• Spatial Information Research
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• v.16 no.2
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• pp.219-236
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• 2008

Data stream collected from sensors contain a large amount of useful information including causality relationships. The causality join query for data stream is to retrieve a set of pairs (cause, effect) from streams of data. A part of causality pairs may however be lost from the query result, due to the delay from sensors to a data stream management system, and the limited size of sliding windows. In this paper, we first investigate spatial, temporal, and spatio-temporal aspects of the causality join query for data stream. Second, we propose several strategies for sliding window management based on these observations. The accuracy of the proposed strategies is studied by intensive experiments, and the result shows that we improve the accuracy of causality join query in data stream from simple FIFO strategy.

• Journal of KIISE:Software and Applications
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• v.35 no.5
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• pp.335-346
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• 2008

Causal relations in ontology should be defined based on the inference types necessary to solve problems specific to application as well as domain. In this paper, we present a model to define and extract causal relations for application ontology for Question-Answering (QA) on fault-diagnosis of electronic devices. Causal categories are defined by analyzing generic patterns of QA application; the relations between concepts in the corpus belonging to the causal categories are defined as causal relations. Instances of casual relations are extracted using lexical patterns in the concept definitions of domain, and extended incrementally with information from thesaurus. On the evaluation by domain specialists, our model shows precision of 92.3% in classification of relations and precision of 80.7% in identifying causal relations at the extraction phase.

• Korean Journal of Logic
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• v.22 no.1
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• pp.183-209
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• 2019

In this paper, I first examine Reiss'(2005) arguments for the causal instrumental variable. Second, I argue that the conditions for causal transitivity I consider meet what the causal instrumental variables and the interveners of the manipulation theory of causation are intended to hold. Reiss shows that two conditions for instrumental variables are not sufficient for causal significance of independent variables for dependent variables. Reiss articulates and reformulates the conditions for instrumental variables in terms of the conditions on causality, while naming his instrumental variables as causal instrumental variables. Reiss argues that the causal instrumental variables are similar to the interveners of the manipulation, or intervention theory of causation. He further argues that the causal instrumental variables do a better job the interveners do. I argue that the conditions for causal transitivity I consider meet the goal the conditions for the causal instrumental variables and the conditions for the interveners both are intended to achieve.

• Korea Trade Review
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• v.42 no.5
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• pp.43-69
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• 2017

The study examined moderating role of thinking style about causality in the relationship between the perceived brand globalness(PBG) and the brand evaluation. Thinking style about causality is divided into interactionism and dispositionism. Individualism culture shows a tendency of thinking style about causality to dispositionism, and collectivist cultures shows a tendency for interactionism. We conducted a survey on Korean college students who represent collectivist cultures and foreign students from countries of individualistic culture and analyzed data through structural equation modeling. Analysis result showed that the higher the respondents perceived PBG, the higher they perceived brand quality, brand quality, and brand price. However, it showed that PBG has greater positive effects on perceived brand quality and perceived brand reputation among Korean students than students from individualistic cultures. On the other hand, the effects of PBG on perceived brand price did not show any significant difference between Korean students and students from individualist cultures. Thus, we can conclude that thinking style about causality plays a moderating role in the relationship between PBG and perceived brand quality and brand reputation, while it does not do so between PBG and perceived brand price.

• Proceedings of the Korean Society for Quality Management Conference
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• 2006.04a
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• pp.161-165
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• 2006

본 연구의 목적은 한국기업을 대상으로 한국 국가품질상(The Korean National Quality Award) 평가도구의 타당성을 검증하고, 이러한 평가 항목들 간에 어떠한 인과관계가 있는지를 Pilot test를 통해 분석하고자 한다. 이를 위해 미국의 말콤볼드리지 국가품질상(The Malcolm Bladrige National Quality Award)의 평가기준인 리더십, 전략계획, 고객과 시장중시, 정보와 분석, 인적자원 중시, 프로세스 관리, 사업성과의 7개 항목과 21개의 세부항목을 이용하였다.

• Journal of the Korean Data and Information Science Society
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• v.24 no.6
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• pp.1189-1197
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• 2013

Data mining is the process of analyzing a huge database from different perspectives and summarizing it into useful information. One of the well-studied problems in data mining is association rule generation. Association rule mining finds the relationship among several items in massive volume database using the interestingness measures such as support, confidence, lift, etc. Typical applications for this technique include retail market basket analysis, item recommendation systems, cross-selling, customer relationship management, etc. But these interestingness measures cannot be used to establish a causality relationship between antecedent and consequent item sets. This paper propose causal association thresholds to compensate for this problem, and then check the three conditions of interestingness measures. The comparative studies with basic and causal association thresholds are shown by numerical example. The results show that causal association thresholds are better than basic association thresholds.

• The KIPS Transactions:PartB
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• v.15B no.2
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• pp.147-158
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• 2008

Fuzzy Cognitive Map (FCM) and Bayesian Belief Network (BBN) are two major frameworks for modeling, representing and reasoning about causal knowledge. Despite their extensive use in causal knowledge engineering, there is no reported work which compares their respective roles. This paper aims to fill the gap by providing a qualitative comparison of the two frameworks through a systematic analysis based on some inherent features of the frameworks. We proposed a set of comparison criteria which covers the entire process of causal knowledge engineering, including modeling, representation, and reasoning. These criteria are usability, expressiveness, reasoning capability, formality, and soundness. The results of comparison have revealed some important facts about the characteristics of FCM and BBN, which will help to determine how FCM and BBN should be used, with respect to each other, in causal knowledge engineering.

• Journal of the Korean Data and Information Science Society
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• v.25 no.4
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• pp.857-868
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• 2014

Data mining is the representative analysis methodology in the era of big data, and is the process to analyze a massive volume database and summarize it into meaningful information. Association rule technique finds the relationship among several items in huge database using the interestingness measures such as support, confidence, lift, etc. But these interestingness measures cannot be used to establish a causality relationship between antecedent and consequent item sets. Moreover, we can not know association direction by them. This paper propose causally confirmed association thresholds to compensate for these problems, and then check the three conditions of interestingness measures. The comparative studies with basic association thresholds, causal association thresholds, and causally confirmed association thresholds are shown by simulation studies. The results show that causally confirmed association thresholds are better than basic and causal association thresholds.

• 한국IT서비스학회:학술대회논문집
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• 2006.11a
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• pp.271-278
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• 2006

IT 조직의 활동에 대한 성과 평가는 과거 재무적인 평가방법이 주를 이루었으나 최근에는 정보시스템 기능과 활동 주체가 다양해짐에 따라 재무적인 접근방법과 함께 새로운 평가 방법 및 기준에 대한 연구가 진행되고 있다. 특히 균형 성과표 (Balanced Scorecard: BSC) 개념을 IT 활동 평가에 적용한 IT-BSC에 대한 연구도 이루어지고 있다. 그러나 정보시스템과 기업전략과의 관계에 대한 규명이나, 다양한 IT 성과측정지표들간의 연계성을 측정하는 연구는 이루어지고 있지 않은 실정이다. 이에 본 본 연구에서는 IT 조직의 성과평가를 위한 핵심성공요인(CSF)과 핵심성과지표(KPI)를 도출하고, 도출된 지표간의 인과관계를 검증하고자 한다. 본 연구의 결과로 도출된 영역과 지표들은 각 산업영역에서 IT 조직의 성과를 측정할 때 참고하고 적용할 수 있는 효과적인 지침으로 활용될 수 있다는 데에 연구의 의미를 둘 수 있다.

• Proceedings of the Korea Society of Information Technology Applications Conference
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• 2007.05a
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• pp.190-197
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• 2007

외환위기 이후 우리나라 금융기관은 상대적으로 위험성이 높은 기업대출보다, 높은 수익성을 가지는 가계 대출에 관심을 기울이게 되었다. 가계대출이 증가함에 따라 개인신용평가의 중요성이 부각되고, 이에 많은 신용평가시스템이 개발되어 왔다. 하지만 기존의 신용평가시스템은 대출 신청 당시의 데이터 및 과거의 데이터를 가지고 개인의 신용을 평가하기 때문에, 미래 상황에 대한 예측은 고려하지 못한다. 시스템 다이나믹스는 시간의 흐름에 따른 각 요인의 변화를 살펴봄으로써 미래 상황에 대한 예측이 가능한 분석 방법이다. 이에 본 연구에서는 시스템 다이나믹스 방법론을 활용하여 개인 신용 상태에 대한 미래의 동태적인 변화를 예측하여, 그 결과를 반영한 신용평가모델을 개발하고자 한다. 이를 위하여, 먼저 신용평점 영향을 주는 변수들을 선정하고, 이 변수들 간의 인과관계를 밝혀낸 후, 인과관계를 토대로 분석 모델을 구축한 뒤, 컴퓨터 시뮬레이션을 실행함으로써, 대출 희망자의 미래의 신용상태 변화 모양을 예측해 본다. 이러한 시뮬레이션 결과를 신용평가에 반영하게 되면, 금융기관의 신용 대출의 위험을 줄이는 데 기여할 것으로 기대된다.