• Journal of Intelligence and Information Systems
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• v.10 no.3
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• pp.27-37
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• 2004

XML has been proposed as a document standard for the representation and exchange of data on the WWW, and also becoming a standard for the search and reuse of scattered documents. When implementing a XML contents management system, special consideration should be imposed on how to model data and how to store the modelled data for effective and efficient management of the semi-structured data. In this paper, we proposed a new data model for the storage and search of XML document data. This proposed data model could represent both of data and structure views of XML documents, and be applied to the new data system for XML documents as well as the existing data systems.

• Journal of the Ergonomics Society of Korea
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• v.33 no.1
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• pp.69-76
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• 2014

Objective: This study aims to present literature providing researchers with insights on specific fields of research and highlighting the major issues in the research topics. A systematic review is suggested using content analysis on literatures regarding "visual search", "eye movement", and "eye track". Background: Literature review can be classified as "narrative" or "systematic" depending on its approach in structuring the content of the research. Narrative review is a traditional approach that describes the current state of a study field and discusses relevant topics. However, since literatures on specific area cover a broad range, reviewers inherently give subjective weight on specific issues. On the contrary, systematic review applies explicit structured methodology to observe the study trends quantitatively. Method: We collected meta-data of journal papers using three search keywords: visual search, eye movement, and eye track. The collected information contains an unstructured data set including many natural languages which compose titles and abstracts, while the keyword of the journal paper is the only structured one. Based on the collected terms, seven categories were evaluated by inductive categorization and quantitative analysis from the chronological trend of the research area. Results: Unstructured information contains heavier content on "stimuli" and "condition" categories as compared with structured information. Studies on visual search cover a wide range of cognitive area whereas studies on eye movement and eye track are closely related to the physiological aspect. In addition, experimental studies show an increasing trend as opposed to the theoretical studies. Conclusion: By systematic review, we could quantitatively identify the characteristic of the research keyword which presented specific research topics. We also found out that the structured information was more suitable to observe the aim of the research. Chronological analysis on the structured keyword data showed that studies on "physical eye movement" and "cognitive process" were jointly studied in increasing fashion. Application: While conventional narrative literature reviews were largely dependent on authors' instinct, quantitative approach enabled more objective and macroscopic views. Moreover, the characteristics of information type were specified by comparing unstructured and structured information. Systematic literature review also could be used to support the authors' instinct in narrative literature reviews.

• The Journal of the Korea Contents Association
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• v.19 no.1
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• pp.1-15
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• 2019

One of the biggest difficulties in the vocational training field is the dropout problem. A large number of students drop out during the training process, which hampers the waste of the state budget and the improvement of the youth employment rate. Previous studies have mainly analyzed the cause of dropouts. The purpose of this study is to propose a machine learning based model that predicts dropout in advance by using various information of learners. In particular, this study aimed to improve the accuracy of the prediction model by taking into consideration not only structured data but also unstructured data. Analysis of unstructured data was performed using Word2vec and Convolutional Neural Network(CNN), which are the most popular text analysis technologies. We could find that application of the proposed model to the actual data of a domestic vocational training institute improved the prediction accuracy by up to 20%. In addition, the support vector machine-based prediction model using both structured and unstructured data showed high prediction accuracy of the latter half of 90%.

• International Journal of Contents
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• v.15 no.4
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• pp.113-119
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• 2019

It has been reported that large amounts of information on agri-foods were delivered to consumers through television and social networks, and the information may influence consumers' behavior. The purpose of this paper was first to analyze relations of social network service and broadcasting program on paprika consumption in the aspect of amounts to purchase and identify potential factors that can promote paprika consumption; second, to develop prediction models of paprika consumption by using structured and unstructured big data. By using data 2010-2017, cross-correlation and time-series prediction algorithms (autoregressive exogenous model and vector error correction model), statistically significant correlations between paprika consumption and television programs/shows and blogs mentioning paprika and diet were identified with lagged times. When paprika and diet related data were added for prediction, these data improved the model predictability. This is the first report to predict paprika consumption by using structured and unstructured data.

• Journal of Korea Multimedia Society
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• v.21 no.2
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• pp.199-209
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• 2018

Recently, services provided to consumers are increasingly being combined with big data such as low-priced shopping, customized advertisement, and product recommendation. With the increasing importance of big data, the web crawler that collects data from the web has also become important. However, there are two problems with existing web crawlers. First, if the URL is hidden from the link, it can not be accessed by the URL. The second is the inefficiency of fetching more data than the user wants. Therefore, in this paper, through the Casper.js which can control the DOM in the headless brwoser, DOM event is generated by accessing the URL to the hidden link. We also propose an intelligent web crawler system that allows users to make steps to fine-tune both Structured and unstructured data to bring only the data they want. Finally, we show the superiority of the proposed crawler system through the performance evaluation results of the existing web crawler and the proposed web crawler.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.12
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• pp.4706-4724
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• 2020

With various structured data, such as the company size, loan balance, and savings accounts, the voice of customer (VOC), which is text data containing contact history and counseling details was analyzed in this study. To analyze unstructured data, the term frequency-inverse document frequency (TF-IDF) analysis, semantic network analysis, sentiment analysis, and a convolutional neural network (CNN) were implemented. A performance comparison of the models revealed that the predictive model using the CNN provided the best performance with regard to predictive power, followed by the model using the TF-IDF, and then the model using semantic network analysis. In particular, a character-level CNN and a word-level CNN were developed separately, and the character-level CNN exhibited better performance, according to an analysis for the Korean language. Moreover, a systematic selection model for optimal text mining techniques was proposed, suggesting which analytical technique is appropriate for analyzing text data depending on the context. This study also provides evidence that the results of previous studies, indicating that individual customers leave when their loyalty and switching cost are low, are also applicable to corporate customers and suggests that VOC data indicating customers' needs are very effective for predicting their behavior.

• Journal of Information Technology Services
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• v.22 no.4
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• pp.123-140
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• 2023

In recent times, the global economy has been subject to increasing volatility, which has made it considerably more difficult to accurately predict economic indicators compared to previous periods. In response to this challenge, the present study conducts an exploratory investigation that aims to predict the Business Survey Index (BSI) by leveraging data mining techniques on both structured and unstructured data sources. For the structured data, we have collected information regarding foreign, domestic, and industrial conditions, while the unstructured data consists of content extracted from newspaper articles. By employing an extensive set of 44 distinct data mining techniques, our research strives to enhance the BSI prediction accuracy and provide valuable insights. The results of our analysis demonstrate that the highest predictive power was attained when using data exclusively from the t-1 period. Interestingly, this suggests that previous timeframes play a vital role in forecasting the BSI effectively. The findings of this study hold significant implications for economic decision-makers, as they will not only facilitate better-informed decisions but also serve as a robust foundation for predicting a wide range of other economic indicators. By improving the prediction of crucial economic metrics, this study ultimately aims to contribute to the overall efficacy of economic policy-making and decision processes.

• Journal of the Korean Society for information Management
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• v.9 no.2
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• pp.118-153
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• 1992

This study att.empts to conduct a system an;~lysis and dcsign for circt~lation control in a univcr~ity library. In this proccss, structured sysltcm analys15 tcrhn~clucs Iikr data flolv diagrnms. data dictiomiry, and entity - relationship analysis, arc. vmploycd to construct both concqitual and p h p c a l data models of current and new circulation systems. Thc whole design aims at an al~tomntctf c~r.culation system on thc [nlcro-compurcr --basis in a mid dle - sized library.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.694-698
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• 2004

In this paper, as one of these efforts for making the intelligent data mining system we propose the Associative frame of the memory according to the following three steps. First,the structured frame for performing the main brain function should be made. In this frame, the concepts of learning memory and episode memory are considered. Second,the learning mechanism for data acquisition and storing mechanism in the memory frame are provided. The obtained data are arranged and stored in the memory following the rules of the structured memory frame. Third, it is the last step of processing the inference and knowledge retrieval function using the stored knowledge in the associative memory frame. This system is applied to the area for estimating the purchasing degree from the type of customer's tastes, the pattern of commodities and the evaluation of a company.

• Korean Journal of Computational Design and Engineering
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• v.16 no.1
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• pp.11-20
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• 2011

A structured beam laser is often used to scan object and make 3D model. Multiple cameras are inevitable to see occluded areas, which is the main reason of the high price of the scanner. In this paper, a low cost 3D foot scanner is developed using one camera and two mirrors. The camera and two mirrors are located below and above the foot, respectively. Occluded area, which is the top of the foot, is reflected by the mirrors. Then the camera measures 3D point data of the bottom and top of the foot at the same time. Then, the whole foot model is reconstructed after symmetrical transformation of the data reflected by mirrors. The reliability of the scan data depends on the accuracy of the parameters between the camera and the laser. A calibration method is also proposed and verified by experiments. The results of the experiments show that the worst errors of the system are 2 mm along x, y, and z directions.