• Journal of Intelligence and Information Systems
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• v.10 no.2
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• pp.15-37
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• 2004

With the growth in the size of datasets, data mining has recently become an important research topic. Especially, interests about spatio-temporal data mining has been increased which is a method for analyzing massive spatio-temporal data collected from a wide variety of applications like GPS data, trajectory data of surveillance system and earth geographic data. In the former approaches, conventional clustering algorithms are applied as spatio-temporal data mining techniques without any modification. In this paper, we focused to SOM that is the most common clustering algorithm applied to clustering analysis in data mining wet and develop the spatio-temporal data mining module based on it. In addition, we analyzed the clustering results of developed SOM module and compare them with those of K-means and Agglomerative Hierarchical algorithm in the aspects of homogeneity, separation, separation, silhouette width and accuracy. We also developed specialized visualization module fur more accurate interpretation of mining result.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.34C no.5
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• pp.93-102
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• 1997

Conventional expert systems have difficulties in the inference on time-varing situations because they don't have the structure for processing time related informations and rule representation method to describe time explicitely. Some expert systems capable of temporal reasoning are not applicable to the domain in which state changes happen by unpredictble events that cannot be represented by periodic changes of data. In this paper, an event based temporal reasoning method is proposed. It is capable of processing te unpredictable events, representing the knowledge related to event and time, and infering by that knowledge as well as infering by periodically time-varing data. The NEO/temporal, an temporal inference engine, is implemented by applying the proposed temporal reasoning situation assessment and decision supporting system is implemented to show the benefits of the proposed temporal information processing model.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.1
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• pp.139-144
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• 2017

As technologies related to sensor network are currently emerging and the use of GeoSensor is increasing along with the development of IoT technology, spatial query processing systems to efficiently process spatial sensor data are being actively studied. However, existing spatial query processing systems do not support a spatial-temporal data type and a spatial-temporal operator for processing spatial-temporal sensor data. Therefore, they are inadequate for processing spatial-temporal sensor data like GeoSensor. Accordingly, this paper developed a spatial-temporal query processing system, for efficient spatial-temporal query processing of spatial-temporal sensor data in a sensor network. Lastly, this paper verified the utility of System through a scenario, and proved that this system's performance is better than existing systems through performance assessment of performance time and memory usage.

• The KIPS Transactions:PartD
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• v.9D no.4
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• pp.531-542
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• 2002

As most of metadata have been handled on restricted applications, we need a same metadata in order to represent a same video data. However, these metadata make problems that the same video data should be supported by the same metadata. Therefore, in this paper, we extend the Dublin core elements to support the metadata which can solve the problems. The proposed video data representation is managed by the extended metadata of Doblin core model, by using the information of structure, content and manipulation of video data. The thirteen temporal relationship operators are reduced to the six temporal relationship operators by using a dummy shot temporal transformation relationship. The reduced six temporal relationship operators through excluding reverse temporal relationship not only maintain a consistency of representation between a metadata and a video data, but also transform n-ary temporal relationship to binary relationship on shots. We show that the proposed metadata model can be applied to representing and retrieving on various applications as equivalent as the same structure.

• Korean Journal of Remote Sensing
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• v.20 no.3
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• pp.163-173
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• 2004

The objective of this study is to develop a method to generate micro-relief digital elevation model (DEM) data of the tidal mudflats using multi-temporal Landsat Thematic Mapper (TM) data. Field spectroscopy measurements showed that reflectance of the exposed mudflat, shallow turbid water, and normal coastal water varied by TM band wavelength. Two sets of DEM data of the inter-tidal mudflat area were generated by interpolating several waterlines extracted from multi-temporal TM data acquired at different sea levels. The waterline appearing in the near-infrared band was different from the one in the middle-infrared band. It was found that the waterline in TM band 4 image was the boundary between the shallow turbid water and normal coastal water and used as a second contour line having 50cm water depth in the study area. DEM data generated by using both TM bands 4 and 5 rendered more detailed topographic relief as compared to the one made by using TM band 5 alone.

• The KIPS Transactions:PartD
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• v.16D no.2
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• pp.237-248
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• 2009

Recently, based on dynamic location or mobility of moving object, many researches on pattern mining methods actively progress to extract more available patterns from various moving patterns for development of location based services. The performance of moving pattern mining depend on how analyze and process the huge set of spatio-temporal data. Some of traditional spatio-temporal pattern mining methods[1-6,8-11]have proposed to solve these problem, but they did not solve properly to reduce mining execution time and minimize required memory space. Therefore, in this paper, we propose new spatio-temporal pattern mining method which extract the sequential and periodic frequent moving patterns efficiently from the huge set of spatio-temporal moving data. The proposed method reduces mining execution time of $83%{\sim}93%$ rate on frequent moving patterns mining using the moving sequence tree which generated from historical data of moving objects based on hash tree. And also, for minimizing the required memory space, it generalize the detained historical data including spatio-temporal attributes into the real world scope of space and time using spatio-temporal concept hierarchy.

• Journal of the Korean Association of Geographic Information Studies
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• v.7 no.4
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• pp.119-132
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• 2004

As geographic object is consisted of 3-dimensional, it must be appropriated use the 3-dimensional coordinate system to express for exact representation. To define 3-dimensional+temporal object model we extended 3-dimensional space and valid time from object model of OpenGIS consortium that is limited to 2-dimensional space. This methodology guarantees compatibility with other systems to construct with the open model. Also, it permitted administration for two kinds of object by considering both objects whose position and shape changes discretely over time and objects whose position changes continuously. 3-dimensional+temporal object model was implemented by 3-dimensional+temporal object component using OLE/COM techniques. The interfaces of the component defined based on 3-dimensional+temporal object model. To use this component, we implement the data consumer for 3-dimensional+temporal data.

• Journal of Internet Computing and Services
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• v.10 no.5
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• pp.49-63
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• 2009

The performance of spatio-temporal moving pattern mining depends on how to analyze and process the huge set of spatio-temporal data due to the nature of it. The several method was presented in order to solve the problems in which existing spatio-temporal moving pattern mining methods[1-10] have, such as increasing execution time and required memory size during the pattern mining, but they did not solve properly yet. Thus, we proposed the STMP/MST method[11] as a preceding research in order to extract effectively sequential and/or periodical frequent occurrence moving patterns from the huge set of spatio-temporal moving data. The proposed method reduces patterns mining execution time, using the moving sequence tree based on hash tree. And also, to minimize the required memory space, it generalizes detailed historical data including spatio-temporal attributes into the real world scopes of space and time by using spatio-temporal concept hierarchy. In this paper, in order to verify the effectiveness of the STMP/MST method, we compared and analyzed performance with existing spatio-temporal moving pattern mining methods based on the quantity of mining data and minimum support factor.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.455-457
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• 2003

Diverse researches are working moving objects. The most important activities in a moving object database system are query and analysis of spatio -temporal data providing decision-making and problem solving support. Traditional spatial database query language and tools are inappropriate of the real world entities. This paper presents a spatio-temporal query and analysis tool with visual environment. It provides effective, interactive and user-friendly as well as statistic analysis. The moving objects database system stores plentiful moving objects data and performs spatio-temporal and nonspatio-temporal queries.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.7
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• pp.1756-1776
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• 2012

Wireless sensor networks (WSNs) are likely to be more prevalent as their cost-effectiveness improves. The spectrum of applications for WSNs spans multiple domains. In environmental sciences, in particular, they are on the way to become an essential technology for monitoring the natural environment and the dynamic behavior of transient physical phenomena over space. Existing sensor network query processors (SNQPs) have also demonstrated that in-network processing is an effective and efficient means of interaction with WSNs for performing queries over live data. Inspired by these findings, this paper investigates the question as to whether spatio-temporal and historical analysis can be carried over WSNs using distributed query-processing techniques. The emphasis of this work is on the spatial, temporal and historical aspects of sensed data, which are not adequately addressed in existing SNQPs. This paper surveys the novel approaches of storing the data and execution of spatio-temporal and historical queries. We introduce the challenges and opportunities of research in the field of in-network storage and in-network spatio-temporal query processing as well as illustrate the current status of research in this field. We also present new areas where the spatio-temporal and historical query processing can be of significant importance.