• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.117-127
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• 2012

Due to the recent expansion of the Web 2.0 -based services, along with the widespread of smartphones, online social network services are being popularized among users. Online social network services are the online community services which enable users to communicate each other, share information and expand human relationships. In the social network services, each relation between users is represented by a graph consisting of nodes and links. As the users of online social network services are increasing rapidly, the SNS are actively utilized in enterprise marketing, analysis of social phenomenon and so on. Social Network Analysis (SNA) is the systematic way to analyze social relationships among the members of the social network using the network theory. In general social network theory consists of nodes and arcs, and it is often depicted in a social network diagram. In a social network diagram, nodes represent individual actors within the network and arcs represent relationships between the nodes. With SNA, we can measure relationships among the people such as degree of intimacy, intensity of connection and classification of the groups. Ever since Social Networking Services (SNS) have drawn increasing attention from millions of users, numerous researches have made to analyze their user relationships and messages. There are typical representative SNA methods: degree centrality, betweenness centrality and closeness centrality. In the degree of centrality analysis, the shortest path between nodes is not considered. However, it is used as a crucial factor in betweenness centrality, closeness centrality and other SNA methods. In previous researches in SNA, the computation time was not too expensive since the size of social network was small. Unfortunately, most SNA methods require significant time to process relevant data, and it makes difficult to apply the ever increasing SNS data in social network studies. For instance, if the number of nodes in online social network is n, the maximum number of link in social network is n(n-1)/2. It means that it is too expensive to analyze the social network, for example, if the number of nodes is 10,000 the number of links is 49,995,000. Therefore, we propose a heuristic-based method for finding the shortest path among users in the SNS user graph. Through the shortest path finding method, we will show how efficient our proposed approach may be by conducting betweenness centrality analysis and closeness centrality analysis, both of which are widely used in social network studies. Moreover, we devised an enhanced method with addition of best-first-search method and preprocessing step for the reduction of computation time and rapid search of the shortest paths in a huge size of online social network. Best-first-search method finds the shortest path heuristically, which generalizes human experiences. As large number of links is shared by only a few nodes in online social networks, most nods have relatively few connections. As a result, a node with multiple connections functions as a hub node. When searching for a particular node, looking for users with numerous links instead of searching all users indiscriminately has a better chance of finding the desired node more quickly. In this paper, we employ the degree of user node vn as heuristic evaluation function in a graph G = (N, E), where N is a set of vertices, and E is a set of links between two different nodes. As the heuristic evaluation function is used, the worst case could happen when the target node is situated in the bottom of skewed tree. In order to remove such a target node, the preprocessing step is conducted. Next, we find the shortest path between two nodes in social network efficiently and then analyze the social network. For the verification of the proposed method, we crawled 160,000 people from online and then constructed social network. Then we compared with previous methods, which are best-first-search and breath-first-search, in time for searching and analyzing. The suggested method takes 240 seconds to search nodes where breath-first-search based method takes 1,781 seconds (7.4 times faster). Moreover, for social network analysis, the suggested method is 6.8 times and 1.8 times faster than betweenness centrality analysis and closeness centrality analysis, respectively. The proposed method in this paper shows the possibility to analyze a large size of social network with the better performance in time. As a result, our method would improve the efficiency of social network analysis, making it particularly useful in studying social trends or phenomena.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.95-112
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• 2021

In university education, the choice of major class plays an important role in students' careers. However, in line with the changes in the industry, the fields of major subjects by department are diversifying and increasing in number in university education. As a result, students have difficulty to choose and take classes according to their career paths. In general, students choose classes based on experiences such as choices of peers or advice from seniors. This has the advantage of being able to take into account the general situation, but it does not reflect individual tendencies and considerations of existing courses, and has a problem that leads to information inequality that is shared only among specific students. In addition, as non-face-to-face classes have recently been conducted and exchanges between students have decreased, even experience-based decisions have not been made as well. Therefore, this study proposes a recommendation system model that can recommend college major classes suitable for individual characteristics based on data rather than experience. The recommendation system recommends information and content (music, movies, books, images, etc.) that a specific user may be interested in. It is already widely used in services where it is important to consider individual tendencies such as YouTube and Facebook, and you can experience it familiarly in providing personalized services in content services such as over-the-top media services (OTT). Classes are also a kind of content consumption in terms of selecting classes suitable for individuals from a set content list. However, unlike other content consumption, it is characterized by a large influence of selection results. For example, in the case of music and movies, it is usually consumed once and the time required to consume content is short. Therefore, the importance of each item is relatively low, and there is no deep concern in selecting. Major classes usually have a long consumption time because they have to be taken for one semester, and each item has a high importance and requires greater caution in choice because it affects many things such as career and graduation requirements depending on the composition of the selected classes. Depending on the unique characteristics of these major classes, the recommendation system in the education field supports decision-making that reflects individual characteristics that are meaningful and cannot be reflected in experience-based decision-making, even though it has a relatively small number of item ranges. This study aims to realize personalized education and enhance students' educational satisfaction by presenting a recommendation model for university major class. In the model study, class history data of undergraduate students at University from 2015 to 2017 were used, and students and their major names were used as metadata. The class history data is implicit feedback data that only indicates whether content is consumed, not reflecting preferences for classes. Therefore, when we derive embedding vectors that characterize students and classes, their expressive power is low. With these issues in mind, this study proposes a Net-NeuMF model that generates vectors of students, classes through network analysis and utilizes them as input values of the model. The model was based on the structure of NeuMF using one-hot vectors, a representative model using data with implicit feedback. The input vectors of the model are generated to represent the characteristic of students and classes through network analysis. To generate a vector representing a student, each student is set to a node and the edge is designed to connect with a weight if the two students take the same class. Similarly, to generate a vector representing the class, each class was set as a node, and the edge connected if any students had taken the classes in common. Thus, we utilize Node2Vec, a representation learning methodology that quantifies the characteristics of each node. For the evaluation of the model, we used four indicators that are mainly utilized by recommendation systems, and experiments were conducted on three different dimensions to analyze the impact of embedding dimensions on the model. The results show better performance on evaluation metrics regardless of dimension than when using one-hot vectors in existing NeuMF structures. Thus, this work contributes to a network of students (users) and classes (items) to increase expressiveness over existing one-hot embeddings, to match the characteristics of each structure that constitutes the model, and to show better performance on various kinds of evaluation metrics compared to existing methodologies.

• Journal of Life Science
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• v.30 no.1
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• pp.45-57
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• 2020

The prenatal period in livestock animals is crucial for meat production because net increase in the number of muscle fibers is finished before birth. However, there is no study on the growth and development mechanism of muscles in Hanwoo during this period. Therefore, to find candidate genes involved in muscle growth and development during this period in Hanwoo, mRNA expression data of longissimus in Hanwoo at 6 and 9 months post-conceptional age (MPA) were analyzed. We independently identified differentially expressed genes (DEGs) using DESeq2 and edgeR which are R software packages, and considered the overlaps of the results as final-DEGs to use in downstream analysis. The DEGs were classified into several modules using WGCNA then the modules' functions were analyzed to identify modules which involved in myogenesis and adipogenesis. Finally, the hub genes which had the highest WGCNA module membership among the top 10% genes of the STRING network maximal clique centrality were identified. 913(6 MPA specific DEGs) and 233(9 MPA specific DEGs) DEGs were figured out, and these were classified into five and two modules, respectively. Two of the identified modules'(one was in 6, and another was in 9 MPA specific modules) functions was found to be related to myogenesis and adipogenesis. One of the hub genes belonging to the 6 MPA specific module was axin1 (AXIN1) which is known as an inhibitor of Wnt signaling pathway, another was succinate-CoA ligase ADP-forming beta subunit (SUCLA2) which is known as a crucial component of citrate cycle.

• Journal of Applied Tourism Food and Beverage Management and Research
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• v.15 no.1
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• pp.113-125
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• 2004

Effect of microwave radiation on physico-chemical properties of cor'n starches was studied. Waxy com, com and high amylose com starches of varying moisture content(20～35%) were subjected to microwave processing(2450MHz) at $120^{\circ}$ and the experimental starch samples were examined by a X-ray diffractometry, rapid viscosity analyzer(RVA) and. with the samples in temperature was observed and the peaks of high amylose com starches at $2^{\circ}$=5.0, 15.0 and $23.0^{\circ}$, were disappeared indicating the melting of crystallines while those of com and waxy com had not changed. A change in gelatinization pattern was observed in the case of corn starches from type A with nearly no peak-viscosity and breakdown to type C. Except a decreased viscosity, no change was observed in those of waxy com starches.

• 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 Intelligence and Information Systems
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• v.15 no.4
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• pp.1-22
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• 2009

The World Wide Web is transitioning from being a mere collection of documents that contain useful information toward providing a collection of services that perform useful tasks. The emerging Web service technology has been envisioned as the next technological wave and is expected to play an important role in this recent transformation of the Web. By providing interoperable interface standards for application-to-application communication, Web services can be combined with component based software development to promote application interaction and integration both within and across enterprises. To make Web services for service-oriented computing operational, it is important that Web service repositories not only be well-structured but also provide efficient tools for developers to find reusable Web service components that meet their needs. As the potential of Web services for service-oriented computing is being widely recognized, the demand for effective Web service discovery mechanisms is concomitantly growing. A number of techniques for Web service discovery have been proposed, but the discovery challenge has not been satisfactorily addressed. Unfortunately, most existing solutions are either too rudimentary to be useful or too domain dependent to be generalizable. In this paper, we propose a Web service organizing framework that combines clustering techniques with string matching and leverages the semantics of the XML-based service specification in WSDL documents. We believe that this is one of the first attempts at applying data mining techniques in the Web service discovery domain. Our proposed approach has several appealing features : (1) It minimizes the requirement of prior knowledge from both service consumers and publishers; (2) It avoids exploiting domain dependent ontologies; and (3) It is able to visualize the semantic relationships among Web services. We have developed a prototype system based on the proposed framework using an unsupervised artificial neural network and empirically evaluated the proposed approach and tool using real Web service descriptions drawn from operational Web service registries. We report on some preliminary results demonstrating the efficacy of the proposed approach.

• Journal of the Korean Institute of Landscape Architecture
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• v.40 no.4
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• pp.90-103
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• 2012

The aim of this study is to be able to understand the problems within the urban agriculture policy promoted by the Government and local autonomous entity base on the comparison of the consciousness of the urban agriculture between urban farmers and public officials and to inquire into the further revitalization scheme in the end. For this purpose, this study drew implications through studying latest trend and the legislation of domestic and foreign urban agriculture and then conducted a questionnaire survey of urban farmers and public officials. Because of this research, the revitalization schemes of urban agriculture are as follows: First, it's necessary to secure the usable arable land, such as the green roof, community garden, as well as urban agriculture park, etc. Second, it is necessary to establish the urban agriculture relations act suited for the actual circumstances of our country and to back up the legislation at an institutional, technological level in terms of a nation in order to secure the durability of urban agriculture. Third, it is advisable to make a proposal about the problems in time of activities for cultivation by forming a network between urban farmers and public officials and to prepare the plan for the active exchange of farming technologies. Fourth, it's necessary to activate the community gardens by supplying the education through cultivation method & its management method, and a variety of urban-agriculture-participation programs. Fifth, it is necessary to set up the specialized and practical education through an institute for landscape architecture. Sixth, it is necessary to induce the spontaneous participation in urban agriculture from urban farmers accompanied by the activities for promotion that are worth arousing urban farmers' interest. Lastly, it is also necessary to establish a legal basis of urban agricultural parks and facilities as well as to promote a search for multilateral policies and their practice so that the further urban agriculture can be stably continued within city boundaries.

• Journal of the Korean Institute of Landscape Architecture
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• v.46 no.2
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• pp.37-51
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• 2018

Recently, urban parks are required to actively participate with residents in order to strengthen social functions and maintain sustainable management. This study analyzed the formation process of volunteer resident groups (Chamsamo) in the neighborhood parks in which local residents can participate in an ongoing basis based on the solidarity of a daily living space. The important factors in the activation of resident activity are derived from 5 aspects including resources, local area, resident group capacity, resident group role, and public support. The results of the study are as follows. 1) Life-friendly resources: It was important to find life-friendly resources such as 'Chamsaem' in the park. The combined resources of continuous human activities provided various benefits to the residents. This has led to stronger attachment and community activities to continue to utilize attractive resources in the park. 2) Sharing Common Daily Spaces and Expansion: As the Chamsamo activities were centered around the neighborhood, the network of activists in the local community expanded. This led to continued resident interest and favorable participation as well as to the regional expansion of Chamsamo activities. 3) Park management as part of everyday life: Park management became a part of everyday life, and pleasant park management was facilitated by utilizing the talents of the residents, who carried out diverse activities and constantly streamlined their hard labor. 4) Chamsamo's Leadership Linking Residents and the Public Sector through Leading Park Management Activities: Chamsamo served as a middle leader in linking the public sector and its users. 5) Role and Support of the Public Sector: In order to be able to sustain the activities of residents, the government's willingness to support the resident-led activities of the park in planning and operating the public sector was required. In the public management system of the park, support for residents' activities such as financing, education, and consulting was necessary.

• Journal of the Korean Geographical Society
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• v.42 no.5
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• pp.691-709
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• 2007

This paper investigates the social and political construction of geographical scale in conjunction with Korean housing politics. Recently, attention has been drawn to the issue of the social and political construction of geographical scale. Spatial scales have increasingly been regarded as socially constructed and politically contested rather than ontologically pregiven or fixed. The scale literature has paid attention to how different spatial scales can be used or articulated in social movements, with an emphasis on 'up-scaling' and 'scales of activism' rather than 'down-scaling' and 'scales of regulation.' Furthermore, the scale literature has focused on the aspect of empowerment. However, it is worthwhile to examine how scale-especially 'down-scaling' and 'scales of regulation'-can be used not only for marginalizing or excluding unprivileged social groups, but also for controlling the (re)production of space, including housing space. Under a regulatory regime, the Korean central government gained more control over the (re)production of housing space at geographical multi-scales by means of 'jumping scales,' specifically 'down-scaling.' The Korean central government has increasingly obtained the capacity to 'jump scales' by using not only multiscalar strategies for housing developments, but also taking advantage of various scales of institutional networking among the central and local governments, quasi-governmental institutions, and Chaebols, across the state. Traditionally, scale has been regarded as an analytical spatial unit or category. However, scale can be seen as means of inclusion(and exclusion) and legitimation. Choosing institutions to include or exclude cannot be separated from the choices and range of spatial scale, and is closely connected to 'scale spatiality of politics.' Facilitating different forms of 'scales of regulation,' the Korean central government included Chaebols and upper- and middle-income groups for the legitimization of housing projects, but excluded local-scale grassroots organizations and unprivileged social groups as decision-makers.

• Spatial Information Research
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• v.23 no.3
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• pp.11-21
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• 2015

While the number of total population in Seoul has been on the constant decline for the last few years, the number of household has increased due to the rising tendency of the smaller households. In 2010, the small households in the metropolitan areas accounted for 44% of the entire households, and Statistics Korea has reported that one person household, which will take up more than 30% of the whole household, will have been the most common type of household by 2020. This reason of rise will be differently shown according to age like the preferred housing type or surrounding environments, this research is suggest to research hypothesis that distinction of age leads to the spatial distribution of one person household. Therefore, this research is to exercise a multiple regression analysis targeting on the facilities, which become the spatial distribution factor of one person household, with the independent variable gained from the concluded area calculated with the area ratio of the spatial unit followed by the service area analysis based on network. The spatial unit is the census output of Seoul, and based on this the interaction between the number of one person household according to age and the factors of its distribution. Also, the spatial regions - downtown, northeast, southeast, northwest, southwest - are designed as dummy variables and the results of each region are found out. As a result, the spatial regions occupied according to age are found to be varied - people in their 20s prefer housings near the college, 30s lease or the monthly rental housings, 40s the monthly rental housings, and over 60s the housing with the floor area of less than $40m^2$. Likewise, one person household has different types of housing environments preferred according to age, and thus a housing policy concerning this will have to be suggested.