• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.763-765
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• 2005

In the area of remote sensing, an immense number of images are continuously generated by various remote sensing systems. These images must then be managed by a database system efficient storage and retrieval. There are many types of image database systems, among which the content-based image retrieval (CBIR) system is the most advanced. CBIR utilizes the metadata of images including the feature data for indexing and searching images. Therefore, the performance of image retrieval is significantly affected by the storage method of the image metadata. There are many features of images such as color, texture, and shape. We mainly consider the shape feature because shape can be identified in any remote sensing while color does not always necessarily appear in some remote sensing. In this paper, we propose a metadata representation and storage method for image search based on shape features. First, we extend MPEG-7 to describe the shape features which are not defined in the MPEG-7 standard. Second, we design a storage schema for storing images and their metadata in a relational database system. Then, we propose an efficient storage method for managing the shape feature data using a Wavelet technique. Finally, we provide the performance results of our proposed storage method.

• Journal of Convergence for Information Technology
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• v.11 no.6
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• pp.24-32
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• 2021

Recently, ransomware attacks have been infected through various channels such as e-mail, phishing, and device hacking, and the extent of the damage is increasing rapidly. However, existing known malware (static/dynamic) analysis engines are very difficult to detect/block against novel ransomware that has evolved like Advanced Persistent Threat (APT) attacks. This work proposes a method for modeling ransomware malicious behavior based on graph databases and detecting novel multi-complex malicious behavior for ransomware. Studies confirm that pattern detection of ransomware is possible in novel graph database environments that differ from existing relational databases. Furthermore, we prove that the associative analysis technique of graph theory is significantly efficient for ransomware analysis performance.

• International Journal of Computer Science & Network Security
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• v.21 no.9
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• pp.247-260
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• 2021

Data modeling is a process of developing a model to design and develop a data system that supports an organization's various business processes. A conceptual data model represents a technology-independent specification of structure of data to be stored within a database. The model aims to provide richer expressiveness and incorporate a set of semantics to (a) support the design, control, and integrity parts of the data stored in data management structures and (b) coordinate the viewing of connections and ideas on a database. The described structure of the data is often represented in an entity–relationship (ER) model, which was one of the first data-modeling techniques and is likely to continue to be a popular way of characterizing entity classes, attributes, and relationships. This paper attempts to examine the basic ER modeling notions in order to analyze the concepts to which they refer as well as ways to represent them. In such a mission, we apply a new modeling methodology (thinging machine; TM) to ER in terms of its fundamental building constructs, representation entities, relationships, and attributes. The goal of this venture is to further the understanding of data models and enrich their semantics. Three specific contributions to modeling in this context are incorporated: (a) using the TM model's five generic actions to inject processing in the ER structure; (b) relating the single ontological element of TM modeling (i.e., a thing/machine or thimac) to ER entities and relationships; and (c) proposing a high-level integrated, extended ER model that includes structural and time-oriented notions (e.g., events or behavior).

• Journal of Internet Computing and Services
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• v.8 no.2
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• pp.95-103
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• 2007

Many techniques have been proposed for mapping from XML to relations, but most techniques did not negotiate the semantics of XML data. The semantics is important to validate storage, query optimization, modification anomaly in process of schema design. Specially, functional dependencies are an important part of database theory, also it is basis of normalization for relational table in BCNF. This paper propose a new technique that reflect functional dependencies to store relation mapped from XML based on XML Schema. The technique can reduce storage redundancy and can keep up content and structure with constraint described by functional dependencies.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.21 no.46
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• pp.33-49
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• 1998

In order to improve the performance of relational databases, one has to reduce the number of disk accesses necessary to transfer data from disk to main memory. The paper proposes to reduce the number of disk I/O accesses by vertically partitioning relation into fragments and allowing attribute replication to fragments if necessary. When zero-one integer programming model is solved by the branch-and-bound method, it requires much computing time to solve a large sized problem. Therefore, heuristic solutions using genetic algorithm(GA) are presented. GA in this paper adapts a few ideas which are different from traditional genetic algorithms, for examples, a rank-based sharing fitness function, elitism and so on. In order to improve performance of GA, a set of optimal parameter levels is determined by the experiment and makes use of it. As relations are vertically partitioned allowing attribute replications and saved in disk, an attribute replicating vertical partition method by GA can attain less access cost than non-attribute-replication one and require less computing time than the branch-and-bound method in large-sized problems. Also, it can acquire a good solution similar to the optimum solution in small-sized problem.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.29 no.9 s.240
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• pp.1235-1242
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• 2005

A district heating(DH) system supplies environmentally-friend heat and is appropriate for reduction of energy consumption and/or air pollutions. The DH transmission pipe, composed of supply and return pipes, has been used to transmit the heat and prevent heat loss during transportation. The two types of pipes are operated at a temperature of $75\~115^{\circ}C\;and\;40\~65^{\circ}C$, respectively, with an operating pressure of less than 1.568MPa. The objectives of this paper are to systematize data processing of transition temperature and investigate its effects on fatigue life of DH pipes. For the sake of this, about 5 millions temperature data were measured during one year at ten locations, and then available fatigue lift estimation schemes were examined and applied to quantify the specific thermal fatigue life of each pipe. As a result, a relational database management system as well as reliable fatigue lift evaluation procedures is established for Korean DH pipes. Also, since the prototypal evaluation results satisfied both cycle-based and stress-based fatigue criteria, those can be used as useful information in the future fer optimal design, operation and energy saving via setting of efficient condition and stabilization of water temperature.

• Journal of the Korea Convergence Society
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• v.12 no.7
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• pp.45-51
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• 2021

In accordance with the rapid non-face-to-face environment and mobile first strategy, the explosive increase and creation of many structured/unstructured data every year demands new decision making and services using big data in all fields. However, there have been few reference cases of using the Hadoop Ecosystem, which uses the rapidly increasing big data every year to collect and load big data into a standard platform that can be applied in a practical environment, and then store and process well-established big data in a relational database. Therefore, in this study, after collecting unstructured data searched by keywords from social network services based on Hadoop 2.0 through three virtual machine servers in the Spring Framework environment, the collected unstructured data is loaded into Hadoop Distributed File System and HBase based on the loaded unstructured data, it was designed and implemented to store standardized big data in a relational database using a morpheme analyzer. In the future, research on clustering and classification and analysis using machine learning using Hive or Mahout for deep data analysis should be continued.

• Journal of Korean Society of Archives and Records Management
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• v.19 no.1
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• pp.23-67
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• 2019

Records in Contexts (RiC) is an international archival description standard developed by integrating and normalizing four archival description standards of the International Council of Archives (ICA). RiC has the advantage of diversifying archival description, exposing the context of records, and ensuring data interoperability between disparate systems. In this study, RiC is set up as a key tool in the design of archive systems, and logical data modeling is performed to implement the database. Because of RiC's conceptual model, RiC-CM can be used as a data reference model, and which makes it possible to develop a data model that meets user requirements. Therefore, this study intends to implement these two data models: relational data model, which is widely used as the database on legacy systems, and graphical data model, which can flexibly extend objects around the relationship between information entities.

• The Journal of the Korea Contents Association
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• v.13 no.9
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• pp.410-417
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• 2013

Long-term behavior analysis considering construction sequence should be performed in the design and the actual construction of reinforced tall buildings. Most of the analytical studies on this subject, however, has not been applied directly to the structural design and the construction caused by the simple approach. As the axial force redistribution of shores and columns is time-dependent, the actual construction sequence with the placement of concrete, form removal, reshoring, shore removal, and the additional load application is very important. Object-oriented analysis program considering construction sequence, especially time-dependent deformation in early days, is developed. This system is composed of input module, database module, database store module, analysis module, and result generation module. Linkage interface between the central database and each of the related module is implemented by the visual c# concept. Graphic user interface and the relational database table are supported for user's convenience.

• Journal of Korea Spatial Information System Society
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• v.11 no.2
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• pp.162-169
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• 2009

The real-time traffic data is generated continuous and unbounded stream data type while intelligent transport system (ITS) needs to provide various and high quality services by combining with spatial information. Traditional database techniques in ITS has shortage for processing dynamic real-time stream data and static spatial data simultaneously. In this paper, we design and implement an advanced traffic monitoring system (ATMS) with the integration of existed data stream management system (DSMS) and spatial DBMS using IntraMap. Besides, the developed ATMS can deal with the stream data of DSMS, the trajectory data of relational DBMS, and the spatial data of SDBMS concurrently. The implemented ATMS supports historical and one time query, continuous query and combined query. Application programmer can develop various intelligent services such as moving trajectory tracking, k-nearest neighbor (KNN) query and dynamic intelligent navigation by using components of the ATMS.