• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.127-141
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• 2017

Advances in Internet technologies and the proliferation of mobile devices enabled consumers to approach a wide range of goods and services, while causing an adverse effect that they have hard time reaching their congenial items even if they devote much time to searching for them. Accordingly, businesses are using the recommender systems to provide tools for consumers to find the desired items more easily. Association Rule Mining (ARM) technology is advantageous to recommender systems in that ARM provides intuitive form of a rule with interestingness measures (support, confidence, and lift) describing the relationship between items. Given an item, its relevant items can be distinguished with the help of the measures that show the strength of relationship between items. Based on the strength, the most pertinent items can be chosen among other items and exposed to a given item's web page. However, the diversity of the measures may confuse which items are more recommendable. Given two rules, for example, one rule's support and confidence may not be concurrently superior to the other rule's. Such discrepancy of the measures in distinguishing one rule's superiority from other rules may cause difficulty in selecting proper items for recommendation. In addition, in an online environment where a web page or mobile screen can provide a limited number of recommendations that attract consumer interest, the prudent selection of items to be included in the list of recommendations is very important. The exposure of items of little interest may lead consumers to ignore the recommendations. Then, such consumers will possibly not pay attention to other forms of marketing activities. Therefore, the measures should be aligned with the probability of consumer's acceptance of recommendations. For this reason, this study proposes a model-based approach to combine those measures into one unified measure that can consistently determine the ranking of recommended items. A regression model was designed to describe how well the measures (independent variables; i.e., support, confidence, and lift) explain consumer's acceptance of recommendations (dependent variables, hit rate of recommended items). The model is intuitive to understand and easy to use in that the equation consists of the commonly used measures for ARM and can be used in the estimation of hit rates. The experiment using transaction data from one of the Korea's largest online shopping malls was conducted to show that the proposed model can improve the hit rates of recommendations. From the top of the list to 13th place, recommended items in the higher rakings from the proposed model show the higher hit rates than those from the competitive model's. The result shows that the proposed model's performance is superior to the competitive model's in online recommendation environment. In a web page, consumers are provided around ten recommendations with which the proposed model outperforms. Moreover, a mobile device cannot expose many items simultaneously due to its limited screen size. Therefore, the result shows that the newly devised recommendation technique is suitable for the mobile recommender systems. While this study has been conducted to cover the cross-selling in online shopping malls that handle merchandise, the proposed method can be expected to be applied in various situations under which association rules apply. For example, this model can be applied to medical diagnostic systems that predict candidate diseases from a patient's symptoms. To increase the efficiency of the model, additional variables will need to be considered for the elaboration of the model in future studies. For example, price can be a good candidate for an explanatory variable because it has a major impact on consumer purchase decisions. If the prices of recommended items are much higher than the items in which a consumer is interested, the consumer may hesitate to accept the recommendations.

• Journal of KIISE:Software and Applications
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• v.30 no.5_6
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• pp.444-453
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• 2003

Recently, a great interest of bio-technology(BT) is concentrated and the image analysis technique for electrophoresis gels is highly requested to analyze genetic information or to look for some new bio-activation materials. For this purpose, the location and quantity of each band in a lane should be measured. In most of existing techniques, the approach of peak searching in a profile of a lane is used. But this peak is improper as the representative of a band, because its location does not correspond to that of the brightest pixel or the center of gravity. Also, it is improper to measure band quantity in most of these approaches because various enhancement processes are commonly applied to original images to extract peaks easily. In this paper, we adopt an approach to measure accumulated brightness as a band quantity in each band region, which Is extracted by not using any process of changing relative brightness, and the gravity center of the region is calculated as a band location. Actually, we first extract lanes with an entropy-based threshold calculated on a gel-image histogram. And then, three other methods are proposed and applied to extract bands. In the MER method, peaks and valleys are searched on a vertical search line by which each lane is bisected. And the minimum enclosing rectangle of each band is set between successive two valleys. On the other hand, in the RG-1 method, each band is extracted by using region growing with a peak as a seed, separating overlapped neighbor bands. In the RG-2 method, peaks and valleys are searched on two vertical lines by which each lane is trisected, and the left and right peaks nay be paired up if they seem to belong to the same band, and then each band region is grown up with a peak or both peaks if exist. To compare above three methods, we have measured the location and amount of bands. As a result, the average errors in band location of MER, RG-1, and RG-2 were 6%, 3%, and 1%, respectively, when the lane length is normalized to a unit value. And the average errors in band amount were 8%, 5%, and 2%, respectively, when the sum of band amount is normalized to a unit value. In conclusion, RG-2 was shown to be more reliable in the accuracy of measuring the location and amount of bands.

• Korean journal of communication and information
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• v.17
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• pp.112-144
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• 2001

This study attempts to analyze how and why Chosun Ilbo and Hankyoreh Shinmun produce particular social discourses about the media reform in different ways. In doing so, this paper attempts to disclose the ideological nature of media reform discourses in social contexts. For the purpose, a content analysis method was applied to the analysis of straight news, while an interpretive discourse analysis was appled to analyze both editorials and columns in newspapers. As a theoretical framework, an articulation theory was applied to explain the relationships among social forces, ideological elements, discourse practices and subjects to produce the media reform discourses. In doing so, I attempted to understand the overall conjuncture of the media reform aspects in social contexts. The period for the analysis was limited from January 10th to August 10th this year. Newspaper articles related to the media reform were obtained from the database of newspaper articles, "KINDS," produced by Korean Press Foundation, in searching the key word, "media reform". Total articles to be analyzed were 765, 429 from Hankyoreh Sinmun and 236 from Chosun Ilbo. The research results, first of all, empirically show that both Chosun Ilbo and Hankure Synmun used straight news for their firms' interests and value judgement, in selecting and excluding events related to media reform or in exaggerating and reducing the meanings of the events, although there are differences in a greater or less degree between two newspaper companies. Accordingly, this paper argues that the monopoly of newspaper subscriber by three major newspapers in Korean society could result in the forming of one-sided social consensus about various social issues through the distorting and unequal reporting by them. Second, this paper's discourse analysis related to the media reform indicates that the discourse of ideology confrontation between the right and the left produced by Chosen Ilbo functioned as a mechanism to realize law enforcement of the right in articulating the request of media reform and the anti-communist ideology. It resulted in the discursive effect of suppressing the request of media reform by civic groups and scholars and made many people to consider the media reform as a ideological matter in Korean society.

• Journal of Intelligence and Information Systems
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• v.17 no.1
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• pp.111-125
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• 2011

Clustering is a process of grouping similar or relevant documents into a cluster and assigning a meaningful concept to the cluster. By this process, clustering facilitates fast and correct search for the relevant documents by narrowing down the range of searching only to the collection of documents belonging to related clusters. For effective clustering, techniques are required for identifying similar documents and grouping them into a cluster, and discovering a concept that is most relevant to the cluster. One of the problems often appearing in this context is the detection of a complex concept that overlaps with several simple concepts at the same hierarchical level. Previous clustering methods were unable to identify and represent a complex concept that belongs to several different clusters at the same level in the concept hierarchy, and also could not validate the semantic hierarchical relationship between a complex concept and each of simple concepts. In order to solve these problems, this paper proposes a new clustering method that identifies and represents complex concepts efficiently. We developed the Hierarchical Overlapping Clustering (HOC) algorithm that modified the traditional Agglomerative Hierarchical Clustering algorithm to allow overlapped clusters at the same level in the concept hierarchy. The HOC algorithm represents the clustering result not by a tree but by a lattice to detect complex concepts. We developed a system that employs the HOC algorithm to carry out the goal of complex concept detection. This system operates in three phases; 1) the preprocessing of documents, 2) the clustering using the HOC algorithm, and 3) the validation of semantic hierarchical relationships among the concepts in the lattice obtained as a result of clustering. The preprocessing phase represents the documents as x-y coordinate values in a 2-dimensional space by considering the weights of terms appearing in the documents. First, it goes through some refinement process by applying stopwords removal and stemming to extract index terms. Then, each index term is assigned a TF-IDF weight value and the x-y coordinate value for each document is determined by combining the TF-IDF values of the terms in it. The clustering phase uses the HOC algorithm in which the similarity between the documents is calculated by applying the Euclidean distance method. Initially, a cluster is generated for each document by grouping those documents that are closest to it. Then, the distance between any two clusters is measured, grouping the closest clusters as a new cluster. This process is repeated until the root cluster is generated. In the validation phase, the feature selection method is applied to validate the appropriateness of the cluster concepts built by the HOC algorithm to see if they have meaningful hierarchical relationships. Feature selection is a method of extracting key features from a document by identifying and assigning weight values to important and representative terms in the document. In order to correctly select key features, a method is needed to determine how each term contributes to the class of the document. Among several methods achieving this goal, this paper adopted the $x^2$�� statistics, which measures the dependency degree of a term t to a class c, and represents the relationship between t and c by a numerical value. To demonstrate the effectiveness of the HOC algorithm, a series of performance evaluation is carried out by using a well-known Reuter-21578 news collection. The result of performance evaluation showed that the HOC algorithm greatly contributes to detecting and producing complex concepts by generating the concept hierarchy in a lattice structure.

• Information Systems Review
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• v.11 no.1
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• pp.161-179
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• 2009

Nowadays an increasing number of Internet users are running individual websites as Blog or Cyworld. As this type of personal media has a great influence on communication among people, business comes to care about Network Effect, Network Software, and Social Network. For instance, Cyworld created the web service called 'Minihompy' for individual web-logs, and acquired 2.4milion users in 2007. Although many people assumed that the popularity of Minihompy, or Blog would be a passing fad, Cyworld has improved its service, and expanded its Network with various contents. This kind of expansion reflects survival efforts from infinite competitions among ISPs (Internet Service Provider) with focus on enhancing usability to users. However, Cyworld's Network Effect is gradually diminished in these days. Both of low production cost of service vendors and the low searching/conversing costs of users combine to make ISPs hard to keep their market share sustainable. To overcome this lackluster trend, Cyworld has adopted new strategies and try to lock their users in their service. Various efforts to improve the continuance and expansion of Network effect remain unclear and uncertain. If we understand beforehand how a service would improve Network effect, and which service could bring more effect, ISPs can get substantial help in launching their new business strategy. Regardless many diverse ideas to increase their user's duration online ISPs cannot guarantee 'how the new service strategies will end up in profitability. Therefore, this research studies about Network effect of Cyworld's 'Minihompy' using System-Dynamics method which could analyze dynamic relation between users and ISPs. Furthermore, the research aims to predict changes of Network Effect based on the strategy of new service. 'Page View' and 'Duration Time' can be enhanced for the short tenn because they enhance the service functionality. However, these services cannot increase the Network in the long-run. Limitations of this research include that we predict the future merely based on the limited data. We also limit the independent variables over Network Effect only to the following two issues: Increasing the number of users and increasing the Service Functionality. Despite of some limitations, this study perhaps gives some insights to the policy makers or others facing the stiff competition in the network business.

• Korean Journal of Psychosomatic Medicine
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• v.11 no.2
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• pp.97-117
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• 2003

Objective: This study is to examine the validity and reliability of Computerized Neurocognitive Function Test among normal children in elementary school. Methods: K-ABC, K-PIC, and Computerized Neurocognitive Function Test were performed to the 120 body of normal children(10 of each male and female) from June, 2002 to January, 2003. Those children had over the average of intelligence and passed the rule out criteria. To verify test-retest reliability for those 30 children who were randomly selected, Computerized Neurocognitive Function Test was carried out again 4 weeks later. Results: As a results of correlation analysis for validity test, four of continues performance tests matched with those on adults. In the memory tests, results presented the same as previous research with a difference between forward test and backward test in short-term memory. In higher cognitive function tests, tests were consist of those with different purpose respectively. After performing factor analysis on 43 variables out of 12 tests, 10 factors were raised and the total percent of variance was 75.5%. The reasons were such as: 'sustained attention, information processing speed, vigilance, verbal learning, allocation of attention and concept formation, flexibility, concept formation, visual learning, short-term memory, and selective attention' in order. In correlation with K-ABC to prepare explanatory criteria, selectively significant correlation(p<.0.5-001) was found in subscale of K-ABC. In the test-retest reliability test, the results reflecting practice effect were found and prominent especially in higher cognitive function tests. However, split-half reliability(r=0.548-0.7726, p<.05) and internal consistency(0.628-0.878, p<.05) of each examined group were significantly high. Conclusion: The performance of Computerized Neurocognitive Function Test in normal children represented differ developmental character than that in adult. And basal information for preparing the explanatory criteria could be acquired by searching for the relation with standardized intelligence test which contains neuropsycological background.

• Journal of Life Science
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• v.20 no.3
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• pp.411-415
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• 2010

Anisakis simplex is one of the parasitic nematodes, and has a complex life cycle in crustaceans, fish, squid or whale. When people eat under-processed or raw fish, it causes anisakidosis and also plays a critical role in inducing serious allergic reactions in humans. However, no web-based database on A. simplex at the level of DNA or protein has been so far reported. In this context, we constructed a web-based database for Anisakis research. To build up the web-based database for Anisakis research, we proceeded with the following measures: First, sequences of order Ascaridida were downloaded and translated into the multifasta format which was stored as database for stand-alone BLAST. Second, all of the nucleotide and EST sequences were clustered and assembled. And EST sequences were translated into amino acid sequences for Nuclear Localization Signal prediction. In addition, we added the vector, E. coli, and repeat sequences into the database to confirm a potential contamination. The web-based database gave us several advantages. Only data that agrees with the nucleotide sequences directly related with the order Ascaridida can be found and retrieved when searching BLAST. It is also very convenient to confirm contamination when making the cDNA or genomic library from Anisakis. Furthermore, BLAST results on the Anisakis sequence information can be quickly accessed. Taken together, the Web-based database on A. simplex will be valuable in developing species specific PCR markers and in studying SNP in A. simplex-related researches in the future.

• The Korean Journal of Archival Studies
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• no.14
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• pp.153-190
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• 2006

Externally, business environment in public institution has being changed as government business reference model(BRM) appeared and business management systems for transparency of a policy decision process are introduced. After Records Automation System started its operation, dissatisfaction grows because of inadequacy in system function and the problems about authenticity of electronic records. With these backgrounds, National Archives and Records Service had carried out 'Information Strategy Planning for Reform to Records Management System' for 5 months from September, 2005. As result, this project reengineers current records management processes and presents the world-class system model. After Records and Archives Management Act was made, the records management in public institution has propelled the concept that paper records are handled by means of the electric data management. In this reformed model, however, we concentrates on the electric records, which have gradually replaced the paper records and investigate on the management methodology considering attributes of electric records. According to this new paradigm, the electric records management raises a new issue in the records management territory. As the major contents of the models connecting with electric records management were analyzed and their significance and bounds were closely reviewed, the aim of this paper is the understanding of the future bearings of the management system. Before the analysis of the reformed models, issues in new business environments and their records management were reviewed. The government's BRM and Business management system prepared the general basis that can manage government's whole results on the online and classify them according to its function. In this points, the model is innovative. However considering the records management, problems such as division into Records Classification, definitions and capturing methods of records management objects, limitations of Records Automation System and so on was identified. For solving these problems, the reformed models that has a records classification system based on the business classification, extended electronic records filing system, added functions for strengthening electric records management and so on was proposed. As regards dramatically improving the role of records center in public institution, searching for the basic management methodology of the records management object from various agency and introducing the detail design to keep documents' authenticity, this model forms the basis of the electric records management system. In spite of these innovations, however, the proposed system for real electric records management era is still in its beginning. In near feature, when the studies is concentrated upon the progress of qualified classifications, records capturing plans for foreign records structures such like administration information system, the further study of the previous preservation technology, the developed prospective of electric records management system will be very bright.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.79-105
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• 2023

Due to unprecedented extreme weather events such as global warming and climate change, many parts of the world suffer from severe pain, and economic losses are also snowballing. In order to address these problems, 'The Paris Agreement' was signed in 2016, and an intergovernmental consultative body was formed to keep the average temperature rise of the Earth below 1.5℃. Korea also declared 'Carbon Neutrality in 2050' to prevent climate catastrophe. In particular, it was found that the increase in temperature caused by greenhouse gas emissions hurts the environment and society as a whole, as well as the export-dependent economy of Korea. In addition, as the diversification of transportation types is accelerating, the change in means of choice is also increasing. As the development paradigm in the low-growth era changes to urban regeneration, interest in idle railway sites is rising due to reduced demand for routes, improvement of alignment, and relocation of urban railways. Meanwhile, it is possible to partially achieve the solar power generation goal of 'Renewable Energy 3020' by utilizing already developed but idle railway sites and take advantage of being free from environmental damage and resident acceptance issues surrounding the location; but the actual use and plan for these solar power facilities are still lacking. Therefore, in this study, using the big data provided by the Korea National Railway and the Renewable Energy Cloud Platform, we develop an algorithm to discover and analyze suitable idle sites where solar power generation facilities can be installed and identify potentially applicable areas considering conditions desired by users. By searching and deriving these idle but relevant sites, it is intended to devise a plan to save enormous costs for facilities or expansion in the early stages of development. This study uses various cluster analyses to develop an optimal algorithm that can derive solar power plant locations on idle railway sites and, as a result, suggests 202 'actively recommended areas.' These results would help decision-makers make rational decisions from the viewpoint of simultaneously considering the economy and the environment.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.89-105
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• 2014

After emergence of Internet, social media with highly interactive Web 2.0 applications has provided very user friendly means for consumers and companies to communicate with each other. Users have routinely published contents involving their opinions and interests in social media such as blogs, forums, chatting rooms, and discussion boards, and the contents are released real-time in the Internet. For that reason, many researchers and marketers regard social media contents as the source of information for business analytics to develop business insights, and many studies have reported results on mining business intelligence from Social media content. In particular, opinion mining and sentiment analysis, as a technique to extract, classify, understand, and assess the opinions implicit in text contents, are frequently applied into social media content analysis because it emphasizes determining sentiment polarity and extracting authors' opinions. A number of frameworks, methods, techniques and tools have been presented by these researchers. However, we have found some weaknesses from their methods which are often technically complicated and are not sufficiently user-friendly for helping business decisions and planning. In this study, we attempted to formulate a more comprehensive and practical approach to conduct opinion mining with visual deliverables. First, we described the entire cycle of practical opinion mining using Social media content from the initial data gathering stage to the final presentation session. Our proposed approach to opinion mining consists of four phases: collecting, qualifying, analyzing, and visualizing. In the first phase, analysts have to choose target social media. Each target media requires different ways for analysts to gain access. There are open-API, searching tools, DB2DB interface, purchasing contents, and so son. Second phase is pre-processing to generate useful materials for meaningful analysis. If we do not remove garbage data, results of social media analysis will not provide meaningful and useful business insights. To clean social media data, natural language processing techniques should be applied. The next step is the opinion mining phase where the cleansed social media content set is to be analyzed. The qualified data set includes not only user-generated contents but also content identification information such as creation date, author name, user id, content id, hit counts, review or reply, favorite, etc. Depending on the purpose of the analysis, researchers or data analysts can select a suitable mining tool. Topic extraction and buzz analysis are usually related to market trends analysis, while sentiment analysis is utilized to conduct reputation analysis. There are also various applications, such as stock prediction, product recommendation, sales forecasting, and so on. The last phase is visualization and presentation of analysis results. The major focus and purpose of this phase are to explain results of analysis and help users to comprehend its meaning. Therefore, to the extent possible, deliverables from this phase should be made simple, clear and easy to understand, rather than complex and flashy. To illustrate our approach, we conducted a case study on a leading Korean instant noodle company. We targeted the leading company, NS Food, with 66.5% of market share; the firm has kept No. 1 position in the Korean "Ramen" business for several decades. We collected a total of 11,869 pieces of contents including blogs, forum contents and news articles. After collecting social media content data, we generated instant noodle business specific language resources for data manipulation and analysis using natural language processing. In addition, we tried to classify contents in more detail categories such as marketing features, environment, reputation, etc. In those phase, we used free ware software programs such as TM, KoNLP, ggplot2 and plyr packages in R project. As the result, we presented several useful visualization outputs like domain specific lexicons, volume and sentiment graphs, topic word cloud, heat maps, valence tree map, and other visualized images to provide vivid, full-colored examples using open library software packages of the R project. Business actors can quickly detect areas by a swift glance that are weak, strong, positive, negative, quiet or loud. Heat map is able to explain movement of sentiment or volume in categories and time matrix which shows density of color on time periods. Valence tree map, one of the most comprehensive and holistic visualization models, should be very helpful for analysts and decision makers to quickly understand the "big picture" business situation with a hierarchical structure since tree-map can present buzz volume and sentiment with a visualized result in a certain period. This case study offers real-world business insights from market sensing which would demonstrate to practical-minded business users how they can use these types of results for timely decision making in response to on-going changes in the market. We believe our approach can provide practical and reliable guide to opinion mining with visualized results that are immediately useful, not just in food industry but in other industries as well.