• 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 Korea Spatial Information System Society
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• v.10 no.2
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• pp.63-79
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• 2008

With the recent development of advanced GIS and complex spatial analysis technologies, the more sophisticated technologies are being required to support the advanced knowledge for solving geographical or spatial problems in various decision support systems. In addition, necessity for research on scientific crime investigation and forensic science is increasing particularly at law enforcement agencies and investigation institutions for efficient investigation and the prevention of crimes. There are active researches on geographic profiling to predict the base location such as criminals' residence by analyzing the spatial patterns of serial crimes. However, as previous researches on geographic profiling use simply statistical methods for spatial pattern analysis and do not apply a variety of spatial and temporal analysis technologies on serial crimes, they have the low prediction accuracy. Therefore, this paper identifies the typology the spatio-temporal patterns of serial crimes according to spatial distribution of crime sites and temporal distribution on occurrence of crimes and proposes STA-BLP(Spatio-Temporal Analysis based Base Location Prediction) algorithm which predicts the base location of serial crimes more accurately based on the patterns. STA-BLP improves the prediction accuracy by considering of the anisotropic pattern of serial crimes committed by criminals who prefer specific directions on a crime trip and the learning effect of criminals through repeated movement along the same route. In addition, it can predict base location more accurately in the serial crimes from multiple bases with the local prediction for some crime sites included in a cluster and the global prediction for all crime sites. Through a variety of experiments, we proved the superiority of the STA-BLP by comparing it with previous algorithms in terms of prediction accuracy.

• Journal of the Korean Geographical Society
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• v.40 no.3 s.108
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• pp.310-320
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• 2005

This study attempts at prototyping and evaluating a geovisualization tool that summarizes and explores human activity patterns using spatio-temporal trajectory data collected from GPS receiver. A set of core conceptualization developed in 'time geography' is successfully represented by our prototype based on the notion of 'space-time cube.' The notions of 'temporal dispersion cylinder' and 'parallel plane plot' are also implemented to allow funker analyses of human activity pattern on the space-time trajectory. The capabilities of the geovisualization environment we proposed include the interactive and dynamic functions that support a variety of explorations on the three components of spatio-temporal data : space(where), time(when), and object(what).

• Journal of KIISE
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• v.43 no.9
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• pp.963-973
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• 2016

Providing a semantic knowledge system using media ontologies requires not only conventional axiom reasoning but also knowledge extension based on various types of reasoning. In particular, spatio-temporal information can be used in a variety of artificial intelligence applications and the importance of spatio-temporal reasoning and expression is continuously increasing. In this paper, we append the LOD data related to the public address system to large-scale media ontologies in order to utilize spatial inference in reasoning. We propose an RDFS/Spatial inference system by utilizing distributed memory-based framework for reasoning about large-scale ontologies annotated with spatial information. In addition, we describe a distributed spatio-temporal SPARQL parallel query processing method designed for large scale ontology data annotated with spatio-temporal information. In order to evaluate the performance of our system, we conducted experiments using LUBM and BSBM data sets for ontology reasoning and query processing benchmark.

• Journal of Biomedical Engineering Research
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• v.28 no.3
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• pp.417-422
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• 2007

In this study, we investigated spatio-temporal eye-hand coordination by the "Global effect paradigm", a target-distracter configuration Global effect is observed a variety of visual tasks, for example, during rapid automatic tracking, scanning for target detail, and the comparison of target configurations. Global effect used to investigate eye-hand coordination with a task that presents a target close to a distracter. Eight young subjects participated in the experiments. The experiments consist of using a distracter less eccentric than the targe1 and using a distracter more eccentric than target. Each experiment consist of tasks which are making reference, pointing the target with watching visual stimulation(saccade) and pointing the target. The subject's task was to move towards the target as quickly as possible. We measured the eye movement by using EOG and the hand movement by using NDI Optotrak system. This study shows that eye and hand movement exists for global effect paradigm in spatio-temporal coordination.

• KIPS Transactions on Computer and Communication Systems
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• v.7 no.6
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• pp.137-144
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• 2018

Vehicular Ad-hoc Networks (VANETS) have enabled to provide a variety of applications such as accident notification, content usage, etc. These applications have spatio-temporal data which have an interesting region and a lifetime according to their properties. However, geocasting protocols to deliver data to an interesting region can provide data to all vehicles in the region through a single transmission only at the current time, but cannot provide data to vehicles passing through the region during the lifetime of the data. Thus, we propose a spatio-temporal geocasting protocol called STGP using a regional caching scheme to send data to vehicles in an interesting region during a data lifetime in VANETs. For efficient and reliable regional caching, the proposed protocol uses the beacon-based data sharing, the extra caching elimination, and the distance-based caching exchange. Simulation results verify that the proposed protocol achieves more reliable and efficient data delivery compared with the existing protocol.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.10
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• pp.473-478
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• 2015

Recently, automatic video monitoring of individual pigs is emerging as an important issue in the management of group-housed pigs. Although a rich variety of studies have been reported on video monitoring techniques in intensive pig farming, it still requires further elaboration. In particular, when there exist adjoining pigs in a crowd pig room, it is necessary to have a way of separating adjoining pigs from the perspective of an image processing technique. In this paper, we propose an efficient image segmentation solution using both spatio-temporal information and region growing method for the identification of individual pigs in video surveillance systems. The experimental results with the videos obtained from a pig farm located in Sejong illustrated the efficiency of the proposed method.

• Journal of The Korean Society of Agricultural Engineers
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• v.49 no.6
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• pp.65-76
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• 2007

Studies in landscape ecology have emphasized on the relationship between landscape patterns and shapes. A variety of landscape metrics has been developed so far to quantify landscape structures. Therefore, their developments and widespread applications become possible with the advent of spatial information systems including geographic information systems(GIS) and remote sensing. This study is to grasp the change of land use and landscape ecology indices, and to analyze the change of landscape structure in a part of Gyeonggi-do during 15 years from 1985 to 2000. Green-area distribution maps and agricultural-area distribution maps for the analysis were reconstructed from land cover maps constructed by WAMIS(Water Management Information System). And then, 4 landscape ecology indices(TA, LPI, SHAPE_AM, CAI_MN) for the green-area and 5 landscape ecology indices(TA, PD, LPI, LSI, CAI_MN) for the agricultural-area were selected by using pearson correlation analysis. According to the spatio-temporal change analysis using landscape ecology indices, the green-area fragmentation of Yongin was the most severe of the study area and the agricultural-area fragmentation of Gwangju and Namyangju was more severe than any other regions.

• The Journal of the Korea Contents Association
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• v.12 no.4
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• pp.31-39
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• 2012

DVC(Distributed Video Coding) techniques have been attracting a lot of research works since these enable us to implement the light-weight video encoder and to provide good coding efficiency by introducing the feedback channel. However, the feedback channel causes the decoder to increase the decoding complexity and requires very high decoding latency because of numerous iterative decoding processes. So, in order to reduce the decoding delay and then to implement in a real-time environment, this paper proposes several parity bit estimation methods which are based on the temporal correlation, spatial correlation and spatio-temporal correlations between LDPCA frames on each bit plane in the consecutive video frames in pixel-domain Wyner-Ziv video coding scheme and then the performances of these methods are compared in fast DVC scheme. Through computer simulations, it is shown that the adaptive spatio-temporal correlation-based estimation method and the temporal correlation-based estimation method outperform others for the video frames with the highly active contents and the low active contents, respectively. By using these results, the proposed estimation schemes will be able to be effectively used in a variety of different applications.

• Journal of KIISE:Computing Practices and Letters
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• v.5 no.5
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• pp.553-563
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• 1999

The Spatial Database Engine(SDETM) developed by Environmental Systems Research Institute, Inc. is a spatial database that employs a client-server architecture incorporated with a set of software services to perform efficient spatial operations and to manage large, shared and geographic data sets. It currently supports a wide variety of spatial search methods and spatial relationships determined dynamically. Spatial objects in the space world can be changed by either non-spatial operations or spatial operations. Conventional geographical information systems(GISs) did not manage their historical information, however, because they handle the snapshot images of spatial objects in the world. In this paper we propose a spatio-temporal data model and an algorithm for temporal relationship macro which is able to manage and retrieve the historical information of spatial objects. The proposed spatio-temporal data model and its operations can be used as a software tool for history management of time-varying objects in database without any change.