• The Journal of the Korea Contents Association
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• v.13 no.12
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• pp.575-584
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• 2013

Data types used for big-data analysis are very widely, such as news, blog, SNS, papers, patents, sensed data, and etc. Particularly, the utilization of web documents offering reliable data in real time is increasing gradually. And web crawlers that collect web documents automatically have grown in importance because big-data is being used in many different fields and web data are growing exponentially every year. However, existing web crawlers can't collect whole web documents in a web site because existing web crawlers collect web documents with only URLs included in web documents collected in some web sites. Also, existing web crawlers can collect web documents collected by other web crawlers already because information about web documents collected in each web crawler isn't efficiently managed between web crawlers. Therefore, this paper proposed a distributed web crawler. To resolve the problems of existing web crawler, the proposed web crawler collects web documents by RSS of each web site and Google search API. And the web crawler provides fast crawling performance by a client-server model based on RMI and NIO that minimize network traffic. Furthermore, the web crawler extracts core content from a web document by a keyword similarity comparison on tags included in a web documents. Finally, to verify the superiority of our web crawler, we compare our web crawler with existing web crawlers in various experiments.

• The Journal of the Korea Contents Association
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• v.14 no.9
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• pp.45-53
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• 2014

In this study, we analyzed the next generation ITS (C-ITS) technology trends, focusing on the national and international C-ITS projects. Based on the promotion practices of developed countries, we pointed out the lack of linkages with the existing ITS infrastructure. As a way to overcome this problem, we proposed the three-direction to enable the existing ITS infrastructure corresponding to the C-ITS. First one is developing a technique to improve the performance of the existing ITS infrastructure and automate the performance management (Performance-enhanced ITS). Second, developing active sensors or fusion sensor which along with V2X communication technology implement of an active safety driving support system (Safety-enhanced ITS). Third, we need to develop a technology that generate the new advanced traffic data by integrating the collected data from existing ITS infrastructure and nomadic device (Cloud-ITS). By improving the function of the existing ITS infrastructure for adaptation to the new V2X communication environment, we enhanced the efficiency of maintenance performance and would maximize the benefit of the introduction of C-ITS.

• Journal of Convergence for Information Technology
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• v.11 no.5
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• pp.23-29
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• 2021

Recently, a situation in which a specific content service is impossible worldwide has occurred due to a failure of the platform service and a significant social and economic problem has been caused in the global service market. In order to secure the stability of platform services, intelligent platform operation management is required. In this study, big data flow management(BDFM) and implementation method were proposed to quickly detect to abnormal service status in the platform operation environment. As a result of analyzing, BDFM technique improved the characteristics of abnormal failure detection by more than 30% compared to the traditional NMS. The big data flow management method has the advantage of being able to quickly detect platform system failures and abnormal service conditions, and it is expected that when connected with AI-based technology, platform management is performed intelligently and the ability to prevent and preserve failures can be greatly improved.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.6
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• pp.681-689
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• 2019

Web usage pattern discovery is an advanced means by using web log data, and it's also a specific application of data mining technology in Web log data mining. In education Data Mining (DM) is the application of Data Mining techniques to educational data (such as Web logs of University, e-learning, adaptive hypermedia and intelligent tutoring systems, etc.), and so, its objective is to analyze these types of data in order to resolve educational research issues. In this paper, the Web log data of a university are used as the research object of data mining. With using the database OLAP technology the Web log data are preprocessed into the data format that can be used for data mining, and the processing results are stored into the MSSQL. At the same time the basic data statistics and analysis are completed based on the processed Web log records. In addition, we introduced the Apriori Algorithm of Web usage pattern mining and its implementation process, developed the Apriori Algorithm program in Python development environment, then gave the performance of the Apriori Algorithm and realized the mining of Web user access pattern. The results have important theoretical significance for the application of the patterns in the development of teaching systems. The next research is to explore the improvement of the Apriori Algorithm in the distributed computing environment.

• Journal of Korean Library and Information Science Society
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• v.52 no.1
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• pp.155-178
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• 2021

In the era of the 4th industrial revolution, public libraries need a strategy for promoting intelligent library services in order to actively respond to changes in the external environment such as artificial intelligence. Therefore, in this study, based on the concept of artificial intelligence and analysis of domestic and foreign artificial intelligence related trends, policies, and cases, we proposed the future direction of introduction and development of artificial intelligence services in the library. Currently, the library operates a reference information service that automatically provides answers through the introduction of artificial intelligence technologies such as deep learning and natural language processing, and develops a big data-based AI book recommendation and automatic book inspection system to increase business utilization and provide customized services for users. Has been provided. In the field of companies and industries, regardless of domestic and overseas, we are developing and servicing technologies based on autonomous driving using artificial intelligence, personal customization, etc., and providing optimal results by self-learning information using deep learning. It is developed in the form of an equation. Accordingly, in the future, libraries will utilize artificial intelligence to recommend personalized books based on the user's usage records, recommend reading and culture programs, and introduce real-time delivery services through transport methods such as autonomous drones and cars in the case of book delivery service. Service development should be promoted.

• Journal of Korean Society for Geospatial Information Science
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• v.21 no.4
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• pp.125-134
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• 2013

Since 2012, the local autonomous governments, under the recommendations regarding cancellation of local committees directing overly-unexecuted urban planning facilities, have tried to prove validity of such facilities. Factors such as specific standards of cancelation process, will execute policies, diversification of local conditions, connectivity to nearby facilities and possible arise of civil complaints, however, all hinder overly-unexecuted urban planning facilities from getting revitalized. Considering that these unexecuted facilities that local governments have to manage increase in number every year, the burden continuously increases for the governments due to the difficulty of setting aside budget for performing validity checks on such facilities. This research aims to analyze the criteria regarding efficient and systematic method on confirming validity of overly-unexecuted urban planning facilities, to establish into several different processes according to defined categories, and to objectify and quantify such standards. Also, using intelligent spatial information such as digital map, LiDAR data and ortho-images, spatial information analysis method suitable for reassessment was chosen and applied to execute validity analysis regarding overly-unexecuted urban planning facilities.

• Journal of Platform Technology
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• v.11 no.5
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• pp.110-125
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• 2023

In this paper, we utilize time-series aircraft location data measured based on 2019 domestic airports to analyze Go-Around and UOC_D situations during the approach phase of domestic airports. Various clustering-based machine learning techniques are applied to determine the most appropriate analysis method for domestic aviation data through experimentation. The ADS-B sensor is solely employed to measure aircraft positions. We designed a model using clustering algorithms such as K-Means, GMM, and DBSCAN to classify abnormal situations. Among them, the RF model showed the best performance overseas, but through experiments, it was confirmed that the GMM showed the highest classification performance for domestic aviation data by reflecting the aspects specialized in domestic terrain.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.5
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• pp.568-574
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• 2006

User adaptive services have been important features in many applications. To provide such services, various techniques with various kinds of data are being used. In this paper, we propose a method to analyze user's past moving paths and predict the goal position and the path to the goal by observing the user's current moving path. We develop a spatiotemporal similarity measure between paths. We choose a past path which is the most similar to the current path using the similarity. Based on the chosen path, user's spatiotemporal position is estimated. Through experiments we confirm this method is useful and effective.

• The Korean Journal of Applied Statistics
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• v.34 no.6
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• pp.969-977
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• 2021

We cluster the electricity consumption of households in A-apartment in Seoul, Korea using Hierarchical K-means clustering algorithm. The data is recorded from the advanced metering infrastructure (AMI), and we focus on the electricity consumption during evening weekdays in summer. Compare to the conventional clustering algorithms, Hierarchical K-means clustering algorithm is recently applied to the electricity usage data, and it can identify usage patterns while reducing dimension. We apply Hierarchical K-means algorithm to the AMI data, and compare the results based on the various clustering validity indexes. The results show that the electricity usage patterns are well-identified, and it is expected to be utilized as a major basis for future applications in various fields.

• Journal of Digital Convergence
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• v.20 no.1
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• pp.71-86
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• 2022

The purpose of this study is to develop and validate a scale to measure the ethical awareness of users who accept artificial intelligence technology or service. To this end, the constructs and properties of AI ethics were identified through literature analysis on AI ethics. Reliability and validity were assessed through a preliminary survey(N=273), after conducting an open-type survey to men and women(N=133) in 10s to 70s nationwide, extracting the first questions, and reviewing them by experts. The results of an online survey conducted on men and women(N=500) were refined by confirmatory factor analysis. Finally, an AI technology ethics scale was developed. The AI technology ethics awareness scale was developed with 16 questions in total of 4 factors (transparency, safety, fairness, accountability) so that general awareness of ethics related to AI technology can be measured by detailed factors. In addition, through follow-up research, it will be possible to reveal the relationship with measurement variables in various fields by using the ethical awareness scale of artificial intelligence technology.