• Journal of KIISE:Databases
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• v.36 no.3
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• pp.169-179
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• 2009

Communication log data consist of communication events such as sending and receiving e-mail or instance message and visiting web sites, etc. Many countries including USA and EU enforce the retention of these data on the communication service providers for the purpose of investigating or detecting criminals through the Internet. Because size of the retained data is very large, the efficient method for extracting valuable information from the data is needed for Law Enforcement Authorities to use the retained data. This paper defines the Interactive Communication Sequence Patterns(ICSPs) that is the important information when each communication event in communication log data consists of sender, receiver, and timestamp of this event. We also define a Mining(FDICSP) problem to discover such patterns and propose a method called Fast Discovering Interactive Communication Sequence Pattern(FDICSP) to solve this problem. FDICSP focuses on the characteristics of ICS to reduce the search space when it finds longer sequences by using shorter sequences. Thus, FDICSP can find Interactive Communication Sequence Patterns efficiently.

• Proceedings of the Korean Information Science Society Conference
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• 2001.04a
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• pp.358-360
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• 2001

본 논문은 웹을 이용하는 사용자들이 웹 문서 액세스 패턴을 파악하여 캐슁을 할 대상을 결정하고 관리하는 적응력이 있는 웹 캐슁 서버를 제안하고 구현한다. 빈번히 나타나는 순서열을 찾는 데이터 마이닝 기법을 캐슁 서버의 로그에 적용하여 순차적으로 액세스되는 웹 객체들을 찾아낸 다음, 필요한 경우 이들을 캐쉬 내에 선반입함으로써 히트율을 높이고, 따라서 캐쉬의 효율을 증가시킬 수 있는 캐슁 서버의 모델을 제시한다. 그리고 초기실험을 통하여, 제안된 캐슁 서버의 효율이 기존 캐슁서버에 비해 실제 상당히 증가함을 보였다.

• The KIPS Transactions:PartD
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• v.9D no.3
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• pp.365-380
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• 2002

Temporal data mining, the incorporation of temporal semantics to existing data mining techniques, refers to a set of techniques for discovering implicit and useful temporal knowledge from large quantities of temporal data. Temporal knowledge, expressible in the form of rules, is knowledge with temporal semantics and relationships, such as cyclic pattern, calendric pattern, trends, etc. There are many examples of temporal data, including patient histories, purchaser histories, and web log that it can discover useful temporal knowledge from. Many studies on data mining have been pursued and some of them have involved issues of temporal data mining for discovering temporal knowledge from temporal data, such as sequential pattern, similar time sequence, cyclic and temporal association rules, etc. However, all of the works treated data in database at best as data series in chronological order and did not consider temporal semantics and temporal relationships containing data. In order to solve this problem, we propose a theoretical framework for temporal data mining. This paper surveys the work to date and explores the issues involved in temporal data mining. We then define a model for temporal data mining and suggest SQL-like mining language with ability to express the task of temporal mining and show architecture of temporal mining system.