• Journal of Korea Multimedia Society
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• v.13 no.12
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• pp.1739-1747
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• 2010

The damage of natural disasters has occurred on huge scale more frequently than before. The damage caused by the disasters are summarized and are analyzed, and are managed as a very general type of documentation, not showing the position of previous damage records and related information such as weather, facilities of CCTV, hospitals, and gas station on the maps. Therefore, it needs to provide map-based searching systems considering damaged area as well as search key-words. This paper focuses on the development of a search system based on the map to manage previous disaster records and related information each disaster using spatial databases. This system consists of three modules, which are databases to store disaster data, the SQL procedure-based search module to extract needed information from the constructed databases, and the map module to express the search results on the map. This paper will contribute to provide framework of a system development for managing the disaster information according to each year and disaster based on the maps and to be utilized as the basic framework in developing damage prediction and prevention systems for disasters in future.

• Bioinformatics and Biosystems
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• v.2 no.2
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• pp.46-51
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• 2007

In the proteomics research using mass spectrometry, the protein database search gives the protein information from the peptide sequences that show the best match with the tandem mass spectra. The protein sequence database has been a powerful knowledgebase for this protein identification. However, as we accumulate the protein sequence information in the database, the database size gets to be huge. Now it becomes hard to consider all the protein sequences in the database search because it consumes much computing time. For the high-throughput analysis of the proteome, usually we have used the non-redundant refined database such as IPI human database of European Bioinformatics Institute. While the non-redundant database can supply the search result in high speed, it misses the variation of the protein sequences. In this study, we have concerned the proteomics data in the point of protein similarities and used the network analysis tool to build a new analysis method. This method will be able to save the computing time for the database search and keep the sequence variation to catch the modified peptides.

• Journal of Korean Library and Information Science Society
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• v.15
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• pp.207-243
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• 1988

This study is an attempt to furnish some helpful data for the maximum use of online information retrieval systems based on the comparative analysis of commands and operating terms employed by DIALOG, ORBIT and BRS which are three largest information retrieval systems in the world. To begin with, the historical development, service contents and retrieval functions of three systems were overviewed in the second chapter, on the basis of which the concrete functions and contents of the commands and the operating terms of the three systems were compared and analyzed one another. Commands were explained divided into basic and special ones. The basic commands subdivided into 44 items were put into the form of a diagram(Table 1). The special commands were first subdivided into search commnad, output-related commands and su n.0, pplementary commands, and then 14 items of which were comparatively analyzed and were represented as a chart(Table 2). In addition, any stand-alone commands employed by each system were also analyzed as a chart(Table 3). The Operating terms other than commands were subdivided into 16 items and were represented as a chart(Table 4). Password, inverted file, search step number in search session, etc were explained to the very detailed extent. A self-evident, single word is that the through understanding of commands is essential to the maximm use of all the functions of each of three systems.

• Journal of Korea Society of Industrial Information Systems
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• v.17 no.2
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• pp.65-72
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• 2012

In the 21st century of information systems, the amount of information resources are ever increasing and the role of information searching system is becoming criticalto easily acquire required information from the web. Generally, it requires the user to have enough pre-knowledge and superior capabilities to identify keywords of information to effectively search the web. However, most of the users undertake searching of the information without holding enough pre-knowledge and spend a lot of time associating key words which are related to their required information. Furthermore, many search engines support the keywords searching system but this only provides collection of similar words, and do not provide the user with exact relational search information with the keywords. Therefore this research report proposes a method of offering expanded user relationship search keywords by analyzing user query patterns to provide the user a system, which conveniently support their searching of the information.

• Information Systems Review
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• v.23 no.3
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• pp.77-96
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• 2021

This research investigates the pivotal role of AI-enabled technologies in vitalizing the local economy. Collaborating with a leading search engine company, we examine the direct and indirect of an AI-based location search service on the success of sampled 7,035 local restaurants in Gangnam area in Seoul. We find that increased use of AI-enabled search and recommendation services significantly improved the selections of previously less-discovered or less-popular restaurants by users, and it also enhanced the stores' overall conversion rates. The main research findings have contributions to extant literature in theorizing the value of AI applications in local economy and have managerial implications for search businesses and local stores by recommending strategic use of AI applications in their businesses that are effective in highly competitive markets.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.2
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• pp.744-768
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• 2016

As various pieces of information can be provided through the web, schemes that provide search results optimized for individual users are required in consideration of user preference. Since the existing social search schemes use users' profiles, the accuracy of the search deteriorates. They also decrease the reliability of a search result because they do not consider a search time. Therefore, a new social search scheme that considers temporal information as well as popularities and user preferences is required. In this paper, we propose a new mobile social search scheme considering popularities and user preferences based on temporal information. Popularity is calculated by collecting the visiting records of users, while user preference is generated by the actual visiting information among the search results. In order to extract meaningful information from the search target objects that have multiple attributes, a skyline processing method is used, and rank is given to the search results by combining the user preference and the popularity with the skyline processing result. To show the superiority of the proposed scheme, we conduct performance evaluations of the existing scheme and the proposed scheme.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.11
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• pp.5671-5693
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• 2017

With the rapid development of cloud computing, more and more data owners are motivated to outsource their data to cloud for various benefits. Due to serious privacy concerns, sensitive data should be encrypted before being outsourced to the cloud. However, this results that effective data utilization becomes a very challenging task, such as keyword search over ciphertexts. Although many searchable encryption methods have been proposed, they only support exact keyword search. Thus, misspelled keywords in the query will result in wrong or no matching. Very recently, a few methods extends the search capability to fuzzy keyword search. Some of them may result in inaccurate search results. The other methods need very large indexes which inevitably lead to low search efficiency. Additionally, the above fuzzy keyword search methods do not support access control. In our paper, we propose a searchable encryption method which achieves fuzzy search and access control through algorithm design and Ciphertext-Policy Attribute-based Encryption (CP-ABE). In our method, the index is small and the search results are accurate. We present word pattern which can be used to balance the search efficiency and privacy. Finally, we conduct extensive experiments and analyze the security of the proposed method.

• Journal of Intelligence and Information Systems
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• v.16 no.4
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• pp.21-41
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• 2010

This paper proposes a system that can serve users with appropriate search results through real time filtering, and implemented adaptive user profiling based personalized information retrieval system(PIRS) using users' implicit feedbacks in order to deal with the problem of existing search systems such as Google or MSN that does not satisfy various user' personal search needs. One of the reasons that existing search systems hard to satisfy various user' personal needs is that it is not easy to recognize users' search intentions because of the uncertainty of search intentions. The uncertainty of search intentions means that users may want to different search results using the same query. For example, when a user inputs "java" query, the user may want to be retrieved "java" results as a computer programming language, a coffee of java, or a island of Indonesia. In other words, this uncertainty is due to ambiguity of search queries. Moreover, if the number of the used words for a query is fewer, this uncertainty will be more increased. Real-time filtering for search results returns only those results that belong to user-selected domain for a given query. Although it looks similar to a general directory search, it is different in that the search is executed for all web documents rather than sites, and each document in the search results is classified into the given domain in real time. By applying information filtering using real time directory classifying technology for search results to personalization, the number of delivering results to users is effectively decreased, and the satisfaction for the results is improved. In this paper, a user preference profile has a hierarchical structure, and consists of domains, used queries, and selected documents. Because the hierarchy structure of user preference profile can apply the context when users perfomed search, the structure is able to deal with the uncertainty of user intentions, when search is carried out, the intention may differ according to the context such as time or place for the same query. Furthermore, this structure is able to more effectively track web documents search behaviors of a user for each domain, and timely recognize the changes of user intentions. An IP address of each device was used to identify each user, and the user preference profile is continuously updated based on the observed user behaviors for search results. Also, we measured user satisfaction for search results by observing the user behaviors for the selected search result. Our proposed system automatically recognizes user preferences by using implicit feedbacks from users such as staying time on the selected search result and the exit condition from the page, and dynamically updates their preferences. Whenever search is performed by a user, our system finds the user preference profile for the given IP address, and if the file is not exist then a new user preference profile is created in the server, otherwise the file is updated with the transmitted information. If the file is not exist in the server, the system provides Google' results to users, and the reflection value is increased/decreased whenever user search. We carried out some experiments to evaluate the performance of adaptive user preference profile technique and real time filtering, and the results are satisfactory. According to our experimental results, participants are satisfied with average 4.7 documents in the top 10 search list by using adaptive user preference profile technique with real time filtering, and this result shows that our method outperforms Google's by 23.2%.

• Proceedings of the KIEE Conference
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• 1999.07c
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• pp.1422-1424
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• 1999

This Paper Presents a efficient algorithm for loss reduction of distribution system by automatic sectionalizing switch operation in distribution systems of radial type. To apply tabu search to reconfiguration of distribution system, in this Paper we Propose the several move methods and efficient reconfiguration procedure. We also discuss the tabu search techniques of diversifying the search of solution space as well as the simple tabu search. The experimental results show that the proposed tabu search techniques have the ability to search a good solution.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.14-17
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• 1995

Various methods and techniques have been proposed for solving optimization problems; the methods have been applied to various practical problems. However the methods have demerits. The demerits which should be covered are, for example, falling into local minima, or, a slow convergence speed to optimal points. In this paper, Likelihood Search Method (L.S.M.) is proposed for searching for a global optimum systematically and effectively in a single framework, which is not a combination of different methods. The L.S.M. is a sort of a random search method (R.S.M.) and thus can get out of local minima. However exploitation of gradient information makes the L.S.M. superior in convergence speed to the commonly used R.S.M..