• Journal of Korean Society of societal Security
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• v.4 no.2
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• pp.29-40
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• 2011

The objective of this paper is to develop an intelligent decision support system that is able to advise disaster countermeasures and degree of incidents on the basis of the collected and analyzed signs of disasters. The concepts derived from ontology, text mining and case-based reasoning are adapted to design the system. The functions of this system include term-document matrix, frequency normalization, confidency, association rules, and criteria for judgment. The collected qualitative data from signs of new incidents are processed by those functions and are finally compared and reasoned to past similar disaster cases. The system provides the varying degrees of how dangerous the new signs of disasters are and the few countermeasures to the disaster for the manager of disaster management. The system will be helpful for the decision-maker to make a judgment about how much dangerous the signs of disaster are and to carry out specific kinds of countermeasures on the disaster in advance. As a result, the disaster will be prevented.

• Journal of Korea Society of Industrial Information Systems
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• v.15 no.4
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• pp.17-27
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• 2010

Major IT vendors expect that cloud computing technology makes it possible to reduce the contents service cycle, speed up application deployment and skip the installation process, reducing operational costs, proactive management etc. However, cloud computing environment for massive content service solutions requires high-performance data processing to reduce the time of data processing and analysis. In this study, Efficient_Cloud_Processing_Scheme(ECPS) is proposed for allocation of resources for massive content services. For high-performance services, optimized resource allocation plan is presented using MapReduce programming techniques and association rules that is used to detect hidden patterns in data mining, based on levels of Hadoop platform(Infrastructure as a service). The proposed ECPS has brought more than 20% improvement in performance and speed compared to the traditional methods.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.43-56
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• 2020

Recommender systems help users make the best choice among various options. Especially, recommender systems play important roles in internet sites as digital information is generated innumerable every second. Many studies on recommender systems have focused on an accurate recommendation. However, there are some problems to overcome in order for the recommendation system to be commercially successful. First, there is a lack of transparency in the recommender system. That is, users cannot know why products are recommended. Second, the recommender system cannot immediately reflect changes in user preferences. That is, although the preference of the user's product changes over time, the recommender system must rebuild the model to reflect the user's preference. Therefore, in this study, we proposed a recommendation methodology using topic modeling and sequential association rule mining to solve these problems from review data. Product reviews provide useful information for recommendations because product reviews include not only rating of the product but also various contents such as user experiences and emotional state. So, reviews imply user preference for the product. So, topic modeling is useful for explaining why items are recommended to users. In addition, sequential association rule mining is useful for identifying changes in user preferences. The proposed methodology is largely divided into two phases. The first phase is to create user profile based on topic modeling. After extracting topics from user reviews on products, user profile on topics is created. The second phase is to recommend products using sequential rules that appear in buying behaviors of users as time passes. The buying behaviors are derived from a change in the topic of each user. A collaborative filtering-based recommendation system was developed as a benchmark system, and we compared the performance of the proposed methodology with that of the collaborative filtering-based recommendation system using Amazon's review dataset. As evaluation metrics, accuracy, recall, precision, and F1 were used. For topic modeling, collapsed Gibbs sampling was conducted. And we extracted 15 topics. Looking at the main topics, topic 1, top 3, topic 4, topic 7, topic 9, topic 13, topic 14 are related to "comedy shows", "high-teen drama series", "crime investigation drama", "horror theme", "British drama", "medical drama", "science fiction drama", respectively. As a result of comparative analysis, the proposed methodology outperformed the collaborative filtering-based recommendation system. From the results, we found that the time just prior to the recommendation was very important for inferring changes in user preference. Therefore, the proposed methodology not only can secure the transparency of the recommender system but also can reflect the user's preferences that change over time. However, the proposed methodology has some limitations. The proposed methodology cannot recommend product elaborately if the number of products included in the topic is large. In addition, the number of sequential patterns is small because the number of topics is too small. Therefore, future research needs to consider these limitations.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.111-126
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• 2017

Recommender systems based on association rule mining significantly contribute to seller's sales by reducing consumers' time to search for products that they want. Recommendations based on the frequency of transactions such as orders can effectively screen out the products that are statistically marketable among multiple products. A product with a high possibility of sales, however, can be omitted from the recommendation if it records insufficient number of transactions at the beginning of the sale. Products missing from the associated recommendations may lose the chance of exposure to consumers, which leads to a decline in the number of transactions. In turn, diminished transactions may create a vicious circle of lost opportunity to be recommended. Thus, initial sales are likely to remain stagnant for a certain period of time. Products that are susceptible to fashion or seasonality, such as clothing, may be greatly affected. This study was aimed at expanding association rules to include into the list of recommendations those products whose initial trading frequency of transactions is low despite the possibility of high sales. The particular purpose is to predict the strength of the direct connection of two unconnected items through the properties of the paths located between them. An association between two items revealed in transactions can be interpreted as the interaction between them, which can be expressed as a link in a social network whose nodes are items. The first step calculates the centralities of the nodes in the middle of the paths that indirectly connect the two nodes without direct connection. The next step identifies the number of the paths and the shortest among them. These extracts are used as independent variables in the regression analysis to predict future connection strength between the nodes. The strength of the connection between the two nodes of the model, which is defined by the number of nodes between the two nodes, is measured after a certain period of time. The regression analysis results confirm that the number of paths between the two products, the distance of the shortest path, and the number of neighboring items connected to the products are significantly related to their potential strength. This study used actual order transaction data collected for three months from February to April in 2016 from an online commerce company. To reduce the complexity of analytics as the scale of the network grows, the analysis was performed only on miscellaneous goods. Two consecutively purchased items were chosen from each customer's transactions to obtain a pair of antecedent and consequent, which secures a link needed for constituting a social network. The direction of the link was determined in the order in which the goods were purchased. Except for the last ten days of the data collection period, the social network of associated items was built for the extraction of independent variables. The model predicts the number of links to be connected in the next ten days from the explanatory variables. Of the 5,711 previously unconnected links, 611 were newly connected for the last ten days. Through experiments, the proposed model demonstrated excellent predictions. Of the 571 links that the proposed model predicts, 269 were confirmed to have been connected. This is 4.4 times more than the average of 61, which can be found without any prediction model. This study is expected to be useful regarding industries whose new products launch quickly with short life cycles, since their exposure time is critical. Also, it can be used to detect diseases that are rarely found in the early stages of medical treatment because of the low incidence of outbreaks. Since the complexity of the social networking analysis is sensitive to the number of nodes and links that make up the network, this study was conducted in a particular category of miscellaneous goods. Future research should consider that this condition may limit the opportunity to detect unexpected associations between products belonging to different categories of classification.

• The Journal of Bigdata
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• v.5 no.2
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• pp.201-213
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• 2020

Despite empirical research that the response to human risk is significantly influenced affective factors, the role of affective factors has been unexplored in information privacy research. This study aims to explore the privacy behaviors of location-based service (LBS) users from an affective point of view. Specifically, the study explored the relationship between three types of privacy threats (collection, hacking, secondary use), two affects (worry, anger), and a coping behavior (continuous use intentions). The structured survey was conducted with 552 users. In order to analyze the effect of the combination of perception of particular privacy threats and particular affects on the intention of continuous use, association rules, one of the data mining techniques, was employed. As a result, there was a difference in the intention to use according to the combination of the perception of risk and affect responses, and the most significant influence on the intention is when the second use of personal information was combined with anger. This study has significant theoretical contribution in that it includes affective factors in the research of information privacy users, complementing the biases of existing cognition-oriented approaches and providing a comprehensive understanding of privacy response behavior.

• Information Systems Review
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• v.15 no.3
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• pp.1-37
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• 2013

This study is conducted to explore the possibility of service convergence to promote mobile learning. This study has attempted to identify how mobile learning service is provided, which services among them are considered most popular, and which services are highly demanded by users. This study has also investigated the potential opportunities for service convergence of mobile service and e-learning. This research is then extended to examine the possibility of active convergence of common services in mobile services and e-learning. Important variables have been identified from related web pages of portal sites using social network analysis (SNA) and association rules. Due to the differences in number and type of variables on different web pages, SNA was used to deal with the difficulties of identifying the degree of complex connection. Association analysis has been used to identify association rules among variables. The study has revealed that most frequent services among common services of mobile services and e-learning were Games and SNS followed by Payment, Advertising, Mail, Event, Animation, Cloud, e-Book, Augmented Reality and Jobs. This study has also found that Search, News, GPS in mobile services were turned out to be very highly demanded while Simulation, Culture, Public Education were highly demanded in e-learning. In addition, It has been found that variables involving with high service convergence based on common variables of mobile and e-learning services were Games and SNS, Games and Sports, SNS and Advertising, Games and Event, SNS and e-Book, Games and Community in mobile services while Games, Animation, Counseling, e-Book, being preceding services Simulation, Speaking, Public Education, Attendance Management were turned out be highly convergent in e-learning services. Finally, this study has attempted to predict possibility of active service convergence focusing on Games, SNS, e-Book which were highly demanded common services in mobile and e-learning services. It is expected that this study can be used to suggest a strategic direction to promote mobile learning by converging mobile services and e-learning.

• Journal of the Korean Data Analysis Society
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• v.20 no.6
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• pp.2907-2915
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• 2018

Journal of the Korean Data Analysis Society founded in 1998 has played the role of a major application journal. In this study, we checked the objective of this journal by checking the abstracts for 10 years. Abstract data was crawled from the online journal site (kdas.jems.or.kr) and analyzed by topic model. As a result, we found 18 topics from 2680 abstracts that had several contents, for example, nursing, marketing, economics, regression, factor analysis, data mining and statistical inferences. Topic1 (regression) is most frequent with 460 documents and we found the usefulness of regression in the applied science area. We confirmed the significant 10 association rules using by Fisher's exact test. Also, for exploring the trend of topics, we conducted the topic analysis for two periods which are 2006-2011 period and 2012-2016 period. We found that the control study was more frequent than survey study over time and regression and factor analysis were frequent regardless of time.