• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.10 no.3
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• pp.27-33
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• 2010

One of the most critical success factor in introducing and operating an integrated information system is the performance management. In this study, we propose some SQL tuning techniques, applicable to optimize the performance of relational database systems. We showed effectiveness of the techniques by applying to a database system of a medium scale company.

• 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 Korea Multimedia Society
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• v.7 no.1
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• pp.1-11
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• 2004

In this paper. we propose a storage system for XML documents using relational databases. Additional processing is required to store XML documents in the relational databases due to the discrepancy between XML structures and relational schema. This study aims to store XML documents with DTD in the relational databases. We propose the association inlining that exploits shred inlining and hybrid inlining and avoids relation fragments and excessive joins. Experiments show some improvements in the performance with the proposed method. The information of the storage structures is extracted from the simplified DTD. Existing map classes are extended in order to map various structures of XML to relational schema. Map classes are defined for various structures such as elements with multiple values, elements with multiple super elements, and elements with recursive structures through analyzing XML documents. Map files that are XML structures and used in generating SQL statements are created by using the extracted information of storage structures and map classes.

• Journal of Korea Multimedia Society
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• v.10 no.12
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• pp.1539-1550
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• 2007

As XML is increasingly used for representing and exchanging data, relational database systems have been trying extend their features to handle XML documents XML documents can be stored in a column with XML data type like primitive types. The shredding method, which is one of the traditional methods for storing and managing XML documents in RDBMS, is still useful and viable although it has some drawbacks due to the structural discrepancy between XML and relational databases. This method may be suitable for data-centric XML documents with simple schema. This paper presents the extended version of the Association inlining method that is based on inlined shredding and compares the performance of querying processing to that of XML type method of conventional relational database systems. The experiments showed that in most cases our method resulted in better performance than the other method based on XML data type. This is due to the fact that our shredding method keeps and uses the order and path information of XML documents. The path table has the information of the corresponding table and column for each distinct path and the structure information of the XML document is extracted and stored in data tables.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.3
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• pp.469-476
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• 2016

Relational Database Management Systems have become de facto database model among most developers and users since the inception of Data Science. From IoT devices, sensors, social media and other sources, data is generated in structured, semi-structured and unstructured formats, in huge volumes, thereby the difficulty of data management greatly increases. Organizations that collect large amounts of data are increasingly turning to non relational databases - NoSQL databases. In this paper, through experiments with real field data, we demonstrate that MongoDB, a document-based NoSQL database, is a better alternative for building a Telco Subscriber Data Management System which hitherto is mainly built with Relational Database Management Systems. We compare the existing system in various phases of data flow with our proposed system powered by MongoDB. We show how various workloads at some phases of the existing system were either completely removed or significantly simplified on the new system. Based on experiment results, using MongoDB for managing telco subscriber data turned out to offer performance better than the existing system built with MSSQL Server.

• Journal of Korea Trade
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• v.23 no.3
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• pp.38-51
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• 2019

Purpose - An appreciable number of small- and medium-sized exporters have continuously succeeded in their export marketplaces even though they do not possess enough resources. Advocating that affiliation with an export service-provider plays an important role in this phenomenon, we aimed to theoretically explain how export service providers' competences and relational factors drive small- and medium-sized exporters' competitive advantages in the long-run. Design/methodology - Drawing prominently on the resource-based view and the relationship-marketing theory, we built an empirically testable model. The model showed the roles of exporter capabilities, export service-provider competences, and relational factors on small- and medium-sized exporters' sustainable competitive advantages. Findings - The results of our research showed that exporter production and branding capabilities positively influenced their sustainable competitive advantage. In addition, export service-providers' marketing competence and relationship-building competence positively moderated the effects of exporter capabilities on their parties' sustainable competitive advantages. Finally, affiliation parties' interfirm trust and relationship commitment positively moderated the effects of export service-provider competence on the relationship between exporter capabilities and sustainable competitive advantages. Originality/value - Although prior studies have highlighted the effects of an exporter's resources on export performance, our research filled a knowledge gap of the effects of other resources on export performance. First, we proposed two types of export service-provider, competence marketing and relationship-building, influencing exporters' competitive advantage. Second, the effects of relational factors were proposed in the context of export affiliations.

• The Journal of the Korea Contents Association
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• v.16 no.5
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• pp.46-57
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• 2016

The purpose of this study was to identify the elements of virtuous cycle to lay a foundation for the coevolution of corporations and communities based on the entrepreneurship and social capital of social enterprises. The study first set out to add the pursuit of social goals to the components of entrepreneurship, namely innovation, progressive spirit, and risk sensitivity and categorized social capital into structural, cognitive, and relational capital. The study set six hypotheses and performed correlation and regression analysis to empirically analyze mediator effect of social capital in relationship between entrepreneurship and social enterprise performance. The findings were summarized as follows: First, relational capital had full mediated effect between entrepreneurship and economic performance of social enterprise. Second, relational capital had full mediated effect between entrepreneurship and social performance of social enterprise.

• Industry Promotion Research
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• v.9 no.3
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• pp.41-55
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• 2024

This study analyzes the impact of digital strategy on firm performance through relational behavior, cognitive dimensions, and continuous digital transformation based on social capital theory. The research model was tested using data collected from a survey of 300 domestic corporate employees who have worked for over a year, conducted from February 20 to 23, 2024, using Smart PLS 4.0. The key findings of the study are as follows: First, it was confirmed that digital strategy plays a crucial role in promoting cooperation and interaction within the organization, enhancing members' understanding and perception of digital technology, and strengthening the firm's competitiveness through continuous change and innovation. Second, continuous digital transformation and cognitive dimensions positively impact firm performance, while the influence of relational behavior dimensions was found to be insignificant. These findings suggest that digital strategy can significantly affect firm performance by fostering interaction and perception changes within the organization, beyond mere technology adoption, and provide strategic implications for Korean firms to effectively pursue digital transformation.

• Journal of Internet Computing and Services
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• v.25 no.3
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• pp.35-41
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• 2024

As online activities have rapidly increased due to recent digital changes and the impact of COVID-19, the importance of large-scale data processing and maintenance is increasing. This study compares the performance of the two main types of databases widely used for data storage and management: Relational Database Management Systems (RDBMS) and Non-Relational Databases (NoSQL). Specifically, we measured and evaluated the execution time of data insertion, query, and deletion functions using MySQL, a representative example of RDBMS, and Redis, a representative example of NoSQL. The experimental results showed that Redis showed performance about 5.84 times faster in data insertion, 6.61 times faster in query, and 12.33 times faster in deletion than MySQL. These results demonstrate that Redis provides superior performance, especially in environments requiring large-scale data processing and maintenance. Therefore, companies and online service providers can choose NoSQL databases such as Redis to ensure more efficient data management solutions. We hope this study will be an essential reference when selecting a database based on data processing performance.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.12
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• pp.485-496
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• 2018

So far, the deep learning, a field of artificial intelligence, has achieved remarkable results in solving problems from unstructured data. However, it is difficult to comprehensively judge situations like humans, and did not reach the level of intelligence that deduced their relations and predicted the next situation. Recently, deep neural networks show that artificial intelligence can possess powerful relational reasoning that is core intellectual ability of human being. In this paper, to analyze and observe the performance of Relation Networks (RN) among the neural networks for relational reasoning, two types of RN-based deep neural network models were constructed and compared with the baseline model. One is a visual question answering RN model using Sort-of-CLEVR and the other is a text-based question answering RN model using bAbI task. In order to maximize the performance of the RN-based model, various performance improvement experiments such as hyper parameters tuning have been proposed and performed. The effectiveness of the proposed performance improvement methods has been verified by applying to the visual QA RN model and the text-based QA RN model, and the new domain model using the dialogue-based LL dataset. As a result of the various experiments, it is found that the initial learning rate is a key factor in determining the performance of the model in both types of RN models. We have observed that the optimal initial learning rate setting found by the proposed random search method can improve the performance of the model up to 99.8%.