• Journal of KIISE:Databases
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• v.34 no.3
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• pp.179-192
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• 2007

This paper addresses an approach that recommends investment types for stock investors by discovering useful rules from past changing patterns of stock prices in databases. First, we define a new rule model for recommending stock investment types. For a frequent pattern of stock prices, if its subsequent stock prices are matched to a condition of an investor, the model recommends a corresponding investment type for this stock. The frequent pattern is regarded as a rule head, and the subsequent part a rule body. We observed that the conditions on rule bodies are quite different depending on dispositions of investors while rule heads are independent of characteristics of investors in most cases. With this observation, we propose a new method that discovers and stores only the rule heads rather than the whole rules in a rule discovery process. This allows investors to define various conditions on rule bodies flexibly, and also improves the performance of a rule discovery process by reducing the number of rules. For efficient discovery and matching of rules, we propose methods for discovering frequent patterns, constructing a frequent pattern base, and indexing them. We also suggest a method that finds the rules matched to a query issued by an investor from a frequent pattern base, and a method that recommends an investment type using the rules. Finally, we verify the superiority of our approach via various experiments using real-life stock data.

• Management & Information Systems Review
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• v.33 no.3
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• pp.93-104
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• 2014

This study investigates price discovery between BlueNext spot and futures in EU ETS carbon emission markets using vector error correction model, GG and Hasbruck information ratio. Especially EUA is European Union Allowances traded on the Emissions Trading Scheme. This emission asset attracts and increasing attention among operators, investors and brokers on emission markets. In this study, we found BlueNext spot and EUA futures market are cointegrated. Following the preceding studies, we judged that EUA futures market contribute to the price discovery process than BlueNext spot market when this GG and Hasbrouck information ratio for BlueNext market are larger than 0.5. In other words, the futures market of EUA plays a more dominant role in price discovery than the spot market.

• Journal of The Korean Association For Science Education
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• v.33 no.1
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• pp.30-45
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• 2013

In this study, we developed a model for analyzing scientists' creative thinking processes, and analyzed Archimedes' thinking process in solving the golden crown problem. As results show, scientists' complex problem solving processes could be represented as a repeating circular model, and the fusion of processes of diverse thinking required for scientists' creativity could be analyzed from the case. Also in this study, we represented the role of experiments in scientists' creative discovery, and investigated the reasons for the difference between the viewpoints of textbooks and historic facts. We found the importance of abductive reasoning and advance knowledge in creative thinking. Archimedes solved the golden crown problem creatively by crossing the scientific thought of dynamics and the daily thought of baths. In this process, abductive reasoning and advance knowledge played an important role. Besides Archimedes' case, if we would reconstruct the creative discovery processes of diverse scientists' in textbooks, students could raise their creative thinking ability by experiencing these processes as educational steps.

• Journal of Korean Academy of Nursing
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• v.36 no.4
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• pp.652-661
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• 2006

Purpose. The purposes of this study were to apply data mining tool to nursing specific knowledge discovery process and to identify the utilization of data mining skill for clinical decision making. Methods. Data mining based on rough set model was conducted on a large clinical data set containing NMDS elements. Randomized 1000 patient data were selected from year 1998 database which had at least one of the five most frequently used nursing diagnoses. Patient characteristics and care service characteristics including nursing diagnoses, interventions and outcomes were analyzed to derive the meaningful decision rules. Results. Number of comorbidity, marital status, nursing diagnosis related to risk for infection and nursing intervention related to infection protection, and discharge status were the predictors that could determine the length of stay. Four variables (age, impaired skin integrity, pain, and discharge status) were identified as valuable predictors for nursing outcome, relived pain. Five variables (age, pain, potential for infection, marital status, and primary disease) were identified as important predictors for mortality. Conclusions. This study demonstrated the utilization of data mining method through a large data set with stan dardized language format to identify the contribution of nursing care to patient's health.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.2
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• pp.691-708
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• 2014

This paper formalizes a special type of social networking knowledge, which is called "workflow performer-role affiliation networking knowledge." A workflow model specifies execution sequences of the associated activities and their affiliated relationships with roles, performers, invoked-applications, and relevant data. In Particular, these affiliated relationships exhibit a stream of organizational work-sharing knowledge and utilize business process intelligence to explore resources allotting and planning knowledge concealed in the corresponding workflow model. In this paper, we particularly focus on the performer-role affiliation relationships and their implications as organizational and business process intelligence in workflow-driven organizations. We elaborate a series of theoretical formalisms and practical implementation for modeling, discovering, and visualizing workflow performer-role affiliation networking knowledge, and practical details as workflow performer-role affiliation knowledge representation, discovery, and visualization techniques. These theoretical concepts and practical algorithms are based upon information control net methodology for formally describing workflow models, and the affiliated knowledge eventually represents the various degrees of involvements and participations between a group of performers and a group of roles in a corresponding workflow model. Finally, we summarily describe the implications of the proposed affiliation networking knowledge as business process intelligence, and how worthwhile it is in discovering and visualizing the knowledge in workflow-driven organizations and enterprises that produce massively parallel interactions and large-scaled operational data collections through deploying and enacting massively parallel and large-scale workflow models.

• Journal of Information Technology Applications and Management
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• v.13 no.4
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• pp.121-140
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• 2006

UDDI (Universal Discovery Description and Integration) Registry is used for Web Services registration and search. UDDI offers the search result to the keyword-based query. UDDI supports WSDL registration but it does not supports WSDL search. So it is required that contents based search and ranking using name and description in UDDI registration information and WSDL. This paper proposes a retrieval engine considering contents of services registered in the UDDI and WSDL. It uses Vector Space Model for similarity comparison between contents of those. UDDI registry information hierarchy and WSDL hierarchy are considered during searching process. This engine suppports two discovery methods. One is Keyword-based search and the other is template-based search supporting ranking for user's query. Template-based search offers how service interfaces correspond to the query for WSDL documents. Proposed retrieval engine can offer search result more accurately than one which UDDI offers and it can retrieve WSDL which is registered in UDDI in detail.

• Environmental and Resource Economics Review
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• v.21 no.3
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• pp.493-517
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• 2012

This paper analyzes a price discovery process for gasoline among branded and independent stations in Korea using a vector error correction model (VECM) and directed acyclic graphs (DAG). Two data sets for daily prices of medium level gasoline running from April 15, 2008 to May 31, 2009 and from January 1, 2011 to December 31, 2011 are used for empirical analysis. Empirical results show that S-OIL has an exogeneity and played a important role in the flow of price information in the market in the first period. In the second period, SK energy played a key role in price discovery process in the market. The price of NH-OIL stations do not cause the price of any other stations, which implies that the entrance of new branded stations with lower gasoline price to market has no influence on gasoline prices of retail markets.

• BMB Reports
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• v.42 no.2
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• pp.65-71
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• 2009

Autophagy, which is a process of self eating, has gained interest in the past decade due to its both beneficial and controversial roles in various biological phenomena. The discovery of autophagy genes (ATG) in yeast has led to focused research designed to elucidate the mechanism and regulation of this process. The role of autophagy in a variety of biological phenomena, including human disease, is still the subject of debate. However, recent findings suggest that autophagy is a highly regulated process with both beneficial and negative effects. Indeed, studies conducted using various model organisms have demonstrated that increased autophagy leads to an extended lifespan. Despite these findings, it is still unknown if all pathways leading to extended lifespan converge at the process of autophagy or not. Here, an overview of modern developments related to the process of autophagy, its regulation and the molecular machinery involved is presented. In addition, this review focuses on one of the beneficial aspects of autophagy, its role in lifespan regulation.

• Journal of The Korean Association For Science Education
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• v.30 no.5
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• pp.557-575
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• 2010

The purpose of the study was to suggest the characteristics and goals of the science teaching model for use as criteria in selecting the appropriate teaching model for science in secondary schools. These characteristics and the goals have been organized based on the analyses of the literature on the teaching and/or instructional model. The teaching models have been classified into four areas, and the characteristics and goals of each area have been summarized as follows: $\cdot$ Traditional models: teaching of scientific knowledge through lectures, acquisition of scientific knowledge through discovery, acquisition of inquiry process skills through inquiry-based teaching/learning $\cdot$ Transitional models: demonstration and discovery as teaching strategies, acquisition of inquiry process skills through inquiry approach, acquisition and change of scientific knowledge $\cdot$ Modernistic model - conceptual change models: differentiation of scientific knowledge, exchange of misconceptions for scientific concepts - learning cycle models: conceptual differentiation, exchange of misconceptions, acquisition of science process skills Also described in this paper are the model's characteristics and goals that can be used as the criteria for selecting the appropriate teaching model for the subject that will be taught.

• Journal of the Korean Society for information Management
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• v.31 no.1
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• pp.231-250
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• 2014

Due to the recent development of Information and Communication Technologies (ICT), the amount of research publications has increased exponentially. In response to this rapid growth, the demand of automated text processing methods has risen to deal with massive amount of text data. Biomedical text mining discovering hidden biological meanings and treatments from biomedical literatures becomes a pivotal methodology and it helps medical disciplines reduce the time and cost. Many researchers have conducted literature-based discovery studies to generate new hypotheses. However, existing approaches either require intensive manual process of during the procedures or a semi-automatic procedure to find and select biomedical entities. In addition, they had limitations of showing one dimension that is, the cause-and-effect relationship between two concepts. Thus;this study proposed a novel approach to discover various relationships among source and target concepts and their intermediate concepts by expanding intermediate concepts to multi-levels. This study provided distinct perspectives for literature-based discovery by not only discovering the meaningful relationship among concepts in biomedical literature through graph-based path interference but also being able to generate feasible new hypotheses.