• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.7B
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• pp.605-619
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• 2003

The main memory DBMS(MMDBMS) efficiently supports various database applications that require high performance since it employs main memory rather than disk as a primary storage. In this paper, we discuss the index manager of the Tachyon, a next-generation MMDBMS. Recently, the gap between the CPU processing and main memory access times is becoming much wider due to rapid advance of CPU technology. By devising data structures and algorithms that utilize the behavior of the cache in CPU, we are able to enhance the overall performance of MMDBMSs considerably. In this paper, we address the practical implementation issues and our solutions for them obtained in developing the cache-conscious index manager of the Tachyon. The main issues touched are (1) consideration of the cache behavior, (2) compact representation of the index entry and the index node, (3) support of variable-length keys, (4) support of multiple-attribute keys, (5) support of duplicated keys, (6) definition of the system catalog for indexes, (7) definition of external APIs, (8) concurrency control, and (9) backup and recovery. We also show the effectiveness of our approach through extensive experiments.

• Journal of Digital Convergence
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• v.12 no.6
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• pp.311-316
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• 2014

Social network server due to the popularity of smart phones, and data stored in a big usable access data services are increasing. Big Data Big Data processing technology is one of the most important technologies in the service, but a solution to this minor security state. In this paper, the data services provided by the big -sized data is distributed using a double hash user to easily access to data of multiple distributed hash chain based data processing technique is proposed. The proposed method is a kind of big data data, a function, characteristics of the hash chain tied to a high-throughput data are supported. Further, the token and the data node to an eavesdropper that occurs when the security vulnerability to the data attribute information to the connection information by utilizing hash chain of big data access control in a distributed processing.

• Convergence Security Journal
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• v.16 no.6_2
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• pp.83-88
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• 2016

Over recent years there has been considerable growth in interest in the use of biometric systems for personal authentication. Biometrics is a field of technology which has been and is being used in the identification of individuals based on some physical attribute. By using biometrics, authentication is directly linked to the person, rather than their token or password. Biometric authentication is a type of system that relies on the unique biological characteristics of individuals to verify identity for secure access to electronic systems. In 2013, Althobati et al. proposed an efficient remote user authentication protocol using biometric information. However, we uncovered Althobati et al.'s protocol does not guarantee its main security goal of mutual authentication. We showed this by mounting threat of data integrity and bypassing the gateway node attack on Althobati et al.'s protocol. In this paper, we propose an improved scheme to overcome these security weaknesses by storing secret data in device. In addition, our proposed scheme should provide not only security, but also efficiency since sensors in WSN(Wireless Sensor Networks) operate with resource constraints such as limited power, computation, and storage space.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.11
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• pp.3183-3192
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• 2009

The purpose of this paper is to introduce Naming application model for sensor networks. Currently, sensor networks comprised of sensor nodes have provided an application range which could not function before. However, unlike general network, current sensor networks are designed to cooperate with major wireless-capable sensor devices with limited resources. Thus, exporting/importing between individual sensor and current sensor networks is very inefficient and unstable. Attribute, schema and DIT(Directory Information Tree) must be designed for sensor network using SN LDAP application model in order to maintain transparency and provide constant service in a situation of data defect. With the system explained as above, Naming application model is made to manage SN Fuzzy Query. It shall be more efficient and stable structure as long as Naming application using a virtual equation in a certain environment with information collected from sensor node is provided. In this paper, I would like to introduce SN Fuzzy LDAP model for sensor network by quick Naming method. Also, naming application which is possible for fuzzy query in a certain environment based on the system will be proved.

• Journal of Digital Contents Society
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• v.16 no.1
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• pp.33-41
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• 2015

Since bitmap indices are useful for OLAP queries over low-cardinality data columns, they are frequently used in data warehouses. In many data warehouse applications, the domain of a column tends to be hierarchical, such as categorical data and geographical data. When the domain of a column is hierarchical, hierarchical bitmap index is able to significantly improve the performance of queries with conditions on that column. This strategy, however, has a limitation in that when a large scale hierarchy is used, building a bimamp for each distinct node leads to a large space overhead. Thus, in this paper, we introduce the way to build hierarchical bitmap index on an attribute whose domain is organized into a large-scale hierarchy in space-constrained environments. Especially, in order to figure out space overhead of hierarchical bitmap indices, we propose the cut-selection strategy which divides the entire hierarchy into two exclusive regions.

• Journal of the Korea Society of Computer and Information
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• v.15 no.8
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• pp.149-156
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• 2010

As following the development of the USN technology, sensor node used in sensor network has capability of quick data process and storage to support efficient network configuration is enabled. In addition, tree-based structure was transformed to cluster in the construction of sensor network. However, query processing based on existing tree structure could be inefficient under the cluster-based network. In this paper, we suggest energy efficient query processing mechanism using filtering through data attribute classification in cluster-based sensor network. The suggestion mechanism use advantage of cluster-based network so reduce energy of query processing and designed more intelligent query dissemination. And, we prove excellence of energy efficient side with MATLab.

• The Journal of the Convergence on Culture Technology
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• v.10 no.1
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• pp.471-476
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• 2024

With the spread of various smart devices and computing devices, big data generation is occurring widely. Machine learning is an algorithm that performs reasoning by learning data patterns. Among the various machine learning algorithms, the algorithm that attracts attention is deep learning based on neural networks. Deep learning is achieving rapid performance improvement with the release of various applications. Recently, among deep learning algorithms, attempts to analyze data using graph structures are increasing. In this study, we present a graph generation method for transferring to a deep learning network. This paper proposes a method of generalizing node properties and edge weights in the graph generation process and converting them into a structure for deep learning input by presenting a matricization We present a method of applying a linear transformation matrix that can preserve attribute and weight information in the graph generation process. Finally, we present a deep learning input structure of a general graph and present an approach for performance analysis.

• Journal of the Korean Geographical Society
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• v.39 no.5 s.104
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• pp.804-818
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• 2004

As spatial data resources become more abundant, the potential for conflict among them increases. Those conflicts can exist between two or many spatial datasets covering the same area and categories. Therefore, it becomes increasingly important to be able to effectively relate these spatial data sources with others then create new spatial datasets with matching geometry and topology. One extensive spatial dataset is US Census Bureau's TIGER file, which includes census tracts, block groups, and blocks. At present, however, census maps often carry information that conflicts with municipally-maintained detailed spatial information. Therefore, in order to fully utilize census maps and their valuable demographic and economic information, the locational information of the census maps must be reconciled with the more accurate municipally-maintained reference maps and imagery. This paper formulates a conceptual framework and two map models of map conflation to make geometrically and topologically consistent source maps according to the reference maps. The first model is based on the cell model of map in which a map is a cell complex consisting of 0-cells, 1-cells, and 2-cells. The second map model is based on a different set of primitive objects that remain homeomorphic even after map generalization. A new hierarchical based map conflation is also presented to be incorporated with physical, logical, and mathematical boundary and to reduce the complexity and computational load. Map conflation principles with iteration are formulated and census maps are used as a conflation example. They consist of attribute embedding, find meaning node, cartographic 0-cell match, cartographic 1-cell match, and map transformation.

• Journal of the Korean Society for information Management
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• v.40 no.1
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• pp.121-148
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• 2023

The purpose of this study is to propose a scholarly paper recommendation system based on metadata attribute similarity with excellent performance. This study suggests a scholarly paper recommendation method that combines techniques from two sub-fields of Library and Information Science, namely metadata use in Information Organization and co-citation analysis, author bibliographic coupling, co-occurrence frequency, and cosine similarity in Bibliometrics. To conduct experiments, a total of 9,643 paper metadata related to "inequality" and "divide" were collected and refined to derive relative coordinate values between author, keyword, and title attributes using cosine similarity. The study then conducted experiments to select weight conditions and dimension numbers that resulted in a good performance. The results were presented and evaluated by users, and based on this, the study conducted discussions centered on the research questions through reference node and recommendation combination characteristic analysis, conjoint analysis, and results from comparative analysis. Overall, the study showed that the performance was excellent when author-related attributes were used alone or in combination with title-related attributes. If the technique proposed in this study is utilized and a wide range of samples are secured, it could help improve the performance of recommendation techniques not only in the field of literature recommendation in information services but also in various other fields in society.

• Journal of Bio-Environment Control
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• v.25 no.3
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• pp.212-217
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• 2016

This study was conducted to investigate the effect of nursery period on growth and yield attribute of green papaya (var. Red lady). The nursery period was 3, 5, 7, 9, 11 and 13 months and the green papaya was transplanted on 15 April, 2015 in a non-heated greenhouse. The plant height, node number and fresh weight of nursery plant were increased as the nursery periods increased. The growth of green papaya with 13 months nursery period was better than those of other treatments. First harvest after transplanting was increased as the nursery periods were shorten. It took 137 days (18 August) at 13 months treatment, and 184 days (2 October) at 3 months treatment. The fruit length and diameter were smallest at 3 months treatment and there was no significant difference among other treatments. The fruit yield was also influenced by the nursery periods, the commercial yield was also increased as the nursery periods increased. The commercial yield was highest at 13 months treatment (3,172kg/10a), followed by 11 (2,247kg/10a) and 9 months treatment (2,357kg/10a). At 7 and 5 months treatment were 1,942kg/10a and 1,787kg/10a, respectively and the yield was lowest at 3 months treatment (1,443kg/10a). The commercial yield was significantly decreased under 7 months treatment. Although the harvest time of 11 months treatment was earlier than that of other treatments in non-heated greenhouse, 9 month treatment will be more recommendable for green papaya production because of operating costs.