• Journal of KIISE:Software and Applications
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• v.36 no.3
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• pp.236-243
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• 2009

With advances in topic detection and tracking(TDT), automatic trend analysis from a collection of time-stamped documents, like patents, news papers, and blog pages, is a challenging research problem. Past research in this area has mainly focused on showing a trend line over time of a given concept by measuring the strength of trend-associated term frequency information. for detection of emerging trends, either a simple criterion such as frequency change was used, or an overall comparison was made against a training data. We note that in order to show most salient trends detected among many possibilities, it is critical to devise a ranking function. To this end, we define four properties(change, persistency, stability and volume) of trend lines drawn from frequency information, to quantify various aspects of trends, and propose a method by which trend lines can be ranked. The properties are examined individually and in combination in a series of experiments for their validity using the ranking algorithm. The results show that a judicious combination of the four properties is a better indicator for salient trends than any single criterion used in the past for ranking or detecting emerging trends.

• Journal of KIISE:Software and Applications
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• v.36 no.5
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• pp.420-429
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• 2009

Patent text is a rich source for discovering technological trends. In order to automate such a discovery process, we attempt to identify phrases corresponding to the problem and its solution method which together form a technology. Problem and solution phrases are identified by a SVM classifier using features based on a combination of a language modeling approach and linguistic clues. Based on the occurrence statistics of the phrases, we identify the time span of each problem and solution and finally generate a trend. Based on our experiment, we show that the proposed semantic phrase identification method is promising with its accuracy being 77% in R-precision. We also show that the unsupervised method for discovering technological trends is meaningful.

• The KIPS Transactions:PartD
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• v.18D no.6
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• pp.493-500
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• 2011

The trends of technical terms express the changes of particular subjects in a specific research field over time. However, the amount of academic literature and patent data is too large to be analyzed by human resources. In this paper, we propose a method that can detect and analyze the trends of terms by modeling the life cycle of the terms. The proposed method is composed of the following steps. First, the technical terms are extracted from academic literature data, and the TDVs(Term Dominance Values) of terms are computed on a periodic basis. Based on the TDVs, the life cycles of terms are modeled, and technical terms with similar temporal patterns of the life cycles are classified into the same trends class. The experiments shown in this paper is performed by exploiting the NDSL academic literature data maintained by KISTI.

• The Journal of the Korea Contents Association
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• v.20 no.2
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• pp.96-104
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• 2020

As consumers' consumption activities become more active due to the activation of online shopping malls, companies are conducting item trend analyses to boost sales. The existing item trend analysis methods are analyzed by considering only the activities of users in online shopping mall services, making it difficult to identify trends for new items without purchasing history. In this paper, we propose a trend analysis method that combines data in online shopping mall services and social network data to analyze item trends in users and potential customers in shopping malls. The proposed method uses the user's activity logs for in-service data and utilizes hot topics through word set extraction from social network data set to reflect potential users' interests. Finally, the item trend change is detected over time by utilizing the item index and the number of mentions in the social network. We show the superiority of the proposed method through performance evaluations using social network data.

• The Journal of the Korea institute of electronic communication sciences
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• v.7 no.6
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• pp.1343-1351
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• 2012

In this study, trends for five research areas of LED(Light Emitting Diode), OLED(Organic Light Emitting Diode), LCD(Liquid Crystal Display), PDP(Plasma Display Panel) and CRT(Cathode Ray Tube) are investigated using bibliometric analysis. The papers and patents citation data were extracted from Scopus and USPTO databases, respectively. We could figure out the research trends by the number of publications and citation information. We prospect the current interests and future trends by investigating the development process of the 5 research areas as function of time.

• Annual Conference on Human and Language Technology
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• 2013.10a
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• pp.133-136
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• 2013

근래 트위터와 페이스북 등의 SNS(Social Network Service)에서 일반 대중의 관심사나 트렌드 등의 이슈를 탐지하는 많은 연구가 이루어지고 있다. 본 논문에서는 검색어에 대한 연관 어휘 추출을 통해 검색어에 연관된 이슈나 화제를 트위터에서 추출하기 위한 방법을 제안한다. 본 논문에서는 연관성이 높은 단어는 서로 가깝게 발생할 것으로 기대하고, 단어 간 거리가 가까울수록, 공기빈도가 높을수록 커지는 단어연관도 계산법을 제안한다. 연관도 값이 임계치를 넘는 어휘를 연관 어휘로 보고 네트워크의 형태로 관련 이슈를 제시한다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.3
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• pp.602-608
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• 2014

Smartphones have become commonplace to use smart, BYOD (Bring Your Own Device) spread the trend of domestic WLAN use is intensifying as a result, the security threat will be greatly increased. Even though WLAN vendors such as Cisco Systems Inc,. Aruba networks released WIPS, MDM, DLP etc, however, these solutions can not be easily introduced for small business due to high cost or administrative reasons. In this paper, without the introduction of expensive H/W equipment, in WLAN environments, packet analysis, AP, Station management, security vulnerabilities can be analyzed by the proposed intrusion detection system.

• Journal of the Korean Society of Marine Environment & Safety
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• v.30 no.1
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• pp.32-42
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• 2024

In this study, 1,474 effective patents were derived for quantitative analysis of five major countries, including Korea, China, Japan, the United States and Europe, for marine space policy simulator technology used as a support for integrated marine space management means, and domestic technology competitiveness and domestic and foreign technology trends were identified through annual and national patent application trends and word cloud analysis. This diagnosed the need for active policy support for research and development of marine space policy simulator technology at the government level and preparation through linkage strategies such as patent application consideration and standardization preoccupation for surrounding technologies to prepare for China-led market monopoly and preoccupation.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.1
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• pp.31-36
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• 2014

Social network services(SNS) including Twitter and Facebook are good resources to extract various issues like public interest, trend and topic. This paper proposes a method to extract query-related issues by calculating relatedness between terms in Twitter. As a term that frequently appears near query terms should be semantically related to a query, we calculate term relatedness in retrieved documents by summing proximity that is proportional to term frequency and inversely proportional to distance between words. Then terms, relatedness of which is bigger than threshold, are extracted as query-related issues, and our system shows those issues with a connected network. By analyzing single transitions in a connected network, compound words are easily obtained.

• The Journal of Korean Institute of Next Generation Computing
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• v.15 no.1
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• pp.86-98
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• 2019

Recently, the trend of building container-based PaaS (Platform-as-a-Service) is expanding. Container-based platform technology has been a core technology for realizing a PaaS. Containers have lower operating overhead than virtual machines, so hundreds or thousands of containers can be run on a single physical machine. However, recording and monitoring the storage logs for a large number of containers running in cloud computing environment occurs significant overhead. This work has identified two problems that occur when detecting a file system change event of a container running in a cloud computing environment. This work also proposes a system for container file system event detection in the environment by solving the problem. In the performance evaluation, this work performed three experiments on the performance of the proposed system. It has been experimentally proved that the proposed monitoring system has only a very small effect on the CPU, memory read and write, and disk read and write speeds of the container.