• Journal of Internet Computing and Services
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• v.16 no.6
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• pp.89-95
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• 2015

Traditional frequent pattern mining discovers valid patterns with no smaller frequency than a user-defined minimum threshold from databases. In this framework, an enormous number of patterns may be extracted by a too low threshold, which makes result analysis difficult, and a too high one may generate no valid pattern. Setting an appropriate threshold is not an easy task since it requires the prior knowledge for its domain. Therefore, a pattern mining approach that is not based on the domain knowledge became needed due to inability of the framework to predict and control mining results precisely according to the given threshold. Top-k frequent pattern mining was proposed to solve the problem, and it mines top-k important patterns without any threshold setting. Through this method, users can find patterns from ones with the highest frequency to ones with the k-th highest frequency regardless of databases. In this paper, we provide knowledge both on frequent and top-k pattern mining. Although top-k frequent pattern mining extracts top-k significant patterns without the setting, it cannot consider both item quantities in transactions and relative importance of items in databases, and this is why the method cannot meet requirements of many real-world applications. That is, patterns with low frequency can be meaningful, and vice versa, in the applications. High utility pattern mining was proposed to reflect the characteristics of non-binary databases and requires a minimum threshold. Recently, top-k high utility pattern mining has been developed, through which users can mine the desired number of high utility patterns without the prior knowledge. In this paper, we analyze two algorithms related to top-k high utility pattern mining in detail. We also conduct various experiments for the algorithms on real datasets and study improvement point and development direction of top-k high utility pattern mining through performance analysis with respect to the experimental results.

• Asia pacific journal of information systems
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• v.24 no.2
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• pp.233-253
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• 2014

Can the stock market really be predicted? Stock market prediction has attracted much attention from many fields including business, economics, statistics, and mathematics. Early research on stock market prediction was based on random walk theory (RWT) and the efficient market hypothesis (EMH). According to the EMH, stock market are largely driven by new information rather than present and past prices. Since it is unpredictable, stock market will follow a random walk. Even though these theories, Schumaker [2010] asserted that people keep trying to predict the stock market by using artificial intelligence, statistical estimates, and mathematical models. Mathematical approaches include Percolation Methods, Log-Periodic Oscillations and Wavelet Transforms to model future prices. Examples of artificial intelligence approaches that deals with optimization and machine learning are Genetic Algorithms, Support Vector Machines (SVM) and Neural Networks. Statistical approaches typically predicts the future by using past stock market data. Recently, financial engineers have started to predict the stock prices movement pattern by using the SNS data. SNS is the place where peoples opinions and ideas are freely flow and affect others' beliefs on certain things. Through word-of-mouth in SNS, people share product usage experiences, subjective feelings, and commonly accompanying sentiment or mood with others. An increasing number of empirical analyses of sentiment and mood are based on textual collections of public user generated data on the web. The Opinion mining is one domain of the data mining fields extracting public opinions exposed in SNS by utilizing data mining. There have been many studies on the issues of opinion mining from Web sources such as product reviews, forum posts and blogs. In relation to this literatures, we are trying to understand the effects of SNS exposures of firms on stock prices in Korea. Similarly to Bollen et al. [2011], we empirically analyze the impact of SNS exposures on stock return rates. We use Social Metrics by Daum Soft, an SNS big data analysis company in Korea. Social Metrics provides trends and public opinions in Twitter and blogs by using natural language process and analysis tools. It collects the sentences circulated in the Twitter in real time, and breaks down these sentences into the word units and then extracts keywords. In this study, we classify firms' exposures in SNS into two groups: positive and negative. To test the correlation and causation relationship between SNS exposures and stock price returns, we first collect 252 firms' stock prices and KRX100 index in the Korea Stock Exchange (KRX) from May 25, 2012 to September 1, 2012. We also gather the public attitudes (positive, negative) about these firms from Social Metrics over the same period of time. We conduct regression analysis between stock prices and the number of SNS exposures. Having checked the correlation between the two variables, we perform Granger causality test to see the causation direction between the two variables. The research result is that the number of total SNS exposures is positively related with stock market returns. The number of positive mentions of has also positive relationship with stock market returns. Contrarily, the number of negative mentions has negative relationship with stock market returns, but this relationship is statistically not significant. This means that the impact of positive mentions is statistically bigger than the impact of negative mentions. We also investigate whether the impacts are moderated by industry type and firm's size. We find that the SNS exposures impacts are bigger for IT firms than for non-IT firms, and bigger for small sized firms than for large sized firms. The results of Granger causality test shows change of stock price return is caused by SNS exposures, while the causation of the other way round is not significant. Therefore the correlation relationship between SNS exposures and stock prices has uni-direction causality. The more a firm is exposed in SNS, the more is the stock price likely to increase, while stock price changes may not cause more SNS mentions.

• Journal of Intelligence and Information Systems
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• v.17 no.1
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• pp.139-152
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• 2011

Recommender systems have become an important research field since the emergence of the first paper on collaborative filtering in the mid-1990s. In general, recommender systems are defined as the supporting systems which help users to find information, products, or services (such as books, movies, music, digital products, web sites, and TV programs) by aggregating and analyzing suggestions from other users, which mean reviews from various authorities, and user attributes. However, as academic researches on recommender systems have increased significantly over the last ten years, more researches are required to be applicable in the real world situation. Because research field on recommender systems is still wide and less mature than other research fields. Accordingly, the existing articles on recommender systems need to be reviewed toward the next generation of recommender systems. However, it would be not easy to confine the recommender system researches to specific disciplines, considering the nature of the recommender system researches. So, we reviewed all articles on recommender systems from 37 journals which were published from 2001 to 2010. The 37 journals are selected from top 125 journals of the MIS Journal Rankings. Also, the literature search was based on the descriptors "Recommender system", "Recommendation system", "Personalization system", "Collaborative filtering" and "Contents filtering". The full text of each article was reviewed to eliminate the article that was not actually related to recommender systems. Many of articles were excluded because the articles such as Conference papers, master's and doctoral dissertations, textbook, unpublished working papers, non-English publication papers and news were unfit for our research. We classified articles by year of publication, journals, recommendation fields, and data mining techniques. The recommendation fields and data mining techniques of 187 articles are reviewed and classified into eight recommendation fields (book, document, image, movie, music, shopping, TV program, and others) and eight data mining techniques (association rule, clustering, decision tree, k-nearest neighbor, link analysis, neural network, regression, and other heuristic methods). The results represented in this paper have several significant implications. First, based on previous publication rates, the interest in the recommender system related research will grow significantly in the future. Second, 49 articles are related to movie recommendation whereas image and TV program recommendation are identified in only 6 articles. This result has been caused by the easy use of MovieLens data set. So, it is necessary to prepare data set of other fields. Third, recently social network analysis has been used in the various applications. However studies on recommender systems using social network analysis are deficient. Henceforth, we expect that new recommendation approaches using social network analysis will be developed in the recommender systems. So, it will be an interesting and further research area to evaluate the recommendation system researches using social method analysis. This result provides trend of recommender system researches by examining the published literature, and provides practitioners and researchers with insight and future direction on recommender systems. We hope that this research helps anyone who is interested in recommender systems research to gain insight for future research.

• Journal of Intelligence and Information Systems
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• v.17 no.3
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• pp.115-130
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• 2011

Many online stores bring features that can build trust in their customers. More so, the number of products or content services on online stores has been increasing rapidly. Hence, personalization on online stores is considered to be an important technology to companies and customers. Recommender systems that provide favorable products and customer product reviews to users are the most commonly used features in this purpose. There are many studies to that investigated the relationship between social presence as an antecedent of trust and provision of recommender systems or customer product reviews. Many online stores have made efforts to increase perceived social presence of their customers through customer reviews, recommender systems, and analyzing associations among products. Primarily because social presence can increase customer trust or reuse intention for online stores. However, there were few studies that investigated the interactions between recommendation type, product type and provision of customer product reviews on social presence. Therefore, one of the purposes of this study is to identify the effects of personalized recommender systems and compare the role of customer reviews with product types. This study performed an experiment to see these interactions. Experimental web pages were developed with $2{\times}2$ factorial setting based on how to provide social presence to users with customer reviews and two product types such as hedonic and utilitarian. The hedonic type was a ringtone chosen from Nate.com while the utilitarian was a TOEIC study aid book selected from Yes24.com. To conduct the experiment, web based experiments were conducted for the participants who have been shopping on the online stores. Participants were a total of 240 and 30% of the participants had the chance of getting the presents. We found out that social presence increased for hedonic products when personalized recommendations were given compared to non.personalized recommendations. Although providing customer reviews for two product types did not significantly increase social presence, provision of customer product reviews for hedonic (ringtone) increased perceived social presence. Otherwise, provision of customer product reviews could not increase social presence when the systems recommend utilitarian products (TOEIC study.aid books). Therefore, it appears that the effects of increasing perceived social presence with customer reviews have a difference for product types. In short, the role of customer reviews could be different based on which product types were considered by customers when they are making a decision related to purchasing on the online stores. Additionally, there were no differences for increasing perceived social presence when providing customer reviews. Our participants might have focused on how recommendations had been provided and what products were recommended because our developed systems were providing recommendations after participants rating their preferences. Thus, the effects of customer reviews could appear more clearly if our participants had actual purchase opportunity for the recommendations. Personalized recommender systems can increase social presence of customers more than nonpersonalized recommender systems by using user preference. Online stores could find out how they can increase perceived social presence and satisfaction of their customers when customers want to find the proper products with recommender systems and customer reviews. In addition, the role of customer reviews of the personalized recommendations can be different based on types of the recommended products. Even if this study conducted two product types such as hedonic and utilitarian, the results revealed that customer reviews for hedonic increased social presence of customers more than customer reviews for utilitarian. Thus, online stores need to consider the role of providing customer reviews with highly personalized information based on their product types when they develop the personalized recommender systems.

• Asian Journal of Atmospheric Environment
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• v.8 no.1
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• pp.35-47
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• 2014

The purpose of the study is to evaluate the pollution level (gaseous and particle phase) in the public facilities for the PAHs, non-regulated materials, forecast the risk level by the health risk assessment (HRA) and propose the guideline level. PAH assessments through sampling of particulate matter of diameter < 2.5 ${\mu}m$ ($PM_{2.5}$). The user and worker exposure scenario for the PAHs consists of 24-hour exposure scenario (WIES) assuming the worst case and the normal exposure scenario (MIES) based on the survey. This study investigated 20 PAH substances selected out of 32 substances known to be carcinogenic or potentially carcinogenic. The risk assessment applies major toxic equivalency factor (TEF) proposed from existing studies and estaimates individual Excess Cancer Risk (ECR). The study assesses the fine dusts ($PM_{2.5}$) and the exposure levels of the gaseous and particle PAH materials for 6 spots in each 8 facility, e.g. underground subway stations, child-care facilities, elderly care facilities, super market, indoor parking lot, terminal waiting room, internet caf$\acute{e}$ (PC-rooms), movie theater. For internet caf$\acute{e}$ (PC-rooms) in particular, that marks the highest $PM_{2.5}$ concentration and the average concentration of 10 spots (2 spots for each cafe) is 73.3 ${\mu}g/m^3$ (range: 6.8-185.2 ${\mu}g/m^3$). The high level of $PM_{2.5}$ seen in internet cafes was likely due to indoor smoking in most cases. For the gaseous PAHs, the detection frequency for 4-5 rings shows high and the elements with 6 rings shows low frequency. For the particle PAHs, the detection frequency for 2-3 rings shows low and the elements with 6 rings show high frequency. As a result, it is investigated that the most important PAHs are the naphthalene, acenaphthene and phenanthrene from the study of Kim et al. (2013) and this annual study. The health risk assessment demonstrates that each facility shows the level of $10^{-6}-10^{-4}$. Considering standards and local source of pollution levels, it is judged that the management standard of the benzo (a)pyrene, one of the PAHs, shall be managed with the range of 0.5-1.2 $ng/m^3$. Smoking and ventilation were considered as the most important PAHs exposure associated with public facility $PM_{2.5}$. This study only estimated for inhalation health risk of PAHs and focused on the associated cancer risk, while multiple measurements would be necessary for public health and policy.

• Journal of the Korea Society of Computer and Information
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• v.24 no.12
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• pp.51-57
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• 2019

In this paper, we analyzed the performance of IoT based smart wearable mine detection device. There are various mine detection methods currently used by the military. Still, in the general field, mine detection is performed by visual detection, probe detection, detector detection, and other detection methods. The detection method by the detector is using a GPR sensor on the detector, which is possible to detect metals, but it is difficult to identify non-metals. It is hard to distinguish whether the area where the detection was performed or not. Also, there is a problem that a lot of human resources and time are wasted, and if the user does not move the sensor at a constant speed or moves too fast, it is difficult to detect landmines accurately. Therefore, we studied the smart wearable mine detection device composed of human body antenna, main microprocessor, smart glasses, body-mounted LCD monitor, wireless data transmission, belt type power supply, black box camera, which is to improve the problem of the error of mine detection using unidirectional ultrasonic sensing signal. Based on the results of this study, we will conduct an experiment to confirm the possibility of detecting underground mines based on the Internet of Things (IoT). This paper consists of an introduction, experimental environment composition, simulation analysis, and conclusion. Introduction introduces the research contents such as mines, mine detectors, and research progress. It consists of large anti-personnel mine, M16A1 fragmented anti-mine, M15 and M19 antitank mines, plastic bottles similar to mines and aluminum cans. Simulation analysis is conducted by using MATLAB to analyze the mine detection device implementation performance, generating and transmitting IoT signals, and analyzing each received signal to verify the detection performance of landmines. Then we will measure the performance through the simulation of IoT-based mine detection algorithm so that we will prove the possibility of IoT-based detection landmine.

• Journal of Korean Public Health Nursing
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• v.10 no.1
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• pp.118-138
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• 1996

Because of accelerated urbanization public body visiting nursing project that started according as matter of health on urban class in the lower brackets of income was concentrated on Social interests has a unsatisfied points to propel project efficiently from the lack of rating materials. Therefore centering around written contents in documentary literature of citizen health by household in five years from starting year of project to now. visiting frequency by medical manpower was evaluated quantitatively and qualitatively in aspect of management hereupon. for the sake of giving a basic materials for public health project of this field. This research presents documentary literature of citizen health which become materials is that as one person's charged region of nurse in duty scale. district is Kang-Buck Gu. the object is resident in the lower brackets of income grounded livelihood protection law and who is admitted by the head of organ~chief of health care). and the number of material centering around the head of a household is 415 copy. The result of research is summarized. as follow. 1. Average visiting frequency examinated by medical manpower show difference according to valuables of supervision characteristics namely average visiting. Frequency of nurse has long term residence in case registration season is early and supervision season is the first year and is high incase a kind of house is unlicdnsed mountain town. Average visiting frequency with doctor is high incase supervision season is the first year and the medical insurance system is admitted by chief of health care. That shows that a man of discomfort behavior left alone are yet many in local society. The meaning of this result shows that the continuity of official relation about class in the lowest brackets of income of long term residence goes well between househole who is a user of visiting nursing service of the object according to midway income under management influences a given duty of nurse s and so causes quantitative decrease. 2. In case behavier and condition of health that nurse diagnoses are bad. as the type matter is a lack of health and the number of patient is large. the average visiting frequency of nurse is high. because average visiting frequency with doctor is high as the condition of health is bad and the number of patient is large. That is similar with that of nurse. CD Average visiting frequency of nurse s seen by matter of disease is very high only in apoplexy by 39.50 and is confined within limits from 7.63 to 11.36 in other disease. But average visiting frequency with doctor is double as many as that of nurse but defined in apoplexy hypertension and articulate. (1) Average visiting frequency of nurse by existence in inoculation of hepatitis is low by 6.73 in unidentified group and very high by 26.89 in group of non-inoculation and the case of the antigenic positive man of B type hepatitis or epileptic who can't be inoculated shows 13.00 and that even family nursing service is needed to them. That result shows that though one person nurse of local charge has a large scale of duty. as visting nursing service is given a class who has a large demand preferentially by respectively accurate nursing diagnosis. the number of diagnosis service is similar with it. 3. During five years. average visiting frequency of nurse is 10.84 and average visiting frequency with doctor is 76.50 seeing from the official scale of nurse. visiting by household is performed two more per year to the average. Seeing this by type of service. average visiting frequency of nurse is higher in indirectly nursing than in directly nursing and that suggests that at the time of visiting household nurse performs education of protection lively save patient but at the time of contrastedly visiting with doctor. directly nursing is more contents of service show no difference by man power and medication dressing by demand is 14.3 and 18.6 the aid of hardship term of doctor and nurse is high by 18.7 and 17.00 in the request of hospitalization when seeing by demands. 4. Action by turns exemplified 1994 is well in sequence of 2/4 turn. 3/4 turn. 1/4 turn. 4/4 turn. When seen by average visiting frequency of nurse but gradually is even. Without difference by turns. average visiting frequency of doctor is much higher in 1/4 turn than other turns. Type of service by turns is all even but directly nursing is inactive in 4/4 and indirectly nursing. Very increases in 4/4 and so. Nurse's quantity of duty is plentiful that shows that by evaluation of last turn and plan of project. Contents of service follows that medication and dressing is the highest by' 5.57 in 1/4turn. goes down gradually by turn. becomes 3.57 in 3/4 turn. and increases again by 4.83 in 4/4 turn. the rest service is higher in 2/4 turn than other turns. 5. Total visiting frequency of nurse is explained to total $37.5\%$ by six valuables of visiting frequency of doctor. nursing demand. demand of diagnosis. condition of behavior. year. Special terms and magnitude of influential power is the same as sequence of enumerated valuables. Namely. the higher the visiting frequency of doctor. the bigger nursing and demand of diagnosis is. the worse the condition of behavior is. the older the object is and the more the household of special terms is. the high total visiting frequency of nurse is.

• Journal of Korea Spatial Information System Society
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• v.7 no.3 s.15
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• pp.67-81
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• 2005

Recently, the interest and research on the development of new application services such as the Location Based Service and Telemetics providing the emergency service, neighbor information search, and route search according to the development of the Geographic Information System have been increasing. User's search in the spatio-temporal database which is used in the field of Location Based Service or Telemetics usually fixes the current time on the time axis and queries the spatial and aspatial attributes. Thus, if the range of query on the time axis is extensive, it is difficult to efficiently deal with the search operation. For solving this problem, the snapshot, a method to summarize the location data of moving objects, was introduced. However, if the range to store data is wide, more space for storing data is required. And, the snapshot is created even for unnecessary space that is not frequently used for search. Thus, non storage space and memory are generally used in the snapshot method. Therefore, in this paper, we suggests the Hash-based Spatio-Temporal Clustering Algorithm(H-STCA) that extends the two-dimensional spatial hash algorithm used for the spatial clustering in the past to the three-dimensional spatial hash algorithm for overcoming the disadvantages of the snapshot method. And, this paper also suggests the knowledge extraction algorithm to extract the knowledge for the path search of moving objects from the past location data based on the suggested H-STCA algorithm. Moreover, as the results of the performance evaluation, the snapshot clustering method using H-STCA, in the search time, storage structure construction time, optimal path search time, related to the huge amount of moving object data demonstrated the higher performance than the spatio-temporal index methods and the original snapshot method. Especially, for the snapshot clustering method using H-STCA, the more the number of moving objects was increased, the more the performance was improved, as compared to the existing spatio-temporal index methods and the original snapshot method.

• Journal of Food Hygiene and Safety
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• v.25 no.4
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• pp.410-417
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• 2010

Various functional foods, marketing health and functional effects, have been distributed in the market. These products, being in forms of foods, tablets, and capsules, are likely to be mistaken as drugs. In addition, non-experts may sell these as foods, or use these for therapy. Efforts for creating health food regulations or building regulatory system for improving the current status of functional foods have been made, but these have not been communicated to consumers yet. As a result, problems of circulating functional foods for therapy or adding illegal medical to such products have persisted, which has become worse by internet media. The cause of this problem can be categorized into (1) product itself and (2) its use, but in either case, one possible cause is lack of communications with consumers. Potential problems that can be caused by functional foods include illegal substances, hazardous substances, allergic reactions, considerations when administered to patients, drug interactions, ingredients with purity or concentrations too low to be detected, products with metabolic activations, health risks from over- or under-dose of vitamin and minerals, and products with alkaloids. (Journal of Health Science, 56, Supplement (2010)). The reason why side effects related to functional foods have been increasing is that under-qualified functional food companies are exaggerating the functionality for marketing purposes. KFDA has been informing consumers, through its web pages, to address the above mentioned issues related to functional foods, but there still is room for improvement, to promote proper use of functional foods and avoid drug interactions. Specifically, to address these issues, institutionalizing to collect information on approved products and their side effects, settling reevaluation systems, and standardizing preclinical tests and clinical tests are becoming urgent. Also to provide crucial information, unified database systems, seamlessly aggregating heterogeneous data in different domains, with user interfaces enabling effective one-stop search, are crucial.

• Journal of the Korea Computer Graphics Society
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• v.26 no.3
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• pp.99-109
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• 2020

This study was conducted to improve the education method for improving perception awareness of the visually-impaired. 'Hear me later' was designed and implemented based on VR content that allows the visually-impaired experience in the eyes and environment. The main target is from middle and high school students to adolescents in their twenties. It is consisted of a student, the user's daily life with waking up at home in the morning, going to school, taking classes at school, and disembarking home late in the dark. In addition, 10 quests are placed on each map to induce users' participation and activity. These quests are a daily activity for non-disabled people, but it is an activity to experience uncomfortable activity for visually impaired people. In order to verify the effect of 'Hear me later', 8 participants in their early teens to early 20s' perception of visually impaired people was measured through pre and post evaluation of VR contents experience. In order to verify the effect of'Hear me later', 8 participants in their early teens to early 20s' perception of visually impaired people was measured through pre-post evaluation of VR experiences. As a result, it was found that in the post-evaluation of VR contents experience, the perception of the visually impaired was increased by 30% compared to the pre-evaluation. In particular, misunderstandings and changes in prejudice toward the visually impaired were remarkable. Through this study, the possibility of a VR-based disability experience education program that can freely construct space-time and maximize the experience was verified. In addition, it laid the foundation to expand it to various fields of improvement of the disabled.