• International Journal of Computer Science & Network Security
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• v.22 no.11
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• pp.381-389
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• 2022

With the collection of huge volumes of text, image, audio, video or combinations of these, in a word multimedia data, the need to explore them in order to discover possible new, unexpected and possibly valuable information for decision making was born. Starting from the already existing data mining, but not as its extension, multimedia mining appeared as a distinct field with increased complexity and many characteristic aspects. Later, the concept of big data was extended to multimedia, resulting in multimedia big data, which in turn attracted the multimedia big data mining process. This paper aims to survey multimedia data mining, starting from the general concept and following the transition from multimedia data mining to multimedia big data mining, through an up-to-date synthesis of works in the field, which is a novelty, from our best of knowledge.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2005.05a
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• pp.266-273
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• 2005

Data mining techniques enable us to generate useful information for decision support from the data sources which are generated and accumulated in the process of routine organizational management activities. College administration system is a typical example that produces a warehouse of student records as each and every student enters a college and undertakes the curricular and extracurricular activities. So far, these data have been utilized to a very limited student service purposes, such as issuance of transcripts, graduation evaluation, GPA calculation, etc. In this paper, we utilize Holland career search test results, TOEIC score, course work list, and GPA score as the input for data mining and generation the student advisory information. Factor analysis, AHP(Analytic Hierarchy Process), artificial neural net, and CART(Classification And Regression Tree) techniques are deployed in the data mining process. Since these data mining techniques are very powerful in processing and discovering useful knowledge and information from large scale student databases, we can expect a highly sophisticated student advisory knowledge and services which may not be obtained with the human student advice experts.

• Proceedings of the CALSEC Conference
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• 2003.09a
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• pp.242-248
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• 2003

◎ Uncover the hidden pattern from massive data(data warehouse) -Builds a reasonable model to predict the future for business advantage -Decision Making based on the learned models(omitted)

• Management Science and Financial Engineering
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• v.19 no.1
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• pp.29-36
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• 2013

This paper considers common due-date assignment and scheduling on parallel machines. The main decisions are: (a) deter-mining the common due-date; (b) allocating jobs to machines; and (c) sequencing the jobs assigned to each machine. The objective is to minimize the sum of the penalties associated with common due-date assignment, earliness and tardiness. As an extension of the existing studies on the problem, we consider sequence-dependent setup times that depend on the type of job just completed and on the job to be processed. The sequence-dependent setups, commonly found in various manufacturing systems, make the problem much more complicated. To represent the problem more clearly, a mixed integer programming model is suggested, and due to the complexity of the problem, two heuristics, one with individual sequence-dependent setup times and the other with aggregated sequence-dependent setup times, are suggested after analyzing the characteristics of the problem. Computational experiments were done on a number of test instances and the results are reported.

• Proceedings of the Korea Information Processing Society Conference
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• 2006.11a
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• pp.305-308
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• 2006

고객관리가 기업의 성패를 좌우하는 중요한 화두로 떠오르면서 보다 쉽고 편리하게 고객의 다양한 Pattern을 발견하고 예측하기 위해 많은 기업들이 CRM과 eCRM을 빠르게 도입하고 있고 Data Mining 기법이 대표적으로 이용되고 있다. 본 논문에서는 Data Mining을 적용함에 있어서 Genetic Algorithm의 무작위 초기화법과 유도된 초기화법을 동시에 사용하는 새로운 집단 초기화 방법을 적용하여 A할인점의 2004년도와 2005년도 우수고객을 예측하였고 실제 고객 데이터와의 비교를 통해 본 논문에서 제안한 새로운 집단 초기화 방법의 성능을 입증하였다.

• International Area Studies Review
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• v.15 no.3
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• pp.145-174
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• 2011

Attracting foreign direct investment in the mining sector becomes a key factor for the continuing economic growth for mining-dependent developing countries. This paper attempts to identify the determining factors that attract FDI inflows into the mining sector. Based on previous conceptual studies, the authors have attempted empirical analyses on a panel of 40 mining countries for the period 1996-2009. These empirical results are the first of their kind given the variables employed are arguably the most comprehensive and exhaustive to date. The empirical results show that market size, trade openness, quality of mined products, quality of infrastructure, regulatory quality, and perceived economic risk associated with the country are positively related to investments in mining. Whereas, tariff rate, corporate tax rate, extent of corruption, and political instability are negatively related to FDI inflows in the mining sector. The empirical results also show that developing countries tend to attract greater amounts of FDI in the mining sector compared to their developed counterparts.

• 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.

• Journal of Energy Engineering
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• v.27 no.3
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• pp.41-47
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• 2018

Bauxite is the principal raw material to produce alumina. Bauxite mining industry has grown gradually due to the rising demand for alumina. U.S Geological Surveys (USGS 2018) estimates the world's reserves of bauxite roughly 55-75 billion tonnes. Vietnam holds up to 3.7 billion tonnes, the third after Guinea and Australia. Most of bauxite reserves are located in Tay Nguyen (Central Highlands), and have only been minimally mined to date. The approved master plan in 2007 of the Government of Vietnam licenses bauxite mining and alumina production projects in Central Highlands through six projects until 2025. These projects constitute many potentially enormous economic, social and environmental impacts.

• Proceedings of the Korea Information Processing Society Conference
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• 2001.04b
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• pp.1089-1092
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• 2001

IDS 에서 가장 중요한 것은 침입을 논리적으로 모델링하고, 이것을 센싱 할 수 있는 Filter 의 개발이며 Filter 에서 발생한 이벤트들에서 특정 공격 행위를 인식할 수 있는 신호인 Signature 의 정의를 통해 이벤트 스트림에서 Signature 를 자동으로 인식할 수 있는 방법에 대한 연구가 가장 핵심적이라고 할 수 있다. 본 논문은 이러한 filter 와 Signature 에서 사용할 수 있도록 특징들이 정의 되어있는 양식으로 원시 데이터로부터 profile 을 생성 filter 와 signature 에서 탐지할 수 있는 모듈을 적용할 수 있도록 네트웍과 host input stream 등의 raw audit data 에서 특징을 추출 Feature Construction 작성에 대한 연구이다.

• Journal of Information Processing Systems
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• v.18 no.1
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• pp.26-47
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• 2022

Chatbots were an important research subject in the past. A chatbot is a computer program or an artificial intelligence program that participates in a conversation via auditory or textual methods. As the research on chatbots progressed, some important issues regarding them changed over time. Therefore, it is necessary to review the technology with a focus on recent advancements and core research technologies. In this paper, we introduce five different chatbot technologies: natural language processing, pattern matching, semantic web, data mining, and context-aware computer. We also introduce the latest technology for the chatbot researchers to recognize the present situation and channelize it in the right direction.