• Korean Circulation Journal
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• v.52 no.12
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• pp.853-864
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• 2022

A retrospective observational study is one of the most widely used research methods in medicine. However, evidence postulated from a single data source likely contains biases such as selection bias, information bias, and confounding bias. Acquiring enough data from multiple institutions is one of the most effective methods to overcome the limitations. However, acquiring data from multiple institutions from many countries requires enormous effort because of financial, technical, ethical, and legal issues as well as standardization of data structure and semantics. The Observational Health Data Sciences and Informatics (OHDSI) research network standardized 928 million unique records or 12% of the world's population into a common structure and meaning and established a research network of 453 data partners from 41 countries around the world. OHDSI is a distributed research network wherein researchers do not own or directly share data but only analyzed results. However, sharing evidence without sharing data is difficult to understand. In this review, we will look at the basic principles of OHDSI, common data model, distributed research networks, and some representative studies in the cardiovascular field using the network. This paper also briefly introduces a Korean distributed research network named FeederNet.

• Journal of the Korea Society of Computer and Information
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• v.22 no.12
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• pp.101-108
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• 2017

The study proposed a system that filters the data that is entered when analyzing big data such as SNS and BLOG. Personal information includes impersonal personal information, but there is also personal information that distinguishes it from personal information, such as religious institution, personal feelings, thoughts, or beliefs. Define these personally identifiable information as sensitive information. In order to prevent this, Article 23 of the Privacy Act has clauses on the collection and utilization of the information. The proposed system structure is divided into two stages, including Big Data Processing Processes and Sensitive Information Filtering Processes, and Big Data processing is analyzed and applied in Big Data collection in four stages. Big Data Processing Processes include data collection and storage, vocabulary analysis and parsing and semantics. Sensitive Information Filtering Processes includes sensitive information questionnaires, establishing sensitive information DB, qualifying information, filtering sensitive information, and reliability analysis. As a result, the number of Big Data performed in the experiment was carried out at 84.13%, until 7553 of 8978 was produced to create the Ontology Generation. There is considerable significan ce to the point that Performing a sensitive information cut phase was carried out by 98%.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.1170-1178
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• 2022

Indoor POI data, an essential component of indoor spatial data, has attribute information of a specific place in the room and is the most critical information necessary for the user. Currently, indoor POI data is manually updated by direct investigation, which is expensive and time-consuming. Recently, research on updating POI using the attribute information of indoor photographs has been advanced to overcome these problems. However, the range of use, such as using only photographs with text information, is limited. Therefore, in this study, and to improvement this, I proposed a new method to update indoor POI data using a smartphone camera. In the proposed method, the POI name is obtained by classifying the indoor scene's photograph into artificial intelligence technology CNN and matching the location criteria to indoor spatial data through AR technology. As a result of creating and experimenting with a prototype application to evaluate the proposed method, it was possible to update POI data that reflects the real world with high accuracy. Therefore, the results of this study can be used as a complement or substitute for the existing methodologies that have been advanced mainly by direct research.

• Journal of Intelligence and Information Systems
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• v.15 no.3
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• pp.67-80
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• 2009

By a number of sensors and sensor networks, we can collect environmental information from a certain sensor space. To discover more useful information and knowledge, we want to employ data mining methodologies to sensor data stream from such sensor spaces. In this paper, we present a novel data preprocessing scheme to improve the performances of the data mining algorithms. Especially, ontologies are applied to represent meanings of the sensor data. For evaluating the proposed method, we have collected sensor streams for about 30 days, and simulated them to compare with other approaches.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.6 no.3
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• pp.241-248
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• 2005

MPEG-7 provides a lot of advantages for retrieving and editing of multimedia data by providing the information of structure and semantics in multimedia data such as digital images and audio. In this paper, a metadata generator and editor system for MPEG-7 is introduced. It suggests the overall technique and framework of generating and editing multimedia data when such content is created. This system also includes not only the functions of generating, editing and saving meta-data of MPEG-7 but also the functions of meta-data editing of moving pictures based on MPEG 1, 2 and browsing of the overall editing process.

• Korean Journal of Computational Design and Engineering
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• v.7 no.3
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• pp.157-169
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• 2002

In the initial stages of ship design, designers represent geometry, arrangement, and dimension of hull structures with 2D geometric primitives such as points, lines, arcs, and drawing symbols. However, these design information(‘2D geometric primitives’) defined in the drawing sheet require more intelligent translation processes by the designers in the next design stages. Thus, the loss of design semantics could be occurred and following design processes could be delayed. In the initial design stages, it is not easy to adopt commercial 3D CAD systems, which have been developed f3r being used in detail and production design stages, because the 3D CAD systems require detailed input for geometry definition. In this study, a semantic product model data structure was proposed, and an initial structural CAD system was developed based on the proposed data structure. Contents(‘product model data and design knowledges’) of the proposed data structure are filled with minimal input of the designers, and then 3D solid model and production material information can be automatically generated as occasion demands. Finally, the applicability of the proposed semantic product model data structure and the developed initial structural CAD system was verified through application to deadweight 300,000ton VLCC(Very Large Crude oil Carrier) product modeling procedure.

• Journal of Korea Society of Digital Industry and Information Management
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• v.5 no.2
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• pp.95-100
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• 2009

The Semantic Web is an evolving extension of the WWW in which the semantics of information and services on the web is defined, making it possible for the web to understand and satisfy the requests of people and machines to use the web content. The sementic web relied on the ontologies that structure underling data for the purpose of comprehensive and transportable machine understanding. The Semantic Web relies on the ontologies that structure underlying data for the purpose of comprehensive and transportable machine understanding. And recommendation systems have been developed as a solution to the abundance of choice people face in many situations. This paper shows that the new recommendation method is suitable for effective recommendation on the semantic web. We present a new procedure for improving the effective recommendation by using the levelized context ontology. Our experimental results also confirm that our method has good recommendation time. Our proposed method can be generalized to fit other application domains.

• Asia pacific journal of information systems
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• v.4 no.2
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• pp.3-34
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• 1994

In this paper we describe a new data modeling language we call $O^{2}LDM$. $O^{2}LDM$ incorporates features from object-oriented and logic approaches. In $O^{2}LDM$ there is a rich collection of objects organized in a type hierarchy. It is possible to compose queries that involve field selection, function application and other constructs which transcend the usual, strictly syntactic, matching of PROLOG. We give the features of $O^{2}LDM$ and motivate its utility for conceptual modeling. We have a prototype implementation for the language, which we have written in ML. In this paper we describe an executable semantics of the deductive process used in the language. We work some examples to illustrate the expressive power of the language, and compare $O^{2}LDM$ to PROLOG.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.726-729
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• 2004

Spatiotemporal phenomena are ubiquitous aspects of real world. In the spatial and temporal databases, integrity constraints maintain the semantics of specific application domain and relationship between domains when proceed update in the database. Efficient maintenance of data integrity has become a critical problem, since testing the validity of a large number of constraints in a large database and after each transaction is an expensive task. Especially, in spatiotemporal domain, data is more complex than traditional domains and very active. Additionally, it is not considered that unified frameworks deal with both spatial and temporal properties to handle integrity constraints. Therefore, there need a model to maintain integrity constraints in the unified frameworks and enforcement and management techniques in order to preserve consistence.

• Korean Journal of Computational Design and Engineering
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• v.17 no.4
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• pp.234-245
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• 2012

This paper proposes a multi-level product modeling framework for long-term lifecycle products. The framework can help engineers to define product models and relate them to physical instances. The framework is defined in three levels; data, design model, modeling language. The data level represents real-world products, The model level describes design models of real-world products. The modeling language level defines concepts and relationships to describe product design models. The concepts and relationships in the modeling language level enable engineers to express the semantics of product models in an engineering-friendly way. The interactions between these three levels are explained to show how the framework can manage long-term lifecycle product information. A prototype system is provided for further understanding of the framework.