• Communications of the Korean Institute of Information Scientists and Engineers
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• v.21 no.10
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• pp.31-35
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• 2003

• Economic and Environmental Geology
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• v.27 no.1
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• pp.101-113
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• 1994

Geochemical investigations based on measurements of water parameters and sampling of stream sediments have been carried out, in the Okdongcheon stream and its tributaries in the Sangdong area of South Korea. There are two main problems occurring in the Okdongcheon stream: an acid mine drainage in the upper reaches and toxic trace metal contamination of the stream sediments mainly in the lower reaches. Acid mine water originating from coal mining was neutralized at the confluence of the Cheonpyongcheon stream whilst suspended solids due to flocculation of iron in water caused turbidity which was undesirable. Sediments in the Okdongcheon stream have been contaminated by mining activites. Iron was heavily concentrated in sediments in the upper Okdongcheon whilst toxic trace metals including Pb, Cu, Zn, Co, Cd, As and Bi were accumulated in sediments at stations draining metallic mining areas and near the tailings dam. There is now a requrement to neutralise the acid mine drainage and to use site-specific analysis of biological communities to ensure the conservation and preservation of aquatic organisms.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.353-356
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• 2007

With advances in sensor technology, current researches on the pertinent techniques are actively directed toward the way which enables the USN computing service. For many applications using sensor networks, the incoming data are by nature characterized as high-speed, continuous, real-time and infinite. Due to such uniqueness of sensor data characteristics, for some instances a finite-sized buffer may not accommodate the entire incoming data, which leads to inevitable loss of data, and requirement for fast processing makes it impossible to conduct a thorough investigation of data. In addition to the potential problem of loss of data, incoming data in its raw form may exhibit high degree of complexity which evades simple query or alerting services for capturing and extracting useful information. Furthermore, as traditional mining techniques are developed to handle fixed, static historical data, they are not useful and directly applicable for analyzing the sensor data. In this paper, (1) describe how three mining techniques (sensor data outlier analysis, sensor pattern analysis, and sensor data prediction analysis) are appropriate for the USN middleware structure, with their application to the stream data in ocean environment. (2) Another proposal is a middleware structure based on USN environment adaptive to above mining techniques. This middleware structure includes sensor nodes, sensor network common interface, sensor data processor, sensor query processor, database, sensor data mining engine, user interface and so on.

• Journal of Korea Spatial Information System Society
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• v.11 no.1
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• pp.127-136
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• 2009

This paper proposes an on-line mining algorithm of moving trajectory patterns in RFID data streams considering changing characteristics over time and constraints of single-pass data scan. Since RFID, sensor, and mobile network technology have been rapidly developed, many researchers have been recently focused on the study of real-time data gathering from real-world and mining the useful patterns from them. Previous researches for sequential patterns or moving trajectory patterns based on stream data have an extremely time-consum ing problem because of multi-pass database scan and tree traversal, and they also did not consider the time-changing characteristics of stream data. The proposed method preserves the sequential strength of 2-lengths frequent patterns in binary relationship table using the time-evolving graph to exactly reflect changes of RFID data stream from time to time. In addition, in order to solve the problem of the repetitive data scans, the proposed algorithm infers candidate k-lengths moving trajectory patterns beforehand at a time point t, and then extracts the patterns after screening the candidate patterns by only one-pass at a time point t+1. Through the experiment, the proposed method shows the superior performance in respect of time and space complexity than the Apriori-like method according as the reduction ratio of candidate sets is about 7 percent.

• The KIPS Transactions:PartD
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• v.15D no.6
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• pp.741-754
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• 2008

Due to the characteristics of a data stream, it is very important to confine the memory usage of a data mining process regardless of the amount of information generated in the data stream. For this purpose, this paper proposes the Prime pattern tree(PPT) for finding frequent itemsets over data streams with using the confined memory space. Unlike a prefix tree, a node of a PPT can maintain the information necessary to estimate the current supports of several itemsets together. The length of items in a prime pattern can be reduced the total number of nodes and controlled by split_delta $S_{\delta}$. The size and the accuracy of the PPT is determined by $S_{\delta}$. The accuracy is better as the value of $S_{\delta}$ is smaller since the value of $S_{\delta}$ is large, many itemsets are estimated their frequencies. So it is important to consider trade-off between the size of a PPT and the accuracy of the mining result. Based on this characteristic, the size and the accuracy of the PPT can be flexibly controlled by merging or splitting nodes in a mining process. For finding all frequent itemsets over the data stream, this paper proposes a PPT to replace the role of a prefix tree in the estDec method which was proposed as a previous work. It is efficient to optimize the memory usage for finding frequent itemsets over a data stream in confined memory space. Finally, the performance of the proposed method is analyzed by a series of experiments to identify its various characteristics.

• The Journal of the Korea Contents Association
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• v.10 no.2
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• pp.99-110
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• 2010

An event means a flow which has a time attribute such as a symptom of patient. Stream data collected by sensors can be summarized as an interval event which has a time interval between the start-time point and the end-time point in multiple stream data environment. Most of temporal mining techniques have considered only the frequent events. However, these approaches may ignore the infrequent event even if it is important. In this paper, we propose a new temporal data mining that can find association rules for the significant temporal relation based on interval events in multidimensional stream data environment. Our method considers the weight of events and stream data on the sensing time point of abnormal events. And we can discover association rules on the significant temporal relation regardless of the occurrence frequency of events. The experimental analysis has shown that our method provide more useful knowledge than other conventional methods.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.04a
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• pp.306-309
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• 2009

일반적으로 이벤트는 발생 시점이라는 시간 속성을 갖는다. 그리고 고객 단위로 이벤트를 축적한 데이터베이스가 있다면 데이터 마이닝을 통하여 유용한 정보를 탐사할 수 있다. 특히 이벤트 발생의 원인과 결과에 대한 관계 규칙을 찾아낼 수 있다면 과거의 정보를 바탕으로 미래를 예측할 수 있는 예측 판단 정보로 사용할 수 있다. 본 연구에서는 데이터 스트림 시스템에서 시간 관계 규칙을 탐사하고 시간 관계 규칙을 구성하는 이벤트 간의 영향력을 측정하기 위한 SM-EC(data Stream Mining for Exploration of Causality)기법을 제안한다. 실험을 통하여 SM-EC가 제공하는 영향력 정보는 다양한 비상 상황에 대처하는 중요한 척도가 될 수 있음을 확인하였다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.8
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• pp.1998-2004
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• 2009

Recently, due to technical developments of various storage devices and networks, the amount of data increases rapidly. The large volume of data streams poses unique space and time constraints on the data mining process. The continuous characteristic of streaming data necessitates the use of algorithms that require only one scan over the stream for knowledge discovery. Most of the researches based on the support are concerned with the frequent itemsets, but ignore the infrequent itemsets even if it is crucial. In this paper, we propose an efficient method WSFI-Mine(Weighted Support Frequent Itemsets Mine) to mine all frequent itemsets by one scan from the data stream. This method can discover the closed frequent itemsets using DCT(Data Stream Closed Pattern Tree). We compare the performance of our algorithm with DSM-FI and THUI-Mine, under different minimum supports. As results show that WSFI-Mine not only run significant faster, but also consume less memory.

• The KIPS Transactions:PartD
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• v.14D no.5
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• pp.483-490
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• 2007

Recently mining techniques that analyze the data stream to discover potential information, have been widely studied. However, most of the researches based on the support are concerned with the frequent event, but ignore the infrequent event even if it is crucial. In this paper, we propose SM-AF method discovering association rules to an abnormal event. In considering the window that an abnormal event is sensed, SM-AF method can discover the association rules to the critical event, even if it is occurred infrequently. Also, SM-AF method can discover the significant rare itemsets associated with abnormal event and periodic event itemsets. Through analysis and experiments, we show that SM-AF method is superior to the previous methods of mining association rules.

• The Journal of the Korea Contents Association
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• v.9 no.1
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• pp.147-158
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• 2009

Recently, the data model in stream data environment has massive, continuous, and infinity properties. However the stream data processing like query process or data analysis is conducted using a limited capacity of disk or memory. In these environment, the traditional frequent pattern discovery on transaction database can be performed because it is difficult to manage the information continuously whether a continuous stream data is the frequent item or not. In this paper, we propose the method which we are able to predict the frequent items using the regression model on continuous stream data environment. We can use as a prediction model on indefinite items by constructing the regression model on stream data. We will show that the proposed method is able to be efficiently used on stream data environment through a variety of experiments.