• Economic and Environmental Geology
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• v.38 no.2 s.171
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• pp.101-111
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• 2005

The objectives of this study we to assess the extent and degree of environmental contamination and to draw general conclusions on the fate of toxic elements derived from mining activities in Korea. 인t abandoned mines with four base-metal mines and four Au-Ag mines were selected and the results of environmental surveys in those areas were discussed. In the base-metal mining areas, the Sambo Pb-Zn-barite, the Shinyemi Pb-Zn-Fe, the Geodo Cu-Fe and the Shiheung Cu-Pb-Zn mine, significant levels of Cd, Cu, Pb and Zn were found in mine dump soils developed over mine waste materials, tailings and slag. Furthermore, agricultural soils, stream sediments and stream water near the mines were severely contaminated by the metals mainly due to the continuing dispersion downstream and downslope from the sites, which was controlled by the feature of geography, prevailing wind directions and the distance from the mine. In e Au-Ag mining areas, the Kubong, the Samkwang, the Keumwang and the Kilkok mines, elevated levels of As, Cd, Cu, Pb and Zn were found in tailings and mine dump soils. These levels may have caused increased concentrations of those elements in stream sediments and waters due to direct dis-charge downstream from tailings and mine dumps. In the Au-Ag mines, As would be the most characteristic contaminant in the nearby environment. Arsenic and heavy metals were found to be mainly associated with sulfide gangue minerals, and mobility of these metals would be enhanced by the effect of oxidation. According to sequential extraction of metals in soils, most heavy metals were identified as non-residual chemical forms, and those are very susceptible to the change of ambient conditions of a nearby environment. As application of pollution index (PI), giving data on multi-element contamination in soils, over 1.0 value of the PI was found in soils sampled at and around the mining areas.

• Journal of Wetlands Research
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• v.10 no.2
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• pp.39-51
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• 2008

In recent days, the cases of using wetlands in treating waste water, storm events, mining leachate, and agriculture effluents are increasing. But there is the lack of the data for wetlands because of the difficulty in long term monitoring. Such an aspect makes the proper use of wetland impractical. In this study for the purpose of generating a long term hydrologic data, the time series of storage amount for Upo, Mokpo, Sajipo, and Jjokjibeol in Topyeong watershed is simulated using SWAT model. Based on the SWAT-Topyeong model involved in several scenarios for constructing new washlands in Topyeong watershed, the temporal behavior of new washlands is analyzed. It is also revealed that the constructed washland can affect the Upo in some degrees.

• The KIPS Transactions:PartB
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• v.11B no.4
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• pp.465-470
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• 2004

Web Information Predictive Systems have the restriction such as they need users profiles and visible feedback information for obtaining the necessary information. For overcoming this restrict, this study designed and implemented Dynamic Web Information Predictive System using Ensemble Support Vector Machine to be able to predict the web information and provide the relevant information every user needs most by click stream data and user feedback information, which have some clues based on the data. The result of performance test using Dynamic Web Information Predictive System using Ensemble Support Vector Machine against the existing Web Information Predictive System has preyed that this study s method is an excellence solution.

• Journal of Internet Computing and Services
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• v.20 no.1
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• pp.87-96
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• 2019

In this paper, we propose a MapReduce-supported clustering technique for collecting and classifying distributed workflow enactment event logs as a preprocessing tool. Especially, we would call the distributed workflow enactment event logs as Workflow BIG-Logs, because they are satisfied with as well as well-fitted to the 5V properties of BIG-Data like Volume, Velocity, Variety, Veracity and Value. The clustering technique we develop in this paper is intentionally devised for the preprocessing phase of a specific workflow process mining and analysis algorithm based upon the workflow BIG-Logs. In other words, It uses the Map-Reduce framework as a Workflow BIG-Logs processing platform, it supports the IEEE XES standard data format, and it is eventually dedicated for the preprocessing phase of the ${\rho}$-Algorithm that is a typical workflow process mining algorithm based on the structured information control nets. More precisely, The Workflow BIG-Logs can be classified into two types: of activity-based clustering patterns and performer-based clustering patterns, and we try to implement an activity-based clustering pattern algorithm based upon the Map-Reduce framework. Finally, we try to verify the proposed clustering technique by carrying out an experimental study on the workflow enactment event log dataset released by the BPI Challenges.

• The KIPS Transactions:PartD
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• v.10D no.2
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• pp.283-292
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• 2003

One-dimensional time-series data have been studied in various database applications such as data mining and data warehousing. However, in the current complex business environment, multidimensional data sequences (MDS') become increasingly important in addition to one-dimensional time-series data. For example, a video stream can be modeled as an MDS in the multidimensional space with respect to color and texture attributes. In this paper, we propose the effective similarity measures on which the similar pattern retrieval is based. An MDS is partitioned into segments, each of which is represented by various geometric and semantic features. The similarity measures are defined on the basis of these segments. Using the measures, irrelevant segments are pruned from a database with respect to a given query. Both data sequences and query sequences are partitioned into segments, and the query processing is based upon the comparison of the features between data and query segments, instead of scanning all data elements of entire sequences.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.4
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• pp.521-528
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• 2016

Web-based services are used widely in many computer application fields due to the increasing use of PCs and mobile devices. Accordingly, topics on the analysis of access logs generated in the application fields have been researched actively to support personalized services in the field, and analyzing techniques based on the weight differentiation of information in access logs have been proposed. This paper outlines an analysis technique for web-click streams, which is useful for finding high utility old item sets in web-click streams, whose data elements are generated at a rapid rate. Using the technique, interesting information can be found, which is difficult to find in conventional techniques for analyzing web-click streams and is used effectively in target marketing. The proposed technique can be adapted widely to analyzing the data generated in a range of computing application fields, such as IoT environments, bio-informatics, etc., which generated data as a form of data streams.

• Economic and Environmental Geology
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• v.31 no.6
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• pp.499-508
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• 1998

The Imgok Creek is located in the Gangreung coal field, which has been known that sulfides are more abundant than other coal fields in Korea, and it has been severly contaminated by acid mine drainage (AMD) discharging from the abandoned coal mines, such as the Youngdong, the Dongduk and the Waryong coal mines. The purposes of this study are to synthetically assess the contamination of natural water, stream sediment and cultivated soils, and to provide the basic data for AMD treatment. Geochemical samples were collected in December, 1996 (dry season) and April, 1997 (after three day's rainfall). TDS of the Youngdong mine water was remarkably higher than those of other mine waters. In the Imgok Creek, concentrations of most elements, except Fe decreased with distance by dilution caused by the inflow of uncontaminated tributaries. From the results of NAMDI and $I_{geo}$ calculation, the Youngdong coal mine was the main contamination source of the study area. Groundwater pollution was not yet confirmed in this study and the paddy and farm land soils were also not yet contaminated by mining activity based on the pollution index ranging from 0.27 to 0.47.

• Journal of the Korean Society of Hazard Mitigation
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• v.8 no.1
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• pp.103-108
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• 2008

Riverbed change is greatly influenced by artificial factors such as dam construction, gravel collection, and river improvement. This study simulated a long-term bed change based on the GSTARS3 model using actual data from the area downstream of the Geum River Daecheong Dam and compared the estimation with a section of the actual measurement. As a result, it was found that the section of the actual measurement was far lower than the result of the simulation in terms of long-term bed change. While the area downstream of Daecheong Dam displayed approximately an average of 2.29 m of streambed degradation on average while the upper stream area showed approximately 0.63 m of bed degradation over 24 years. In the simulation of the area downstream of Daecheong Dam based on the GSTARS3 model, similar bed degradation was observed. However, a great difference was detected between the result and the actual measurement. According to the cause analysis, the riverbed in the area downstream of Daecheong Dam has continuously degraded due to the dam construction and mass collection of gravel. The mass collection of gravel was the main cause of riverbed change. It was found that about 76% of all riverbed degradation was caused by the mass collection of gravel.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.85-107
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• 2019

Online consumers browse products belonging to a particular product line or brand for purchase, or simply leave a wide range of navigation without making purchase. The research on the behavior and purchase of online consumers has been steadily progressed, and related services and applications based on behavior data of consumers have been developed in practice. In recent years, customization strategies and recommendation systems of consumers have been utilized due to the development of big data technology, and attempts are being made to optimize users' shopping experience. However, even in such an attempt, it is very unlikely that online consumers will actually be able to visit the website and switch to the purchase stage. This is because online consumers do not just visit the website to purchase products but use and browse the websites differently according to their shopping motives and purposes. Therefore, it is important to analyze various types of visits as well as visits to purchase, which is important for understanding the behaviors of online consumers. In this study, we explored the clustering analysis of session based on click stream data of e-commerce company in order to explain diversity and complexity of search behavior of online consumers and typified search behavior. For the analysis, we converted data points of more than 8 million pages units into visit units' sessions, resulting in a total of over 500,000 website visit sessions. For each visit session, 12 characteristics such as page view, duration, search diversity, and page type concentration were extracted for clustering analysis. Considering the size of the data set, we performed the analysis using the Mini-Batch K-means algorithm, which has advantages in terms of learning speed and efficiency while maintaining the clustering performance similar to that of the clustering algorithm K-means. The most optimized number of clusters was derived from four, and the differences in session unit characteristics and purchasing rates were identified for each cluster. The online consumer visits the website several times and learns about the product and decides the purchase. In order to analyze the purchasing process over several visits of the online consumer, we constructed the visiting sequence data of the consumer based on the navigation patterns in the web site derived clustering analysis. The visit sequence data includes a series of visiting sequences until one purchase is made, and the items constituting one sequence become cluster labels derived from the foregoing. We have separately established a sequence data for consumers who have made purchases and data on visits for consumers who have only explored products without making purchases during the same period of time. And then sequential pattern mining was applied to extract frequent patterns from each sequence data. The minimum support is set to 10%, and frequent patterns consist of a sequence of cluster labels. While there are common derived patterns in both sequence data, there are also frequent patterns derived only from one side of sequence data. We found that the consumers who made purchases through the comparative analysis of the extracted frequent patterns showed the visiting pattern to decide to purchase the product repeatedly while searching for the specific product. The implication of this study is that we analyze the search type of online consumers by using large - scale click stream data and analyze the patterns of them to explain the behavior of purchasing process with data-driven point. Most studies that typology of online consumers have focused on the characteristics of the type and what factors are key in distinguishing that type. In this study, we carried out an analysis to type the behavior of online consumers, and further analyzed what order the types could be organized into one another and become a series of search patterns. In addition, online retailers will be able to try to improve their purchasing conversion through marketing strategies and recommendations for various types of visit and will be able to evaluate the effect of the strategy through changes in consumers' visit patterns.

• 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 $3.86^{\circ}C$.