• The KIPS Transactions:PartB
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• v.8B no.5
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• pp.533-538
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• 2001

In this paper, we extend FARGs single mode attribute to multiple attributes for real image application and present a new CBIR using FMARG(Fuzzy Multiple Attribute Relational Graph), which can handle queries involving multiple attributes, not only object label, but also color, texture and spatial relation. In the experiment using the synthetic image database of 1,024 images and the natural image database of 1.026 images built from NETRA database and Corel Draw, the proposed approach shows 6~30% recall increase in the synthetic image database and a good performance, at the displacements and the retrieved number of similar images in the natural image database, compared with the single attribute approach.

• Journal of KIISE:Computing Practices and Letters
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• v.6 no.2
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• pp.265-273
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• 2000

In relational database management systems(RDBMS), a table consIn relational database management systems(RDBMS), a table consists of sets of records which are composed of a set of attributes. The number of distinct values(NDV) of an attribute denotes the number of distinct attribute values that actually appear in the database records, and is widely used in optimizing queries and supporting statistic queries. Object-relational database management systems(ORBBMSS), however, support the inheritance between tables which enforces an attribute defined in a super-table to be inherited in sub-tables automatically. Hence, in ORDBMSS, not only NDV of an attribute In a single table but also NDV of an attribute in multiple tables(HNDV) is needed. In this paper, we propose a method that calculates HNDV using arrays of attribute value intervals. In this method, an array of attribute value intervals is created for an attribute of interest In each table in a table hierarchy, and HNDV can be calculated or estimated by merging the arrays of attribute value intervals. The proposed method accurately calculates HNDV using small additional storage space and is efficient for an environment where only some of the tables in a table hierarchy are frequently updated.

• Journal of Korea Society of Digital Industry and Information Management
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• v.17 no.1
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• pp.75-84
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• 2021

For analyzing big data, the social network is increasingly being utilized through relational data, which means the connection characteristics between entities such as people and objects. When the relational data does not exist directly, a social network can be configured by calculating relational data such as attribute similarity from attribute data of entities and using it as links. In this paper, the composition method of the social network using the attribute similarity between entities as a connection relationship, and the clustering method using subgroups for the configured social network are suggested, and the clustering effectiveness of the clustering results is evaluated. The analysis results can vary depending on the type and characteristics of the data to be analyzed, the type of attribute similarity selected, and the criterion value. In addition, the clustering effectiveness may not be consistent depending on the its evaluation method. Therefore, selections and experiments are necessary for better analysis results. Since the analysis results may be different depending on the type and characteristics of the analysis target, options for clustering, etc., there is a limitation. In addition, for performance evaluation of clustering, a study is needed to compare the method of this paper with the conventional method such as k-means.

• Korean Journal of Remote Sensing
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• v.3 no.2
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• pp.115-119
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• 1987

After the introduction of the topologically structured geographic information system(GIS) with relational DBMS, the attribute data can be handled without considering locational data. By utilzing of the characteristic of the relational DBMS, it can be used as an expert system building tool in GIS. The relational DBMS of the GIS furnishes the data needed to perform deductive functions of the expert system, and the rule based approach provides the decision rules. Therefore, rule based approach with the expert judgement can be easily combined with relational DBMS.

• 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.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.337-339
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• 1996

In this paper, we construct rough relational database model using approximation concepts of rough set. Also, we analyze the relation between objects, attributes and attribute values and, propose the method that can generate flexible retrieval results.

• Journal of KIISE:Software and Applications
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• v.37 no.5
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• pp.363-369
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• 2010

Demonstration-based learning has the advantage that a user can easily teach his/her robot new task knowledge just by demonstrating directly how to perform the task. However, many previous demonstration-based learning techniques used a kind of attribute-value vector model to represent their state spaces and policies. Due to the limitation of this model, they suffered from both low efficiency of the learning process and low reusability of the learned policy. In this paper, we present a new demonstration-based learning method, in which the relational model is adopted in place of the attribute-value model. Applying the relational instance-based learning to the training examples extracted from the records of the user demonstrations, the method derives a relational instance-based policy which can be easily utilized for other similar tasks in the same domain. A relational policy maps a context, represented as a pair of (state, goal), to a corresponding action to be executed. In this paper, we give a detail explanation of our demonstration-based relational policy learning method, and then analyze the effectiveness of our learning method through some experiments using a robot simulator.

• Journal of Fisheries and Marine Sciences Education
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• v.29 no.1
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• pp.242-256
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• 2017

The purpose of this study was to describe and understand the characteristics of morphological, contextual as relational aggression among 4-year old children during free play periods. The ethnographic methods included participants observation of children's play interaction behaviors, field notes, video taping and analysis of transcribed date. The results are as exclusion occurred by group power taking the relational attribute, including direct language, while the other would not be revealed easily other than being in line with real relation. Also as follows; proactive relational aggression was more frequently observed in girls, and their major strategies employed against someone they disliked were ignoring them, distorting play-rules, and so on. Major strategies of reactive relational aggression in girls were largely manipulative in nature, for example, 'threatrning their friend' and 'withdrawal of friendship'.

• Journal of Computing Science and Engineering
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• v.5 no.3
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• pp.183-196
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• 2011

There has recently been a surge of interest in relational database mining that aims to discover useful patterns across multiple interlinked database relations. It is crucial for a learning algorithm to explore the multiple inter-connected relations so that important attributes are not excluded when mining such relational repositories. However, from a data privacy perspective, it becomes difficult to identify all possible relationships between attributes from the different relations, considering a complex database schema. That is, seemingly harmless attributes may be linked to confidential information, leading to data leaks when building a model. Thus, we are at risk of disclosing unwanted knowledge when publishing the results of a data mining exercise. For instance, consider a financial database classification task to determine whether a loan is considered high risk. Suppose that we are aware that the database contains another confidential attribute, such as income level, that should not be divulged. One may thus choose to eliminate, or distort, the income level from the database to prevent potential privacy leakage. However, even after distortion, a learning model against the modified database may accurately determine the income level values. It follows that the database is still unsafe and may be compromised. This paper demonstrates this potential for privacy leakage in multi-relational classification and illustrates how such potential leaks may be detected. We propose a method to generate a ranked list of subschemas that maintains the predictive performance on the class attribute, while limiting the disclosure risk, and predictive accuracy, of confidential attributes. We illustrate and demonstrate the effectiveness of our method against a financial database and an insurance database.

• Journal of information and communication convergence engineering
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• v.20 no.4
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• pp.265-272
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• 2022

The most effective method to improve information system capabilities is to enable instant access to several relational database sources and transform data with a logical structure into multiple target relational databases. There are numerous data transformation tools available; however, they typically contain fixed procedures that cannot be changed by the user, making it impossible to fulfill the near-real-time data transformation requirements. Furthermore, some tools cannot build object references or alter attribute constraints. There are various situations in which tool changes in data type cause conflicts and difficulties with data quality while transforming between the two systems. The R-programming language was extensively used throughout this study, and several different relational database structures were utilized to complete the proposed study. Experiments showed that the developed study can improve the performance of information systems by interacting with and exchanging data with various relational databases. The study addresses data quality issues, particularly the completeness and integrity dimensions of the data transformation processes.