• KIPS Transactions on Computer and Communication Systems
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• v.9 no.11
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• pp.281-290
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• 2020

With the spread of smartphones, services that want to use personalized data are increasing. In particular, healthcare-related services deal with a variety of data, and data visualization techniques are used to effectively show this. As data visualization techniques are used, interactions in visualization are also naturally emphasized. In the PC environment, since the interaction for data visualization is performed with a mouse, various filtering for data is provided. On the other hand, in the case of interaction in a mobile environment, the screen size is small and it is difficult to recognize whether or not the interaction is possible, so that only limited visualization provided by the app can be provided through a button touch method. In order to overcome the limitation of interaction in such a mobile environment, we intend to enable data visualization interactions through conversations with chatbots so that users can check individual data through various visualizations. To do this, it is necessary to convert the user's query into a query and retrieve the result data through the converted query in the database that is storing data periodically. There are many studies currently being done to convert natural language into queries, but research on converting user queries into queries based on visualization has not been done yet. Therefore, in this paper, we will focus on query generation in a situation where a data visualization technique has been determined in advance. Supported interactions are filtering on task x-axis values and comparison between two groups. The test scenario utilized data on the number of steps, and filtering for the x-axis period was shown as a bar graph, and a comparison between the two groups was shown as a line graph. In order to develop a natural language processing model that can receive requested information through visualization, about 15,800 training data were collected through a survey of 1,000 people. As a result of algorithm development and performance evaluation, about 89% accuracy in classification model and 99% accuracy in query generation model was obtained.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.11a
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• pp.271-275
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• 2005

In this research, we propose a Unified Fuzzy rule-based knowledge Inference Systems UFIS) to help the expert in cosmetic brand detection. Users' preferred cosmetic product detection is very important in the level of CRM. To this Purpose, many corporations trying to develop an efficient data mining tool. In this study, we develop a prototype fuzzy rule detection and inference system. The framework used in this development is mainly based on two different mechanisms such as fuzzy rule extraction and RDB (Relational DB)-based fuzzy rule inference. First, fuzzy clustering and fuzzy rule extraction deal with the presence of the knowledge in data base and its value is presented with a value between $0\∼1$. Second, RDB and SQL(Structured Query Language)-based fuzzy rule inference mechanism provide more flexibility in knowledge management than conventional non-fuzzy value-based KMS(Knowledge Management Systems)

• Proceedings of the Korea Association of Information Systems Conference
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• 2005.05a
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• pp.79-92
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• 2005

To query the vast amount of web pages which are available i]l the Internet, it is necessary to extract the encoded information in the web pages for converting it into structured data (e.g. relational data for SQL) or semistructured data (e.g. XML data for XQuery), In this paper, we propose a new web information extraction system, PIES, to convert web information into XML documents. PIES is based on a user-specified target schema and HTML tag pattern descriptions. The web information is extracted by the pattern descriptions and validated by the target schema. We designed a new language to describe extraction rules, and a new regular expression to describe HTML tag patterns. We implemented PIES and applied it to the US patent web site to evaluate its correctness. It successfully extracted more than thousands of US patent data and converted them into XML documents.

• Herbal Formula Science
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• v.19 no.2
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• pp.1-10
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• 2011

Objectives : Since its enactment in 1987, the 56 standard prescriptions covered by insurance have remained unchanged from its original version. In this study, we tried to discover most frequently used herbal combinations by analyzing prescriptions used in actual clinical settings. Methods : We have built Structured Query Language to analyze herbal combination and progressed this analysis through analyzing the frequencies of medicinal herb combinations in medical prescription slips. Results : We have found out that traditional Korean medical doctors use about 13 herbs in a prescriptions and usually use 253 kinds of herb. And We have found out the most frequently used herbal combination. Conclusions : In this study, We can suggest new method to decide what do we need on insurance prescriptions.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.4
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• pp.353-359
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• 2005

In this research, we propose a Unified Fuzzy rule-based knowledge Inference Systems (UFIS) to help the expert in cosmetic brand detection. Users' preferred cosmetic product detection is very important in the level of CRM. To this purpose, many corporations trying to develop an efficient data mining tool. In this study, we develop a prototype fuzzy rule detection and inference system. The framework used in this development is mainly based on two different mechanisms such as fuzzy rule extraction and RDB (Relational DB)-based fuzzy rule inference. First, fuzzy clustering and fuzzy rule extraction deal with the presence of the knowledge in data base and its value is presented with a value between 0 -1. Second, RDB and SQL (Structured Query Language)-based fuzzy rule inference mechanism provide more flexibility in knowledge management than conventional non-fuzzy value-based KMS (Knowledge Management Systems).

• ETRI Journal
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• v.42 no.2
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• pp.239-246
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• 2020

Recently, Linked Open Data has become a large set of knowledge bases. Therefore, the need to query Linked Data using question answering (QA) techniques has attracted the attention of many researchers. A QA system translates natural language questions into structured queries, such as SPARQL queries, to be executed over Linked Data. The two main challenges in such systems are lexical and semantic gaps. A lexical gap refers to the difference between the vocabularies used in an input question and those used in the knowledge base. A semantic gap refers to the difference between expressed information needs and the representation of the knowledge base. In this paper, we present a novel method using an ontology lexicon and dependency parse trees to overcome lexical and semantic gaps. The proposed technique is evaluated on the QALD-5 benchmark and exhibits promising results.

• Journal of Information Processing Systems
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• v.13 no.4
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• pp.689-702
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• 2017

The Structured Query Language (SQL) Injection continues to be one of greatest security risks in the world according to the Open Web Application Security Project's (OWASP) [1] Top 10 Security vulnerabilities 2013. The ease of exploitability and severe impact puts this attack at the top. As the countermeasures become more sophisticated, SOL Injection Attacks also continue to evolve, thus thwarting the attempt to eliminate this attack completely. The vulnerable data is a source of worry for government and financial institutions. In this paper, a detailed survey of different types of SQL Injection and proposed methods and theories are presented, along with various tools and their efficiency in intercepting and preventing SQL attacks.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.275-278
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• 2022

본 고는 일상생활 속 다양한 정보들을 제공하는 웹 사이트를 데이터베이스와 연계하여 제작하는 프로젝트를 소개한다. 해당 프로젝트는 코로나19 거리두기 완화 조치에 따라 학교에 익숙하지 못한 학생들에게 학교 주변 시설에 대하여 실용적이며 활용성 높은 정보를 제공하기 위한 웹 사이트를 제작한다. 해당 웹 사이트는 웹 사용자의 편의를 위한 콘텐츠 추가, 점진적인 제공 정보 확대가 예정되어 있어 코로나19 일상생활 회복에 도움이 될 것으로 기대된다.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.253-266
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• 2018

This paper proposes a methodology applying sequence tagging methodology to improve the performance of NER(Named Entity Recognition) used in QA system. In order to retrieve the correct answers stored in the database, it is necessary to switch the user's query into a language of the database such as SQL(Structured Query Language). Then, the computer can recognize the language of the user. This is the process of identifying the class or data name contained in the database. The method of retrieving the words contained in the query in the existing database and recognizing the object does not identify the homophone and the word phrases because it does not consider the context of the user's query. If there are multiple search results, all of them are returned as a result, so there can be many interpretations on the query and the time complexity for the calculation becomes large. To overcome these, this study aims to solve this problem by reflecting the contextual meaning of the query using Bidirectional LSTM-CRF. Also we tried to solve the disadvantages of the neural network model which can't identify the untrained words by using ontology knowledge based feature. Experiments were conducted on the ontology knowledge base of music domain and the performance was evaluated. In order to accurately evaluate the performance of the L-Bidirectional LSTM-CRF proposed in this study, we experimented with converting the words included in the learned query into untrained words in order to test whether the words were included in the database but correctly identified the untrained words. As a result, it was possible to recognize objects considering the context and can recognize the untrained words without re-training the L-Bidirectional LSTM-CRF mode, and it is confirmed that the performance of the object recognition as a whole is improved.

• Journal of the Korea Society of Computer and Information
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• v.15 no.11
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• pp.11-20
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• 2010

XML view is proposed to integrate between XML data and heterogeneous data over distributed environment and global XML view is used to search distributed heterogeneous data. At this time, standard query language for user is XQuery and the method for processing global XQuery queries over distributed environment is one of the new research topics. One of the basic and simple methods to process distributed SQL queries is that generates local queries for processing a global query and constructs the result of the global query from the results of the local queries. However, the syntax of XQuery differs from SQL because the XQuery contains some special expressions like FOR clauses for querying to semi-structured data, of course, FOR clauses are not used in SQL. Therefore, there are some problems to adopt the method for processing global SQL queries for generating local XQuery queries. This paper defines some problems when generates local XQuery queries for processing global XQuery queries and proposes a method for generating local XQuery queries considered these problems. Also we implement and evaluate a Global XQuery Processor which uses our method.