• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.109-125
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• 2019

As interest on intelligent search engines increases, various studies have been conducted to extract and utilize the features related to products intelligencely. In particular, when users search for goods in e-commerce search engines, the 'color' of a product is an important feature that describes the product. Therefore, it is necessary to deal with the synonyms of color terms in order to produce accurate results to user's color-related queries. Previous studies have suggested dictionary-based approach to process synonyms for color features. However, the dictionary-based approach has a limitation that it cannot handle unregistered color-related terms in user queries. In order to overcome the limitation of the conventional methods, this research proposes a model which extracts RGB values from an internet search engine in real time, and outputs similar color names based on designated color information. At first, a color term dictionary was constructed which includes color names and R, G, B values of each color from Korean color standard digital palette program and the Wikipedia color list for the basic color search. The dictionary has been made more robust by adding 138 color names converted from English color names to foreign words in Korean, and with corresponding RGB values. Therefore, the fininal color dictionary includes a total of 671 color names and corresponding RGB values. The method proposed in this research starts by searching for a specific color which a user searched for. Then, the presence of the searched color in the built-in color dictionary is checked. If there exists the color in the dictionary, the RGB values of the color in the dictioanry are used as reference values of the retrieved color. If the searched color does not exist in the dictionary, the top-5 Google image search results of the searched color are crawled and average RGB values are extracted in certain middle area of each image. To extract the RGB values in images, a variety of different ways was attempted since there are limits to simply obtain the average of the RGB values of the center area of images. As a result, clustering RGB values in image's certain area and making average value of the cluster with the highest density as the reference values showed the best performance. Based on the reference RGB values of the searched color, the RGB values of all the colors in the color dictionary constructed aforetime are compared. Then a color list is created with colors within the range of ${\pm}50$ for each R value, G value, and B value. Finally, using the Euclidean distance between the above results and the reference RGB values of the searched color, the color with the highest similarity from up to five colors becomes the final outcome. In order to evaluate the usefulness of the proposed method, we performed an experiment. In the experiment, 300 color names and corresponding color RGB values by the questionnaires were obtained. They are used to compare the RGB values obtained from four different methods including the proposed method. The average euclidean distance of CIE-Lab using our method was about 13.85, which showed a relatively low distance compared to 3088 for the case using synonym dictionary only and 30.38 for the case using the dictionary with Korean synonym website WordNet. The case which didn't use clustering method of the proposed method showed 13.88 of average euclidean distance, which implies the DBSCAN clustering of the proposed method can reduce the Euclidean distance. This research suggests a new color synonym processing method based on RGB values that combines the dictionary method with the real time synonym processing method for new color names. This method enables to get rid of the limit of the dictionary-based approach which is a conventional synonym processing method. This research can contribute to improve the intelligence of e-commerce search systems especially on the color searching feature.

• The KIPS Transactions:PartD
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• v.12D no.6 s.102
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• pp.857-862
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• 2005

MND(Ministry of National Defense) has developed MND AF(Ministry of National Defense Architecture Framework) and CADM(Core Architecture Data Model to guarantee interoperability among defense information systems. But, it is very difficult to manage architecture product documented through MND AF and CADM. So, there Is necessity for development of modeling tool and repository system which can develop architecture products and manage architecture product informations in common repository In this paper, we developed architecture product management system which supports development and management of meta model and architecture product of MND AF and CADM. Through architecture product management system architect of each agency can construct architecture product in a more effective and efficient way with modeling method and a user can search and refer useful architecture product informations using query function. Also, architecture product management system provides the basis for system integration and interoperability with integration, analysis and comparison of architecture product.

• Journal of Digital Contents Society
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• v.13 no.1
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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 Institute of Electronics Engineers of Korea SP
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• v.47 no.6
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• pp.137-145
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• 2010

Digital vessel have to accurate and efficient mange the digital data from various sensors in the digital vessel. But, In sensor network, it is difficult to transmit and analyze the entire stream data depending on limited networks, power and processor. Therefore it is suitable to use alternative stream data processing after classifying the continuous stream data. In this paper, We propose efficient processing method that arrange some sensors (temperature, humidity, lighting, voice) and process query based on sliding window for efficient input stream and pre-clustering using multiple Support Vector Machine(SVM) algorithm and manage hash table to summarized information. Processing performance improve as store and search and memory using hash table and usage reduced so maintain hash table in memory. We obtained to efficient result that accuracy rate and processing performance of proposal method using 35,912 data sets.

• The Journal of the Korea Contents Association
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• v.20 no.4
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• pp.1-13
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• 2020

In order to understand the recommendation algorithm applied to the online video platform, this study examines the relationship between the content characteristics of K-pop music videos and related videos recommended for playback on YouTube, and analyses which videos are recommended as related videos through network analysis. As a result, the more liked videos, the higher recommendation ranking and most of the videos belonging to the same channel or produced by the same agency were recommended as related videos. As a result of the network analysis of the related video, the network of K-pop music video is strongly formed, and the BTS music video is highly centralized in the network analysis of the related video. These results suggest that the network between K-pops is strong, so when you enter K-pop as a search query and watch videos, you can enjoy K-pop continuously. But when watching other genres of video, K-pop may not be recommended as a related video.

• Journal of the Korea Society of Computer and Information
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• v.10 no.6 s.38
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• pp.75-84
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• 2005

Bulletin board systems have evolved to support diverse multimedia data as well as text. However, current board systems have an weakness : it takes much time and efforts for users to figure out contents of articles. Most board systems provide a searching function with lexical level data access for solving that problem, however it fails to serve users' intented searching results. Moreover, it is nearly impossible to search proper articles if they contain multimedia data. This paper proposed a bulletin board system adopting the Semantic Web to solve this issue. The proposed system provides users with new ontology which is used for describing articles' domain knowledge and multimedia features. Users can describe their own board ontology using the proposed ontology. To support semantic-based searching for diverse domain knowledge without modification of the system, the system dynamically generated input/query interface and RDF data access module according to the board ontology written by administrators. The proposed board system shows that semantic-based searching is feasible and effective for users to find their intended articles.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.25 no.1
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• pp.107-129
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• 2014

Providing reliable and high quality information sources will be one of the basic skills of librarians in the future. Therefore, this study proposed evaluation criteria for health-related information sources based on a survey of public and medical librarians. As a result, a total of 21 items were selected as evaluation items, in three groups. The first, the health information content group, had 13 evaluation items, including accuracy, recency, medical expertise, regular updates, consideration of audience, objectivity, ease of understanding, plain (non-scientific or technical) language, completeness, relevance to the topic, verifiability, citation of information sources, and specification of precautions or warnings. The second group, the health-information sources group, had 5 evaluation items including clarity of health information for achieving its purpose, clarification of the responsibility of health information, compliance to the privacy policy, fairness of health information providers, and ethics of health information providers. The third group was the health-information website design group, and featured 4 evaluation criteria: ease of access, search capabilities, website ease of use, and query-response services.

• Journal of Korea Spatial Information System Society
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• v.11 no.2
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• pp.151-161
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• 2009

In Location-Based Services (LBSs), users requesting a location-based query send their exact location to a database server and thus the location information of the users can be misused by adversaries. Therefore, a privacy protection method is required for using LBS in a safe way. In this paper, we propose a new cloaking region generation algorithm using grids for privacy protection in LBSs. The proposed algorithm creates a m inimum cloaking region by finding L buildings and then performs K-anonymity to search K users. For this, we make use of not only a grid-based index structure, but also an efficient pruning techniques. Finally, we show from a performance analysis that our cloaking region generation algorithm outperforms the existing algorithm in term of the size of cloaking region.

• The KIPS Transactions:PartD
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• v.10D no.3
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• pp.427-438
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• 2003

In a data warehouse, users typically search for trends, patterns, or unusual data behaviors by issuing queries interactively. The OLAP range-sum query is widely used in finding trends and in discovering relationships among attributes in the data warehouse. In a recent environment of enterprises, data elements in a data cube are frequently changed. The problem is that the cost of updating a prefix sum cube is very high. In this paper, we propose a novel algorithm which reduces the update cost significantly by an index structure called the Δ-tree. Also, we propose a hybrid method to provide either approximate or precise results to reduce the overall cost of queries. It is highly beneficial for various applications that need quick approximate answers rather than time consuming accurate ones, such as decision support systems. An extensive experiment shows that our method performs very efficiently on diverse dimensionalities, compared to other methods.

• Journal of KIISE:Databases
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• v.29 no.1
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• pp.58-71
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• 2002

Generally, multi-dimensional data such as image and spatial data require large amount of storage space. There is a limit to store and manage those large amount of data in single workstation. If we manage the data on parallel computing environment which is being actively researched these days, we can get highly improved performance. In this paper, we propose a parallel high-dimensional index structure that exploits the parallelism of the parallel computing environment. The proposed index structure is nP(processor)-n$\times$mD(disk) architecture which is the hybrid type of nP-nD and lP-nD. Its node structure increases fan-out and reduces the height of a index tree. Also, A range search algorithm that maximizes I/O parallelism is devised, and it is applied to K-nearest neighbor queries. Through various experiments, it is shown that the proposed method outperforms other parallel index structures.