• Proceedings of the Korea Information Processing Society Conference
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• 2010.11a
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• pp.1402-1405
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• 2010

본 논문에서는 시간 및 공간 마이닝 기술을 적용한 다이렉트 마케팅 지원 시스템 프로토타입을 개발하였다. 개발한 프로토타입은 서울시를 대상으로 약 인구 500명 크기의 블록단위 e-Commerce 구매 패턴과 유사블록 그룹핑 및 기타 마케팅에 유용한 외부 공개 자료의 검색 기능을 포함한다. 또한, 마케팅 캠페인에 프로토타입의 활용도를 높이기 위해서 상품선호도 기반 검색, 라이프스타일 기반 검색 및 복합정보 기반 검색 모듈 등의 다양한 서비스를 제공한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.04a
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• pp.3-6
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• 2009

변압기 무선부하감시 시스템에서 30분 간격으로 계측된 부하 데이터와 GIS-AMR 데이터웨어하우스로부터 변압기 속성 및 공간적 특징을 추출하여 정확한 변압기의 부하 패턴을 예측하기 위한 시공간 패턴 마이닝 기법을 적용하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2008.11a
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• pp.249-252
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• 2008

환자 이력, 구매자 이력, 웹 로그 이력 데이터에 대한 시간 데이터 마이닝에 대한 연구에서 시간 간격 관계 규칙을 찾아내는 것은 가변적인 시간 간격의 데이터를 하나의 이벤트로 요약하는 것은 합리적이지 못하다. 이는 그 이벤트가 가변적인 시간 간격 내에서 서로 독립적인 이벤트일 수 있기 때문이다. 그러므로 이벤트들의 시퀀스를 독립적인 서브 시퀀스로 나누어 각 서브 시퀀스별로 시간 간격을 갖는 인터벌 이벤트로 요약하는 것이 합리적이다. 본 논문은 이벤트 시퀀스를 시간 간격을 갖는 인터벌 이벤트로 요약하고 요약된 인터벌 이벤트들로부터 인터벌 관계 규칙을 찾아내는 새로운 시간 데이터 마이닝 기법을 제안하고 있다. 이 기법은 인터벌 관계들 사이의 규칙을 찾아줌으로서 기존의 데이터 마이닝 기법과 비교하여 질적으로 우수한 지식을 제공한다.

• Journal of the Economic Geographical Society of Korea
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• v.10 no.4
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• pp.427-443
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• 2007

The purpose of this study is to investigate spacio-temporal structures of traffic flows on the subway network in the Metropolitan Seoul, and the relationships between topological structures of traffic flows and land use patterns. In particular we analyze in the topological structures of traffic flows on the subway network in time dimension as well as in spatial dimension. For the purpose, this study utilizes data mining techniques to the one day T-card transaction data of the last four years, which has developed for exploring the characteristics of traffic flows from large scale trip-transaction databases. The topological structures of traffic flows on the subway network has changed considerably during the last four years. The volumes of traffic flows, the travel time and stops per trip have increased until 2006 and decreased again in 2007. The results are visualized by utilizing GIS and analyzed, and thus the spatial patterns of traffic flows are analyzed. The spatial distribution patterns of trip origins and destinations show substantial differences among time zones during a day. We analyze the relationships between traffic flows at subway stops and the geographical variables reflecting land use around them. We obtain 6 log-linear functions from stepwise multiple regression analysis. We test multicollinearity among the variables and autocollelation for the residuals.

• Ocean and Polar Research
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• v.36 no.4
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• pp.423-435
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• 2014

Verifying the similarity of environmental characteristics between an artificial impact site and a preserved or reference site is necessary to quantitatively and qualitatively evaluate the environmental impact of mining activity. Although an impact site (BIS station) and a preserved site (called KOMO station) that have been selected in the Korea manganese nodule contract area may share similar environmental characteristics, similarities in terms of the water column environment between both sites has not been investigated. In this study, we compared the chemical properties of the water columns and sinking particle fluxes between BIS and KOMO stations through two observations (August 2011 and September 2012). Additionally, we observed particle fluxes at the KOMO station for five years (July 2003~July 2008) to understand long-term natural variability. Vertical distributions of water column properties such as dissolved oxygen, inorganic nutrients (N, P, Si), total organic carbon below surface layer (within the depth range of 200 m) were not considerably different between the two sites. Especially, values of water column parameters in the abyssopelagic zone from 4000 m to bottom layer (~5000 m) were very similar between the BIS and KOMO sites. Sinking particle fluxes from the two sites also showed similar seasonality. However, natural variation of particle flux at the KOMO site varied from 3.5 to $129.9mg\;m^{-2}day^{-1}$, with a distinct temporal variation originating from ENSO events (almost forty times higher than a minimum value). These results could provide valuable information to more exactly evaluate the environmental impact of mining activity on water columns.

• Economic and Environmental Geology
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• v.37 no.5
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• pp.509-519
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• 2004

The ground subsidence that occurred in the abandoned coal mining area, Gaeun, Korea, was observed using 25 JERS-1 SAR interferograms from November 1992 to October 1998. We carried out measurements on a subset of image pixels corresponding to point-wise stable reflectors(PS: permanent scatterer) by exploiting a long temporal series of interferometric phases and compared it with the distribution map of in situ examined crack level. PSs could be identified by means of amplitude dispersion index and coherence of the interferograms and the density of PS was much higher in an urban area than in a mountainous region. The measured subsidence rate represented the average velocity in a period of image acquisition and excluded complex nonlinear displacements such as an abrupt collapse. The mean line-of-sight velocity in the study area is 0.19cm/yr and the estimation error is 0.18cm/yr. The center of the abandoned Gaeun coal mine(0.49cm/yr) and the area opposite Gaeun station(1.66cm/yr) were observed as the most highly subsiding areas.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.1
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• pp.451-458
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• 2011

In order to provide the optimal location based services such as the optimal moving path search or the scheduling pattern prediction, the extraction of significant moving pattern which is considered the temporal and spatial properties of the location-based historical data of the moving objects is essential. In this paper, for the extraction of significant moving pattern we propose the location generalization method which translates the location attributes of moving object into the spatial scope information based on $R^*$-tree for more efficient patterning the continuous changes of the location of moving objects and for indexing to the 2-dimensional spatial scope. The proposed method generates the moving sequences which is satisfied the constraints of the time interval between the spatial scopes using the generalized spatial data, and extracts the significant moving patterns using them. And it can be an efficient method for the temporal pattern mining or the analysis of moving transition of the moving objects to provide the optimal location based services.

• Journal of the Korean Association of Geographic Information Studies
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• v.2 no.3
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• pp.46-59
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• 1999

Remote sensing provides informations on the changes of the hydrological states and variables over with the temporal and spatial distribution to monitor hydrological conditions and changes for large area. Especially, it can extract a spatial distribution of hydrological parameters such as surface albedo, vegetation informations, and surface temperature to effectively manage water resources of the watershed. In this study, we analyzed the characteristic of temporal and spatial changes in surface hydrological parameters which is necessary to identify the spatial distribution of water resources. 5 Landsat TM data of 1995 which is collected for Bochong-chon watershed, located in the upper stream of Keum River, were used to estimate characteristics on the change of hydrological parameters and atmospheric correction was carried out using COST model. The study showed that the difference of the albedo by the land cover was very sensitive depending upon the change of sun elevation and the amount of water in the soil. The difference between the surface temperature analysis and the measured air temperature was from $2.5^{\circ}C$ to $3.86^{\circ}C$.

• Smart Media Journal
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• v.6 no.3
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• pp.41-48
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• 2017

Event detection using social media has been widespread since social network services have been an active communication channel for connecting with others, diffusing news message. Especially, the real-time characteristic of social media has created the opportunity for supporting for real-time applications/systems. Social network such as Twitter is the potential data source to explore useful information by mining messages posted by the user community. This paper proposed a novel system for temporal event detection by analyzing social data. As a result, this information can be used by first responders, decision makers, or news agents to gain insight of the situation. The proposed approach takes advantages of deep learning methods that play core techniques on the main tasks including informative data identifying from a noisy environment and temporal event detection. The former is the responsibility of Convolutional Neural Network model trained from labeled Twitter data. The latter is for event detection supported by Recurrent Neural Network module. We demonstrated our approach and experimental results on the case study of earthquake situations. Our system is more adaptive than other systems used traditional methods since deep learning enables to extract the features of data without spending lots of time constructing feature by hand. This benefit makes our approach adaptive to extend to a new context of practice. Moreover, the proposed system promised to respond to acceptable delay within several minutes that will helpful mean for supporting news channel agents or belief plan in case of disaster events.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.8
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• pp.521-529
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• 2020

Forest fires are one of the most important environmental risks that have adverse effects on many aspects of life, such as the economy, environment, and health. The early detection, quick prediction, and rapid response of forest fires can play an essential role in saving property and life from forest fire risks. For the rapid discovery of forest fires, there is a method using meteorological data obtained from local sensors installed in each area by the Meteorological Agency. Meteorological conditions (e.g., temperature, wind) influence forest fires. This study evaluated a Data Mining (DM) approach to predict the burned area of forest fires. Five DM models, e.g., Stochastic Gradient Descent (SGD), Support Vector Machines (SVM), Decision Tree (DT), Random Forests (RF), and Deep Neural Network (DNN), and four feature selection setups (using spatial, temporal, and weather attributes), were tested on recent real-world data collected from Gyeonggi-do area over the last five years. As a result of the experiment, a DNN model using only meteorological data showed the best performance. The proposed model was more effective in predicting the burned area of small forest fires, which are more frequent. This knowledge derived from the proposed prediction model is particularly useful for improving firefighting resource management.