• Journal of Industrial Technology
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• v.21 no.A
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• pp.155-166
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• 2001

This paper deals with the subsequence searching problem under time-warping. Our work is motivated by the observation that subsequence searches slow down quadratically as the average length of data sequences increases. To resolve this problem, the Segment-Based Approach for Subsequence Searches (SBASS) is proposed. The SBASS divides data and query sequences into a series of segments, and retrieves all data subsequences. Our segmentation scheme allows segments to have different lengths; thus we employ the time warping distance as a similarity measure for each segment pair. For efficient retrieval of similar subsequences, we extract feature vectors from all data segments exploiting their monotonically changing properties, and build a spatial index using feature vectors. The effectiveness of our approach is verified through extensive experiments.

• Journal of Korea Multimedia Society
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• v.20 no.5
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• pp.791-799
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• 2017

With the development of the internet of things, sensor and device are can be applied to various scenario. Overall improving of the dependability index of internet of things is the ultimate goal. And reliability aims to increase the success rate of internet of things service delivery. Many studies about internet of things system have been made on the system to assess a dependability for providing reliable service to user, but it has difficult to reflect the user context for evaluating the device reliability. Also, most do not consider the availability of content information. In this paper, it proposed dependable device searching system in the internet of things environment. This system evaluates device dependability based on device status and measured data. Through the proposed system, it can be provided reliable context information for user-centric service.

• Journal of KIISE:Databases
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• v.31 no.6
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• pp.650-663
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• 2004

In molecular biology, DNA sequence searching is one of the most crucial operations. Since DNA databases contain a huge volume of sequences, a fast indexing mechanism is essential for efficient processing of DNA sequence searches. In this paper, we first identify the problems of the suffix tree in aspects of the storage overhead, search performance, and integration with DBMSs. Then, we propose a new index structure that solves those problems. The proposed index consists of two parts: the primary part represents the trie as bit strings without any pointers, and the secondary part helps fast accesses of the leaf nodes of the trio that need to be accessed for post processing. We also suggest an efficient algorithm based on that index for DNA sequence searching. To verify the superiority of the proposed approach, we conducted a performance evaluation via a series of experiments. The results revealed that the proposed approach, which requires smaller storage space, achieves 13 to 29 times performance improvement over the suffix tree.

• Proceedings of the Korea Contents Association Conference
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• 2007.11a
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• pp.346-349
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• 2007

In this paper, the integrated search system made for the web news and review retrieval service is introduced. We made XSLTRobot that extract title, date, author and content from html document like news or reviews for search service. XSLTRobot used the XSLT technology in order to extract desired part of html page. The Intergrated Information Retrieval System(IIRS) is suitable for various search data format. And we introduce Dynamic Index Storage which is module of IIRS. Dynamic Index Storage is used to environment which needs fast index update like news. And it's design focused on retrieval performance because there was not many document that it has to update on a real time.

• Journal of Internet Computing and Services
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• v.11 no.1
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• pp.15-20
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• 2010

In this paper, we propose a novel approach to reduce spatial query access latency and energy consumption by leveraging results from nearby peers in wireless broadcast environments. We propose a three-tier Hierarchical Location-Based Sequential access index, called HLBS, which provides selective tuning (pruning and searching entries) without pointers using a linear accessing structure based on the location of each data object. The HLBS saves search cost and index overhead, since the small index size with a sequential index structure results in low access latency overhead and facilitates efficient searches for sequential-access media (wireless channels with data broadcast). Comprehensive experiments illustrate that the proposed scheme is more efficient than the previous techniques in terms of energy consumption.

• Korea Trade Review
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• v.44 no.1
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• pp.269-283
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• 2019

Due to environmental issues such as global warming, the importance of renewable energy is growing. Solar Power System is one of the most growing eco-friendly energy industries in the world, but Korea's solar energy industry faces fierce competition due to the trade regulations and changes in energy related laws in the major markets such as the U.S., EU and China. Therefore, Korea needs to diversify its export markets towards emerging markets. This paper analyzed 162 countries in the world and developed a model to measure how promising the countries are. GSMI(Grid connected Solar Market Index) and OSMI(Off-grid Solar Market Index) are invented based on the models. By using the developed model and the data of 162 countries over the 15-year period from 2000 to 2014, the foreign markets are ranked for searching the export market. According to the analysis, China, Japan, U.S, India and Taiwan ranked first to fifth in GSMI and OSMI ranking, which were followed by China, India, Bangladesh, Philippines and Afghanistan. The model developed through this research is expected to provide a more reasonable and scientific approach to the advancement of the Korean solar energy industry into overseas markets.

• The KIPS Transactions:PartD
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• v.9D no.5
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• pp.779-788
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• 2002

There are many technically difficult issues in searching multimedia data such as image, video and audio because they are massive and more complex than simple text-based data. As a method of searching multimedia data, a similarity retrieval has been studied to retrieve automatically basic features of multimedia data and to make a search among data with retrieved features because exact match is not adaptable to a matrix of features of multimedia. In this paper, data clustering and its indexing are proposed as a speedy similarity-retrieval method of multimedia data. This approach clusters similar images on adjacent disk cylinders and then builds Indexes to access the clusters. To minimize the search cost, the hashing is adapted to index cluster. In addition, to reduce I/O time, the proposed searching takes just one I/O to look up the location of the cluster containing similar object and one sequential file I/O to read in this cluster. The proposed schema solves the problem of multi-dimension by using clustering and its indexing and has higher search efficiency than the content-based image retrieval that uses only clustering or indexing structure.

• Journal of the Korean Society for information Management
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• v.33 no.3
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• pp.177-193
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• 2016

This study aims to analyze physicians' online information search process to solve the clinical questions at the point of care. To achieve this purpose, ten university hospital-based physicians participated in-depth interviews and observation studies. Based on Wilson's problem solving process, this study analyzed the characteristics of each information search stage and efficiency of online searching. The results showed that participants tend to relatively immediately formulate their clinical questions. However, basic searching strategies were only used and a few preferred information sources were chosen. However, average satisfaction degree of online searching appeared high with 5.7 (7 Likert-scale) and problem-solving index increased after searching. As physicians are likely to use well organized and evidenced-based credible information easily, it implies the needs for an integrated search system within the electronic medical record (EMR). In addition, as other online resources' awareness is lower comparing Google and PubMed, active promotions and training of other resources are needed.

• The Journal of Society for e-Business Studies
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• v.18 no.2
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• pp.153-173
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• 2013

The ontology has been gaining increasing interests by recent arise of the semantic web and related technologies. The focus is mostly on inference query processing that requires high-level techniques for storage and searching ontologies efficiently, and it has been actively studied in the area of semantic-based searching. W3C's recommendation is to use RDFS and OWL for representing ontologies. However memory-based editors, inference engines, and triple storages all store ontology as a simple set of triplets. Naturally the performance is limited, especially when a large-scale ontology needs to be processed. A variety of researches on proposing algorithms for efficient inference query processing has been conducted, and many of them are based on using proven relational database technology. However, none of them had been successful in obtaining the complete set of inference results which reflects the five characteristics of the ontology properties. In this paper, we propose a new index structure called hyper cube index to efficiently process inference queries. Our approach is based on an intuition that an index can speed up the query processing when extensive inferencing is required.

• Journal of KIISE:Databases
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• v.32 no.4
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• pp.362-371
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• 2005

Similarity indexing and searching are well known to be difficult in high-dimensional applications such as multimedia databases. Especially, they become more difficult when multiple features have to be indexed together. In this paper, we propose a novel indexing method called the GB-index that is designed to efficiently handle complex similarity queries as well as relevance feedback in high-dimensional image databases. In order to provide the flexibility in controlling multiple features and query objects, the GB-index treats each dimension independently The efficiency of the GB-index is realized by specialized bitmap indexing that represents all objects in a database as a set of bitmaps. Main contributions of the GB-index are three-fold: (1) It provides a novel way to index high-dimensional data; (2) It efficiently handles complex similarity queries; and (3) Disjunctive queries driven by relevance feedback are efficiently treated. Empirical results demonstrate that the GB-index achieves great speedups over the sequential scan and the VA-file.