the sense of community positively influences the community loyalty and brand attitude. However, when considering the NFC as a moderating variable, we found that the effect of interactivity and reward service on the sense of community was stronger for a group with a lower level of NFC compared to a group with a higher level, while the effect of information providing service on the sense of community was stronger for a group with a higher level of NFC compared to a group with a lower level. This research revealed that NFC can affect the degree of individual perception on the sense of community which has been considered as an important indicator for the community loyalty and brand attitude. Hence, when firms developing customer relation strategy through building an online brand community, they need to reflect customers' NFC and accordingly provide varying degree of interactivity, information, reward, and personalization services.

• Journal of Internet Computing and Services
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• v.17 no.6
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• pp.53-59
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• 2016

Recently, huge stream data have been generated in real time from various applications such as wireless sensor networks, Internet of Things services, and social network services. For this reason, to develop an efficient method have become one of significant issues in order to discover useful information from such data by processing and analyzing them and employing the information for better decision making. Since stream data are generated continuously and rapidly, there is a need to deal with them through the minimum access. In addition, an appropriate method is required to analyze stream data in resource limited environments where fast processing with low power consumption is necessary. To address this issue, the sliding window model has been proposed and researched. Meanwhile, one of data mining techniques for finding meaningful information from huge data, pattern mining extracts such information in pattern forms. Frequency-based traditional pattern mining can process only binary databases and treats items in the databases with the same importance. As a result, frequent pattern mining has a disadvantage that cannot reflect characteristics of real databases although it has played an essential role in the data mining field. From this aspect, high utility pattern mining has suggested for discovering more meaningful information from non-binary databases with the consideration of the characteristics and relative importance of items. General high utility pattern mining methods for static databases, however, are not suitable for handling stream data. To address this issue, sliding window based high utility pattern mining has been proposed for finding significant information from stream data in resource limited environments by considering their characteristics and processing them efficiently. In this paper, we conduct various experiments with datasets for performance evaluation of sliding window based high utility pattern mining algorithms and analyze experimental results, through which we study their characteristics and direction of improvement.

• Journal of the Economic Geographical Society of Korea
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• v.22 no.3
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• pp.258-273
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• 2019

Bilbao, Spain, made a mark as a example of the regional revitalization by culture and tourism. Korean Government have a perspective that culture and tourism could be an alternative to the regional crisis of manufacturing in 2018. The main purpose of this study is to analyze the locational specificity and the revival strategies for the regional development of Bilbao in a structural context. This could provide implications to the regional crisis of Korea. The main results are summarized as follows. Firstly, the local government of Bilbao has taken an active role, using not only its political and financial autonomy but also its locational advantage as an important nodal region of transnational trade networks in Europe. Secondly, Bilbao was able to sustain its regional revitalization initiatives for a long period by facilitating public-private partnership system. Finally, despite the effectiveness of the mega project and place marketing, low job security and the polarization of the service sector have emerged as a problem at the same time. Still, the deindustrialization of Bilbao could be possible due to the various services including knowledge-based services and financial services as well as culture and tourism.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.93-107
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• 2014

In this paper, we report what we have observed with regard to user-perspective issue clustering based on multi-layered two-mode network analysis. This work is significant in the context of data collection by companies about customer needs. Most companies have failed to uncover such needs for products or services properly in terms of demographic data such as age, income levels, and purchase history. Because of excessive reliance on limited internal data, most recommendation systems do not provide decision makers with appropriate business information for current business circumstances. However, part of the problem is the increasing regulation of personal data gathering and privacy. This makes demographic or transaction data collection more difficult, and is a significant hurdle for traditional recommendation approaches because these systems demand a great deal of personal data or transaction logs. Our motivation for presenting this paper to academia is our strong belief, and evidence, that most customers' requirements for products can be effectively and efficiently analyzed from unstructured textual data such as Internet news text. In order to derive users' requirements from textual data obtained online, the proposed approach in this paper attempts to construct double two-mode networks, such as a user-news network and news-issue network, and to integrate these into one quasi-network as the input for issue clustering. One of the contributions of this research is the development of a methodology utilizing enormous amounts of unstructured textual data for user-oriented issue clustering by leveraging existing text mining and social network analysis. In order to build multi-layered two-mode networks of news logs, we need some tools such as text mining and topic analysis. We used not only SAS Enterprise Miner 12.1, which provides a text miner module and cluster module for textual data analysis, but also NetMiner 4 for network visualization and analysis. Our approach for user-perspective issue clustering is composed of six main phases: crawling, topic analysis, access pattern analysis, network merging, network conversion, and clustering. In the first phase, we collect visit logs for news sites by crawler. After gathering unstructured news article data, the topic analysis phase extracts issues from each news article in order to build an article-news network. For simplicity, 100 topics are extracted from 13,652 articles. In the third phase, a user-article network is constructed with access patterns derived from web transaction logs. The double two-mode networks are then merged into a quasi-network of user-issue. Finally, in the user-oriented issue-clustering phase, we classify issues through structural equivalence, and compare these with the clustering results from statistical tools and network analysis. An experiment with a large dataset was performed to build a multi-layer two-mode network. After that, we compared the results of issue clustering from SAS with that of network analysis. The experimental dataset was from a web site ranking site, and the biggest portal site in Korea. The sample dataset contains 150 million transaction logs and 13,652 news articles of 5,000 panels over one year. User-article and article-issue networks are constructed and merged into a user-issue quasi-network using Netminer. Our issue-clustering results applied the Partitioning Around Medoids (PAM) algorithm and Multidimensional Scaling (MDS), and are consistent with the results from SAS clustering. In spite of extensive efforts to provide user information with recommendation systems, most projects are successful only when companies have sufficient data about users and transactions. Our proposed methodology, user-perspective issue clustering, can provide practical support to decision-making in companies because it enhances user-related data from unstructured textual data. To overcome the problem of insufficient data from traditional approaches, our methodology infers customers' real interests by utilizing web transaction logs. In addition, we suggest topic analysis and issue clustering as a practical means of issue identification.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.305-316
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• 2011

Recommender system is the system which, by using automated information filtering technology, recommends products or services to the customers who are likely to be interested in. Those systems are widely used in many different Web retailers such as Amazon.com, Netfix.com, and CDNow.com. Various recommender systems have been developed. Among them, Collaborative Filtering (CF) has been known as the most successful and commonly used approach. CF identifies customers whose tastes are similar to those of a given customer, and recommends items those customers have liked in the past. Numerous CF algorithms have been developed to increase the performance of recommender systems. However, the relative performances of CF algorithms are known to be domain and data dependent. It is very time-consuming and expensive to implement and launce a CF recommender system, and also the system unsuited for the given domain provides customers with poor quality recommendations that make them easily annoyed. Therefore, predicting in advance whether the performance of CF recommender system is acceptable or not is practically important and needed. In this study, we propose a decision making guideline which helps decide whether CF is adoptable for a given application with certain transaction data characteristics. Several previous studies reported that sparsity, gray sheep, cold-start, coverage, and serendipity could affect the performance of CF, but the theoretical and empirical justification of such factors is lacking. Recently there are many studies paying attention to Social Network Analysis (SNA) as a method to analyze social relationships among people. SNA is a method to measure and visualize the linkage structure and status focusing on interaction among objects within communication group. CF analyzes the similarity among previous ratings or purchases of each customer, finds the relationships among the customers who have similarities, and then uses the relationships for recommendations. Thus CF can be modeled as a social network in which customers are nodes and purchase relationships between customers are links. Under the assumption that SNA could facilitate an exploration of the topological properties of the network structure that are implicit in transaction data for CF recommendations, we focus on density, clustering coefficient, and centralization which are ones of the most commonly used measures to capture topological properties of the social network structure. While network density, expressed as a proportion of the maximum possible number of links, captures the density of the whole network, the clustering coefficient captures the degree to which the overall network contains localized pockets of dense connectivity. Centralization reflects the extent to which connections are concentrated in a small number of nodes rather than distributed equally among all nodes. We explore how these SNA measures affect the performance of CF performance and how they interact to each other. Our experiments used sales transaction data from H department store, one of the well?known department stores in Korea. Total 396 data set were sampled to construct various types of social networks. The dependant variable measuring process consists of three steps; analysis of customer similarities, construction of a social network, and analysis of social network patterns. We used UCINET 6.0 for SNA. The experiments conducted the 3-way ANOVA which employs three SNA measures as dependant variables, and the recommendation accuracy measured by F1-measure as an independent variable. The experiments report that 1) each of three SNA measures affects the recommendation accuracy, 2) the density's effect to the performance overrides those of clustering coefficient and centralization (i.e., CF adoption is not a good decision if the density is low), and 3) however though the density is low, the performance of CF is comparatively good when the clustering coefficient is low. We expect that these experiment results help firms decide whether CF recommender system is adoptable for their business domain with certain transaction data characteristics.

• Journal of Family Resource Management and Policy Review
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• v.25 no.3
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• pp.87-102
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• 2021

Child abuse and neglect are recently increasing in Korea, and although the government has actively improved the child protection system, the number of abused children and the rate of cases judged as abuse have continuously risen. Given that 75% of child abusers are parents, child abuse and neglect are expected to recur. To prevent such a recurrence, various intervention programs for abused children and their parents are required. The purpose of this study were to design a recovery support service process and investigate the effectiveness of pilot program for families of origin, including neglected(protected) children, to improve the system by which these programs are operated, and formulate policy alternatives that reinforce "family preservation" principles. The pilot program was implemented from June to November 2020 in 4-local healthy family support center. The number of program participants and the frequency of participation in each other differed, because of the difference in number of confirmed coronavirus cases in each region and the requirement for social distancing. Through the program, a community-based service process was developed for neglected(protected) children and their parents, and cooperative networks between related facilities and institutions were established. The study formulated the following recommendations: First, a cooperation system among government departments mandated to provide different services to neglected(protected) children is needed. Second, wider and various channels through which abused children can avail of protective services should be developed within communities. Third, more stable environments for program operation should be cultivated, and cooperative partnerships should be sought for knowledge sharing among relevant government departments. Another necessary measure is for a center to develop its own business model, in which the duplication of services provided by involved organizations is avoided. Finally, clear guidelines, administrative standards, and specific plans for program operation should be arranged. Also regional characteristics are maintained, but services should be standardized.

• Journal of Internet Computing and Services
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• v.17 no.1
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• pp.47-53
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• 2016

A type of workflow affiliation network is one of the specialized social network types, which represents the associative relation between actors and activities. There are many methods on a workflow affiliation network measuring centralities such as degree centrality, closeness centrality, betweenness centrality, eigenvector centrality. In particular, we are interested in the closeness centrality measurements on a workflow affiliation network discovered from enterprise workflow models, and we know that the time complexity problem is raised according to increasing the size of the workflow affiliation network. This paper proposes an estimated ranking algorithm and analyzes the accuracy and average computation time of the proposed algorithm. As a result, we show that the accuracy improves 47.5%, 29.44% in the sizes of network and the rates of samples, respectively. Also the estimated ranking algorithm's average computation time improves more than 82.40%, comparison with the original algorithm, when the network size is 2400, sampling rate is 30%.

• Journal of Multimedia Information System
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• v.4 no.4
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• pp.163-170
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• 2017

Internet of things (IoT) is a new paradigm for collecting, processing and analyzing various contents in order to detect anomalies and to monitor particular patterns in a specific environment. The collected data can be used to discover new patterns and to offer new insights. IoT-enabled data mashup is a new technology to combine various types of information from multiple sources into a single web service. Mashup services create a new horizon for different applications. Environmental monitoring is a serious tool for the state and private organizations, which are located in regions with environmental hazards and seek to gain insights to detect hazards and locate them clearly. These organizations may utilize IoT - enabled data mashup service to merge different types of datasets from different IoT sensor networks in order to leverage their data analytics performance and the accuracy of the predictions. This paper presents an IoT - enabled data mashup service, where the multimedia data is collected from the various IoT platforms, then fed into an environmental cognition service which executes different image processing techniques such as noise removal, segmentation, and feature extraction, in order to detect interesting patterns in hazardous areas. The noise present in the captured images is eliminated with the help of a noise removal and background subtraction processes. Markov based approach was utilized to segment the possible regions of interest. The viable features within each region were extracted using a multiresolution wavelet transform, then fed into a discriminative classifier to extract various patterns. Experimental results have shown an accurate detection performance and adequate processing time for the proposed approach. We also provide a data mashup scenario for an IoT-enabled environmental hazard detection service and experimentation results.

• Journal of Internet Computing and Services
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• v.9 no.1
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• pp.159-170
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• 2008

The ubiquitous network revolution is beginning with the onset of digital convergence, whereby computers, horne appliances, and communications and broadcast media are being unified into digital media with the founding of the information super high speed. This technical advancement is creating a new culture and a new space and accelerating society's transition to the new and unique social paradigm of a 'ubiquitous society'. In particular, studies on ubiquitous communications are well underway. Lately, the focus has been on the Bluetooth technology due to its applicability in various environments. Applying Bluetooth connectivity to new environments such as ubiquitous or sensor networks requires finding new ways of using it. Thus, the scalable Bluetooth piconet scheme with independent slave device is proposed. It follows from work by Sea et al. But extended scatternet is not considered is Kiisc05 paper. Therefore, we propose secure bridge connection scheme for scalable Bluetooth scatternet.

• The Journal of the Korea Contents Association
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• v.13 no.7
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• pp.1-8
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• 2013

As yet the use of 3D projection mapping facade has been limited to advertising and performance in the outside. And interactive elements are lacking. In this paper, propose a interaction system which control of projection mapping facade for multi-audience participation using smart devices. The system can be interaction for the multi-façade. And single or multiple can participate in the game. A user test based on the result confirmed an effectiveness of the proposed method. This research showed a practical method in which interaction of projection facade system can be used to user devices. The results of this study can be used as a base module for projection facade interaction system. In addition, It can be utilized for converged content development such as performances, games, education and various applications services.