• Proceedings of the Korea Information Processing Society Conference
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• 2009.11a
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• pp.727-728
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• 2009

최근 컴퓨터와 인터넷의 발달로 과거 창구거래를 이용하던 방법에서 HTS(Home Trading System)을 이용하여 거래하게 됨으로써 개인투자자들도 쉽게 주식투자를 할 수 있게 되었다. 그러나 개인들이 방대한 양의 과거 데이터를 분석하기에는 상당한 어려움이 있다. 본 논문에서는 주식 데이터베이스로부터 과거 특정 종목들 간 연관성을 추출하여 투자자들로 하여금 주식 선별에 참고가 될 수 있는 방안에 관하여 논의한다. 기존의 논문에서 제안된 과거 패턴을 이용하여 미래의 주가변화를 예측하는 것과 달리, 종목들 간에 연관성을 통하여 하나의 테마가 형성 되었을 때 주도주의 변화로 관련주의 변화를 파악하여 투자에 유익한 정보를 제공하는데 목적이 있다.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.7
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• pp.865-870
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• 2004

Development of new information and traffic technology causes fast-growing in the field of information-based system. At recent, development of LBS(Location Based Service) makes a remarkable growth of industry as GPS(Global Positioning System) becomes wide-spread and location information becomes more important. However, there is a problem like infringement of privacy when location information is used improperly［1］. In this paper, LBS platform is proposed in order to prevent infringement of privacy. To implement, we classify user path as pattern in a zone of user life. Thereupon, location information is provided according to user' specific situation.

• 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.

• Proceedings of the Korean Information Science Society Conference
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• 2007.10c
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• pp.200-204
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• 2007

최근 유비쿼터스 환경의 발달로 인해 사용자 중심의 유비쿼터스 기술이 활발히 연구되고 있다. 이에 따른 각종 응용 분야가 활발히 연구 중이며, 그 중에서 특히 U-Health 기술이 주목받고 있다. U-Health 기술은 질병의 치료라는 전통적인 관점의 의료 서비스에서 벗어나 건강한 상태의 지속적인 관리와 질병의 예방이라는 적극적이고 확장된 개념으로 발전해가고 있다. 건강상태를 관리하고 진단하기 위해서는 기존의 진단데이터를 효율적으로 관리하고, 그것을 토대로 하여 유용한 정보를 얻어 낼 수 있는 방법이 필요하다. 지금까지는 데이터를 처리하기 위하여 통계적인 수치나 전문가에 의한 전문지식을 토대로 하는 방법을 사용하고 있다. 그러나, 건강상태를 관리하고 진단을 목적으로 하는 시스템에서는 높은 정확성이 보장되어야 한다. 또한 유비쿼터스 환경의 특성상 적은 메모리의 사용과 빠른 마이닝 속도가 수반되어야 한다. 본 논문에서는 튜플기반의 진단데이터들을 마이닝하여 진단패턴을 뽑아내는 의료 진단 마이닝 알고리즘을 제안한다. 본 알고리즘은 진단패턴정보의 정확성을 높일 수 있는 장점을 가지며, 튜플기반의 데이터들을 트리 구조로 구성함으로써 마이닝 속도를 향상시킨다. 더 나아가 트리 구조의 컴팩트한 데이터 구조로 메모리 적재가 용이하다. 이는 센서가 부착된 개별 사용자로부터 실시간으로 들어오는 건강상태와 진단패턴과의 비교, 분석을 가능하게 함으로써 보다 정확하고 빠른 진단결과를 내려줄 수 있는 의사결정시스템의 사용에 적합하다.

• KIISE Transactions on Computing Practices
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• v.21 no.11
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• pp.713-720
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• 2015

As computing and network technologies are further developed, communication equipment continues to become smaller, and as a result, mobility is now a predominant feature of current technology. Therefore, demand for speech recognition systems in mobile environments is rapidly increasing. This paper proposes a novel method to recognize the Korean vowel 'ㅐ' as a part of a phoneme-based Korean speech recognition system. The proposed method works by analyzing a sequence of patterns in the time domain instead of the frequency domain, and consequently, its use can markedly reduce computational costs. Three algorithms are presented to detect typical sequence patterns of 'ㅐ', and these are combined to produce the final decision. The results of the experiment show that the proposed method has an accuracy of 89.1% in recognizing the vowel 'ㅐ'.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.413-414
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• 2023

IT 기술의 발달로 E-Commerce 분야는 실시간으로 발생되는 데이터양이 증가하고 있으며, 발생된 데이터는 개인화 맞춤 서비스에 많이 활용되고 있다. 그러나 신생 E-commerce 기업은 신규 상품 및 기존 상품에 대한 정보와 고객 간의 상호 작용 데이터가 존재하지 않아 콜드 스타트 문제가 발생한다. 이에 본 논문에서는 E-commerce 환경에서 실시간 사용자 구매패턴 분석을 통한 사용자 상품 추천 시스템을 제안한다. 제안하는 시스템은 Kafka와 Spark를 사용해 실시간 스트림을 데이터를 처리한다. 주요 기능은 ALS 알고리즘과, FP-Growth 알고리즘을 적용해 콜트 스타트 문제를 해결하며, 사용자 구매 패턴 분석을 통한 분석 결과에 맞는 상품을 사용자에게 추천한다.

• Journal of KIISE:Databases
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• v.33 no.7
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• pp.741-753
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• 2006

As the total amount of traffic data in network has been growing at an alarming rate, many researches to mine traffic data with the purpose of getting useful information are currently being performed. However, network users' privacy can be compromised during the mining process. In this paper, we propose an efficient and practical privacy preserving sequential pattern mining method on network traffic data. In order to discover frequent sequential patterns without violating privacy, our method uses the N-repository server model and the retention replacement technique. In addition, our method accelerates the overall mining process by maintaining the meta tables so as to quickly determine whether candidate patterns have ever occurred. The various experiments with real network traffic data revealed tile efficiency of the proposed method.

• The Journal of the Korea Contents Association
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• v.18 no.4
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• pp.48-59
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• 2018

Recently, network technology has been used in various fields due to development of network technology. However, there has been an increase in the number of attacks targeting public institutions and companies by exploiting the evolving network technology. Meanwhile, the existing network intrusion detection system takes much time to process logs as the amount of network log increases. Therefore, in this paper, we propose a Spark-based network log analysis system that detects unstructured network attack pattern. by using Snort. The proposed system extracts and analyzes the elements required for network attack pattern detection from large amount of network log data. For the analysis, we propose a rule to detect network attack patterns for Port Scanning, Host Scanning, DDoS, and worm activity, and can detect real attack pattern well by applying it to real log data. Finally, we show from our performance evaluation that the proposed Spark-based log analysis system is more than two times better on log data processing performance than the Hadoop-based system.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.23 no.1
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• pp.182-196
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• 2024

This study introduces driving pattern analysis and change detection methods using smartphone sensors, based on contrastive learning. These methods characterize driving patterns without labeled data, allowing accurate classification with minimal labeling. In addition, they are robust to domain changes, such as different vehicle types. The study also examined the applicability of these methods to smartphones by comparing them with six lightweight deep-learning models. This comparison supported the development of smartphone-based driving pattern analysis and assistance systems, utilizing smartphone sensors and contrastive learning to enhance driving safety and efficiency while reducing the need for extensive labeled data. This research offers a promising avenue for addressing contemporary transportation challenges and advancing intelligent transportation systems.

• Journal of Internet Computing and Services
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• v.10 no.2
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• pp.1-13
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• 2009

Intrusion detection is a process that identifies the attacks and responds to the malicious intrusion actions for the protection of the computer and the network resources. Due to the fast development of the Internet, the types of intrusions become more complex recently and need immediate and correct responses because the frequent occurrences of a new intrusion type rise rapidly. Therefore, to solve these problems of the intrusion detection systems, we propose a sequential pattern miner for analysis of the alert data in order to support intelligent and automatic detection of the intrusion. Sequential pattern mining is one of the methods to find the patterns among the extracted items that are frequent in the fixed sequences. We apply the prefixSpan algorithm to find out the alert sequences. This method can be used to predict the actions of the sequential patterns and to create the rules of the intrusions. In this paper, we propose an extended prefixSpan algorithm which is designed to consider the specific characteristics of the alert data. The extended sequential pattern miner will be used as a part of alert data analyzer of intrusion detection systems. By using the created rules from the sequential pattern miner, the HA(high-level alert analyzer) of PEP(policy enforcement point), usually called IDS, performs the prediction of the sequence behaviors and changing patterns that were not visibly checked.