• Journal of Information Processing Systems
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• 제14권1호
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• pp.32-55
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• 2018

The digitization of music has seen a considerable increase in audience size from a few localized listeners to a wider range of global listeners. At the same time, the digitization brings the challenge of smoothly retrieving music from large databases. To deal with this challenge, many systems which support the smooth retrieval of musical data have been developed. At the computational level, a query music piece is compared with the rest of the music pieces in the database. These systems, music information retrieval (MIR systems), work for various applications such as general music retrieval, plagiarism detection, music recommendation, and musicology. This paper mainly addresses two parts of the MIR research area. First, it presents a general overview of MIR, which will examine the history of MIR, the functionality of MIR, application areas of MIR, and the components of MIR. Second, we will investigate music similarity measurement methods, where we provide a comparative analysis of state of the art methods. The scope of this paper focuses on comparative analysis of the accuracy and efficiency of a few key MIR systems. These analyses help in understanding the current and future challenges associated with the field of MIR systems and music similarity measures.

• Proceedings of the Korea Information Processing Society Conference
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• 한국정보처리학회 2008년도 추계학술발표대회
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• pp.878-881
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• 2008

To index the object's trajectory is an important aspect in moving object database management. This paper implements an optimizing index structure named Rend 3D R-tree based on 3D R-Tree. This paper demonstrates the time period update method to reconstruct the MBR for the moving objects in order to decrease the dead space that is produced in the closed time dimension of the 3D R-tree, then a rend method is introduced for indexing both current data and history data. The result of experiments illustrates that given methods outperforms 3D R-Tree and LUR tree in query processes.

• The KIPS Transactions:PartA
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• 제13A권6호
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• pp.545-554
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• 2006

Although the current Web search engines provide tremendous information, it is hard to find right information among the huge information. Users need to spend extra time to filter out unnecessary information. In order to overcome the limit of current Web search engines, Semantic Web was developed to provide efficient search, integration, and reuse of information by structuring semantic information from Web resources. In this paper, an elementary education contents retrieval system using Semantic Web is proposed. The proposed system emphasizes history contents that have high relevancy among data. For construction of the proposed system, ontology is proposed first for elementary study contents and ontology for historical contents is proposed for easy access to those contents using semantic relation among them. Based on the ontology, the proposed system is designed and implemented. The proposed system has the following characteristics. First, the system provides various query formats in detail so that search results can be refined efficiently. Second, the system presents only semantically information connected with key words or including key words using study contents ontology. Finally, the proposed system can increase study effects by presenting various contents that are related with query by users.

• Journal of Digital Contents Society
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• 제13권1호
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• pp.43-52
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• 2012

For accurate search on information resources, it is needed to manage gradual changes in ontology efficiently. Recently, because ontology is often written using OWL, techniques that can manage changes in OWL documents are required. To meet these needs, in this paper, we classify changeable elements to detect changes in OWL ontology and propose a storage schema that can manage the changes according to the characteristics of each element. And we suggest the possibility of improving performance of query processing using views that provide information about classes or properties in each ontology version. The proposed storage schema stores changes in metadata associated with each ontology version. In addition, it can manage metadata that must be added or deleted through reasoning when ontology changes. So, the proposed storage schema can support queries about history of changes in ontology and provide accurate and valid metadata that is suitable for user-selected ontology version.

• Journal of the Korea Society of Computer and Information
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• 제20권5호
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• pp.99-106
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• 2015

This research is focused on the development of the proprietary database embedded in the OTT device, which is used for searching and indexing video contents, and also the development of the search algorithm in the form of the critical components of the interface application with the OTT's database to provide video query searching, such as remote control smartphone application. As the number of available channels has increased to anywhere from dozens to hundreds of channels, it has become increasingly difficult for the viewer to find programs they want to watch. To address this issue, content providers are now in need of methods to recommend programs catering to each viewer's preference. the present study aims provide of the algorithm which recommends contents of OTT program by analyzing personal watching pattern based on one's history.

• Journal of English Language & Literature
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• 제64권4호
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• pp.565-584
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• 2018

The pedagogical dynamic dramatized in Shaw's Pygmalion, which sets man as a distinct pedagogical authority and woman his subject spawning similarly patterned plays many decades later, has been relatively overlooked in the play's criticism clouded by its predominantly mythical theme. Shaw stages Eliza's pedagogical subordination to Higgins followed by her Nora-esque exit with the declaration, "I'll go and be a teacher." The central premise of this article is that the pioneering modern playwright and feminist's pedagogical rewriting of A Doll's House sets out a historical dialogue between Eliza, a new woman who repositions herself as a teacher renouncing her earlier subordinate pedagogical position that is culturally ascribed to women while threatening to replace her paternal teacher, and her immediate precursors, that is, Victorian women teachers whose professional career was socially "anathematized." Through a historical probe into the social status of Victorian women teachers, the article attempts to align their abortive career with Eliza's new womanly re-appropriation of the profession of teaching. With Pygmalion as the starting point of its query, this article conducts a historical survey on the literary representation of pedagogical women from the mid to late Victorian era to the turn of the century. Reading a wide selection of novels and plays alongside of Pygmalion (1912), such as Jane Eyre (1847), A Doll's House (1879), An Enemy of the People (1882), The Odd Women (1893), and The Importance of Being Earnest (1895), it contextualizes Eliza's resolution to be a teacher within the history of female pedagogy. This historical contextualization of the career choice of one of the earliest new women characters in modern drama helps appraise the historical significance of such choice.

• International Journal of Computer Science & Network Security
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• 제22권2호
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• pp.406-412
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• 2022

Storing large amounts of data has always been a big problem from the beginning of computing history. Big Data has made huge advancements in improving business processes by finding the customers' needs using prediction models based on web and social media search. The main purpose of big data stream processing frameworks is to allow programmers to directly query the continuous stream without dealing with the lower-level mechanisms. In other words, programmers write the code to process streams using these runtime libraries (also called Stream Processing Engines). This is achieved by taking large volumes of data and analyzing them using Big Data frameworks. Streaming platforms are an emerging technology that deals with continuous streams of data. There are several streaming platforms of Big Data freely available on the Internet. However, selecting the most appropriate one is not easy for programmers. In this paper, we present a detailed description of two of the state-of-the-art and most popular streaming frameworks: Apache Ignite and Hazelcast. In addition, the performance of these frameworks is compared using selected attributes. Different types of databases are used in common to store the data. To process the data in real-time continuously, data streaming technologies are developed. With the development of today's large-scale distributed applications handling tons of data, these databases are not viable. Consequently, Big Data is introduced to store, process, and analyze data at a fast speed and also to deal with big users and data growth day by day.

• The Journal of Korean Medical History
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• 제30권2호
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• pp.45-61
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• 2017

Objectives : This research aims to investigate the consumer's interest in the Korean Medicine (KM) industry by using Google-trends and Naver-Data lab. A quick and uncomplicated way for those who are already involved with KM industry but do not have expertise in utilizing Big-data searches, is introduced. Methods : 'Direct keyword' was set by FGI (Focus Group Interview) and 'Detailed keyword' was set by using relevant word search and autocomplete search functions in the search engine. By inquiring Naver-Data lab, keyword search volumes are compared by age and sex, date range, and originating region of the researcher. It is possible to determine whether the data is reliable or authentic through examining the associated query. Selected direct keywords used through FGI (Focus Group Interview) were 'Acupuncture', 'Herbal Medicine', 'Cupping', 'Musculoskeletal Disease', 'Diet', and 'Stemina'. Based on these keywords, the following results were derived from the keyword analysis. Results : From August 2016, there was a noticeable surge of interest in men's 'Cupping'. The search for 'Diet' increased in the second quarter of 2016 from all ages. The search volume of 'Stemna' for individuals in their 20s is higher than that of those in their 30s or 40s'. Researchers from the region of Chungcheongbuk-do had a higher level of interest in analgesics and less interest in Korean Medicine. There is a greater interest in the KM market from European countries and America, than from Korea, China, and other Asian countries. Discussion : Despite the limitations of the research, it is meaningful to introduce a quick and easy data search method to compare information by age, sex, and region. Conclusion : The future of research into Korea Medicine and this market is confirmed by our data results which indicate interest from Europe, the United States, and other western countries, but less interest from Korea, China and other Asian countries.

• Journal of Internet Computing and Services
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• 제16권4호
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• pp.39-50
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• 2015

In information retrieval process through search engine, some users want to retrieve several documents that are corresponding with specific time period situation. For example, if user wants to search a document that contains the situation before 'Japanese invasions of Korea era', he may use the keyword 'Japanese invasions of Korea' by using searching query. Then, search engine gives all of documents about 'Japanese invasions of Korea' disregarding time period in order. It makes user to do an additional work. In addition, a large percentage of cases which is related to historical documents have different time period between generation date of a document and record time of contents. If time period in document contents can be extracted, it may facilitate effective information for retrieval and various applications. Consequently, we pursue a research extracting time period of Joseon era's historical documents by using historic literature for Joseon era in order to deduct the time period corresponding with document content in this paper. We define historical objects based on historic literature that was collected from web and confirm a possibility of extracting time period of web document by machine learning techniques. In addition to the machine learning techniques, we propose and apply the similarity filtering based on the comparison between the historical objects. Finally, we'll evaluate the result of temporal indexing accuracy and improvement.

• Journal of the Korea Society of Computer and Information
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• 제17권6호
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• pp.163-172
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• 2012

Collaborative filtering which is used explicit method in a existing recommedation system, can not only reflect exact attributes of item but also still has the problem of sparsity and scalability, though it has been practically used to improve these defects. This paper proposes the personalized recommendation system using RFM method and k-means clustering in u-commerce which is required by real time accessablity and agility. In this paper, using a implicit method which is is not used complicated query processing of the request and the response for rating, it is necessary for us to keep the analysis of RFM method and k-means clustering to be able to reflect attributes of the item in order to find the items with high purchasablity. The proposed makes the task of clustering to apply the variable of featured vector for the customer's information and calculating of the preference by each item category based on purchase history data, is able to recommend the items with efficiency. To estimate the performance, the proposed system is compared with existing system. As a result, it can be improved and evaluated according to the criteria of logicality through the experiment with dataset, collected in a cosmetic internet shopping mall.