• Journal of Information Technology Services
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• v.13 no.3
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• pp.323-331
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• 2014

This research is based on real-time service model that uses lock screen of smart devices which is mostly exposed to device users. The potential for lock screen space is immense due to their exposing time for user. The effect can be maximized by offering useful information contents on lock screen. This service model offers real-time keyword with abridged sentence. They match real-time keyword with news by using text matching algorithm and extracts kernel sentence from news to provide short sentence to user. News from the lock screen to match real-time query sentence, and then only to the original core of the ability to move a user evaluation was conducted after adding. The report provided a key statement users feel the lack of original Not if you go to an average of 5.71%. Most algorithms allow only real-time zoom key sentence extracted keywords can accurately determine the reason for that was confirmed.

• 한국지형공간정보학회:학술대회논문집
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• 1994.11a
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• pp.32-42
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• 1994

A GIS-based regional risk analysis program to interactively study the vulnerability of bridges in a regional highway network is described. The analysis utilizes three major components. The use of a GIS system as the integrating environment to display geographic data, to handle inquiries and to display the results of a query. A risk model for bridges which can predict the level of damage due to a particular intensity of ground motion at a bridge site. A ground motion attenuation model to predict the intensity of ground motion at a particular bridge. The interactive components are supported by data files which encode characteristics such as potential earthquake sources and magnitudes, and characteristics of the bridges which are important for damage and failure analysis.

• 한국IT서비스학회:학술대회논문집
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• 2006.11a
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• pp.170-176
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• 2006

B2B registries are information systems to registrate B2B related business information such as companies' profiles, business documents, business processes, services and to provide query facilities to find information about potential business partners. In this study, we focus on the design of the repository for B2B business processes. In this paper, a metadata ontology is designed to registrate B2B business processes. In practice, there are several competitive business process definition languages such as ebXML BPSS (Business Process Specification Schema), WSBPEL (Web Service Business Process Execution Language), BPMN (Business Process Modeling Notation), and so on. In order to registrate business processes based on different representation frameworks, the proposed metadata ontology consist of three layers, common metadata, language-specific metadata, and interrelationship metadata. To implement the proposed metadata ontology using ebXML registry, metadata mapping scheme to ebRIM (ebXML Registry Information Model) are also suggested.

• Journal of Integrative Natural Science
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• v.9 no.1
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• pp.41-61
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• 2016

Non-small cell lung cancer (NSCLC) is the leading cause of cancer-related mortality worldwide. Phosphoinositide 3-kinases (PI3Ks) play important role in Non-Small Cell Lung Cancer. PI3Ks constitute a lipid kinase family which modulates the function of numerous substrates involved in the regulation of cell survival, cell cycle progression and cellular growth. Herein, we describe the ligand based pharmacophore combined with molecular docking studies methods to identify new potent PI3K inhibitors. Several pharmacophore models were generated and validated by Guner-Henry scoring Method. The best models were utilized as 3D pharmacophore query to screen against ZINC database (Chemical and Natural) and the retrieved hits were further validated by fitness score, Lipinski's rule of five. Finally four compounds were found to have good potential and they may act as novel lead compounds for PI3K inhibitor designing.

• The Journal of Society for e-Business Studies
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• v.8 no.3
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• pp.1-16
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• 2003

XML(Extensible Markup Language) is an emerging standard for data representation and exchange in e-commerce and internet-based information. However, to realize this potential, it is necessary to be able to extract structured data from XML documents and store it in a database, as well as to generate XML documents from data extracted from a database. Although many DBMS vendors are scrambling to extend their products to handle XML, there is a need for a lightweight, DBMS and platform-independent XML middleware as well. In this paper we describe such a XML2RDB middleware, that solves the following problems . generating relational schema from XML DTDs for storage of XML documents, importing data from XML documents into relational tables, creating XML documents according to a XMLQL(XML Query Language) from data extracted from a database.

• Genomics & Informatics
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• v.15 no.1
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• pp.19-27
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• 2017

Understanding complex relationships among heterogeneous biological data is one of the fundamental goals in biology. In most cases, diverse biological data are stored in relational databases, such as MySQL and Oracle, which store data in multiple tables and then infer relationships by multiple-join statements. Recently, a new type of database, called the graph-based database, was developed to natively represent various kinds of complex relationships, and it is widely used among computer science communities and IT industries. Here, we demonstrate the feasibility of using a graph-based database for complex biological relationships by comparing the performance between MySQL and Neo4j, one of the most widely used graph databases. We collected various biological data (protein-protein interaction, drug-target, gene-disease, etc.) from several existing sources, removed duplicate and redundant data, and finally constructed a graph database containing 114,550 nodes and 82,674,321 relationships. When we tested the query execution performance of MySQL versus Neo4j, we found that Neo4j outperformed MySQL in all cases. While Neo4j exhibited a very fast response for various queries, MySQL exhibited latent or unfinished responses for complex queries with multiple-join statements. These results show that using graph-based databases, such as Neo4j, is an efficient way to store complex biological relationships. Moreover, querying a graph database in diverse ways has the potential to reveal novel relationships among heterogeneous biological data.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.353-356
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• 2007

With advances in sensor technology, current researches on the pertinent techniques are actively directed toward the way which enables the USN computing service. For many applications using sensor networks, the incoming data are by nature characterized as high-speed, continuous, real-time and infinite. Due to such uniqueness of sensor data characteristics, for some instances a finite-sized buffer may not accommodate the entire incoming data, which leads to inevitable loss of data, and requirement for fast processing makes it impossible to conduct a thorough investigation of data. In addition to the potential problem of loss of data, incoming data in its raw form may exhibit high degree of complexity which evades simple query or alerting services for capturing and extracting useful information. Furthermore, as traditional mining techniques are developed to handle fixed, static historical data, they are not useful and directly applicable for analyzing the sensor data. In this paper, (1) describe how three mining techniques (sensor data outlier analysis, sensor pattern analysis, and sensor data prediction analysis) are appropriate for the USN middleware structure, with their application to the stream data in ocean environment. (2) Another proposal is a middleware structure based on USN environment adaptive to above mining techniques. This middleware structure includes sensor nodes, sensor network common interface, sensor data processor, sensor query processor, database, sensor data mining engine, user interface and so on.

• Journal of KIISE:Software and Applications
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• v.31 no.2
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• pp.126-136
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• 2004

This paper proposes a new content-based image retrieval algorithm using color-spatial information. For the purpose, the paper suggests two kinds of indexing key to prune away irrelevant images to a given query image; MCS(Main Colors' Set), which is related with color information and DBS (Distribution Block Signature), which is related with spatial information. After successively applying these filters to a database, we could get a small amount of high potential candidates that are somewhat similar to the query image. Then we would make use of new QM(Quad modeling) and relevance feedback mechanism to obtain more accurate retrieval. It would enhance the retrieval effectiveness by dynamically modulating the weights of color-spatial information. Experiments show that the proposed algorithm can apply successfully image retrieval applications.

• Journal of the Korea Society of Computer and Information
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• v.26 no.4
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• pp.63-74
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• 2021

Location-based services (LBSs) are expected to process a large number of spatial queries, such as shortest path and k-nearest neighbor queries that arrive simultaneously at peak periods. Deploying more LBS servers to process these simultaneous spatial queries is a potential solution. However, this significantly increases service operating costs. Recently, batch processing solutions have been proposed to process a set of queries using shareable computation. In this study, we investigate the problem of batch processing moving k-nearest neighbor (MkNN) queries in dynamic spatial networks, where the travel time of each road segment changes frequently based on the traffic conditions. LBS servers based on one-query-at-a-time processing often fail to process simultaneous MkNN queries because of the significant number of redundant computations. We aim to improve the efficiency algorithmically by processing MkNN queries in batches and reusing sharable computations. Extensive evaluation using real-world roadmaps shows the superiority of our solution compared with state-of-the-art methods.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.1238-1245
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• 2024

The construction industry faces the challenge of providing effective, engaging, and rule-specific safety learning. Traditional methodologies exhibit limited adaptability to technological advancement and struggle to deliver optimal learning experiences. Recently, there has been widespread adoption of information retrieval and ontology-based chatbots, as well as content delivery methods, for safety learning and education. However, existing information and content retrieval methods often struggle with accessing and presenting relevant safety learning materials efficiently. Additionally, the rigid and complex structures of ontology-based approaches pose obstacles in accommodating dynamic content and scaling for large datasets. They require more computational resources for ontology management. To address these limitations, this paper introduces iSafe Chatbot, a novel framework for construction safety learning. Leveraging Natural Language Processing (NLP) and Large Language Model (LLM), iSafe Chatbot aids safety learning by dynamically retrieving and interpreting relevant Occupational Safety and Health Administration (OSHA) rules from the comprehensive safety regulation database. When a user submits a query, iSafe Chatbot identifies relevant regulations and employs LLM techniques to provide clear explanations with practical examples. Furthermore, based on the user's query and context, iSafe Chatbot recommends training video content from video database, enhancing comprehension and engagement. Through advanced NLP, LLM, and video content delivery, iSafe Chatbot promises to revolutionize safety learning in construction, providing an effective, engaging, and rule-specific experience. Preliminary tests have demonstrated the potential of the iSafe Chatbot. This framework addresses challenges in accessing safety materials and aims to enhance knowledge and adherence to safety protocols within the industry.