• Convergence Security Journal
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• v.24 no.1
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• pp.35-42
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• 2024

Multi-tenancy architecture plays a crucial role in cloud-based services and applications, and data isolation within such environments has emerged as a significant security challenge. This paper investigates various data isolation methods including schema-based isolation, logical isolation, and physical isolation, and compares their respective advantages and disadvantages. It evaluates the practical application and effectiveness of these data isolation methods, proposing security considerations and selection criteria for data isolation in the development of multi-tenant websites. This paper offers important guidance for developers, architects, and system administrators aiming to enhance data security in multi-tenancy environments. It suggests a foundational framework for the design and implementation of efficient and secure multi-tenant websites. Additionally, it provides insights into how the choice of data isolation methods impacts system performance, scalability, maintenance ease, and overall security, exploring ways to improve the security and stability of multi-tenant systems.

• Journal of the Korean Society for Library and Information Science
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• v.58 no.2
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• pp.269-288
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• 2024

To structure the KOS registry, it is necessary to select a classification system that suits the characteristics of the collected KOS. This study aimed to classify domestic KOS collected through various classification schems, and based on these results, provide insights for selecting a classification system when structuring the KOS registry. A total of 313 KOS data collected via web searches were categorized using five types of classification systems and a thesaurus, and the results were analyzed. The analysis indicated that for international linkage of the KOS registry, foreign classification systems should be applied, and for optimization with domestic knowledge resources or to cater to domestic researchers, domestic classification systems need to be applied. Additionally, depending on the field-specific characteristics of the KOS, research area KOS should apply classification systems based on academic disciplines, while public sector KOS should consider classification systems based on government functions. Lastly, it is necessary to strengthen the linkage between domestic and international KOS, which also requires the application of multiple classification systems.

• Journal of Korean Library and Information Science Society
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• v.55 no.2
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• pp.109-132
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• 2024

As the bibliographic environment has been evolved towards linked data and semantic web, revision of KCR5 based on RDA is also in progress in Korea. Even authorized access points play an important role in identification and linkage between resources in the evolving bibliographic environment. However, the original RDA applied by KCR5 does not provide authorized access points for all entities. Based on the analysis of the authorized access point of manifestation in RDA 2020, this research identified properties and proposed a linking structure of the authorized access point that can be applied to the revision of KCR5. An authorized access point for manifestation is an access point that considers both intellectual and physical aspects, and can be the foundation for linking and identifying actual resources. The proposed structure is expected to be a linking structure for authority record optimized to the current bibliographic environment, which can be helpful in the practical application of the authorized access point for manifestation.

• Korean Journal of Ecology and Environment
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• v.49 no.4
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• pp.279-288
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• 2016

Stable Isotope Analysis(SIA) of carbon and nitrogen is useful tool for the understanding functional roles of target organisms in biological interactions in the food web. Recently, mixing model based on SIA is frequently used to determine which of the potential food sources predominantly assimilated by consumers, however, application of model is often limited and difficult for non-expert users of software. In the present study, we suggest easy manual of R software and package SIAR with example data regarding selective feeding of crustaceans dominated freshwater zooplankton community. We collected SIA data from the experimental mesocosms set up at the littoral area of eutrophic Chodae Reservoir, and analyzed the dominant crustacean species main food sources among small sized particulate organic matters (POM, <$50{\mu}m$), large sized POM (>$50{\mu}m$), and attached POM using mixing model. From the results obtained by SIAR model, Daphnia galeata and Ostracoda mainly consumed small sized POM while Simocephalus vetulus consumed both small and large sized POM simultaneously. Copepods collected from the reservoir showed no preferences on various food items, but in the mesocosm tanks, main food sources for the copepods was attached POM rather than planktonic preys including rotifers. The results have suggested that their roles as grazers in food web of eutrophicated reservoirs are different, and S. vetulus is more efficient grazer on wide range of food items such as large colony of phytoplankton and cyanobacteria during water bloom period.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.3
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• pp.669-678
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• 2018

This study developed information technology infrastructures for building a driving environment analysis platform using various big data, such as vehicle sensing data, public data, etc. First, a small platform server with a parallel structure for big data distribution processing was developed with H/W technology. Next, programs for big data collection/storage, processing/analysis, and information visualization were developed with S/W technology. The collection S/W was developed as a collection interface using Kafka, Flume, and Sqoop. The storage S/W was developed to be divided into a Hadoop distributed file system and Cassandra DB according to the utilization of data. Processing S/W was developed for spatial unit matching and time interval interpolation/aggregation of the collected data by applying the grid index method. An analysis S/W was developed as an analytical tool based on the Zeppelin notebook for the application and evaluation of a development algorithm. Finally, Information Visualization S/W was developed as a Web GIS engine program for providing various driving environment information and visualization. As a result of the performance evaluation, the number of executors, the optimal memory capacity, and number of cores for the development server were derived, and the computation performance was superior to that of the other cloud computing.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.3
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• pp.830-860
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• 2022

[Introduction] Nowadays, many companies are shifting their businesses online due to the growing trend among customers to buy and shop online, as people prefer online purchasing products. [Problem] Users share a vast amount of information about products, making it difficult and challenging for the end-users to make certain decisions. [Motivation] Therefore, we need a mechanism to automatically analyze end-user opinions, thoughts, or feelings in the social media platform about the products that might be useful for the customers to make or change their decisions about buying or purchasing specific products. [Proposed Solution] For this purpose, we proposed an automated SentiDecpective approach, which classifies end-user reviews into negative, positive, and neutral sentiments and identifies deceptive crowd-users rating information in the social media platform to help the user in decision-making. [Methodology] For this purpose, we first collected 11781 end-users comments from the Amazon store and Flipkart web application covering distant products, such as watches, mobile, shoes, clothes, and perfumes. Next, we develop a coding guideline used as a base for the comments annotation process. We then applied the content analysis approach and existing VADER library to annotate the end-user comments in the data set with the identified codes, which results in a labelled data set used as an input to the machine learning classifiers. Finally, we applied the sentiment analysis approach to identify the end-users opinions and overcome the deceptive rating information in the social media platforms by first preprocessing the input data to remove the irrelevant (stop words, special characters, etc.) data from the dataset, employing two standard resampling approaches to balance the data set, i-e, oversampling, and under-sampling, extract different features (TF-IDF and BOW) from the textual data in the data set and then train & test the machine learning algorithms by applying a standard cross-validation approach (KFold and Shuffle Split). [Results/Outcomes] Furthermore, to support our research study, we developed an automated tool that automatically analyzes each customer feedback and displays the collective sentiments of customers about a specific product with the help of a graph, which helps customers to make certain decisions. In a nutshell, our proposed sentiments approach produces good results when identifying the customer sentiments from the online user feedbacks, i-e, obtained an average 94.01% precision, 93.69% recall, and 93.81% F-measure value for classifying positive sentiments.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.47-63
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• 2015

As knowledge is paid attention as a new production factor that generates added value, studies continue to apply knowledge management to business environment. In addition, as ICT (Information Communication Technology) was engrafted in business environment, it leads to increasing task efficiency and productivity of individual workers. Accordingly, the way that a business achieves its goal has changed to one in which its individual members are willing to take part in the organization and share information to create new values (Han, 2003) and studies for the system and service to support such transition are carrying out. Of late, a new concept called 'Enterprise 2.0' newly appears. It is the extension of Wen 2.0 and its technology, which focus on participation, sharing and openness, to the work environment of a business (Jung, 2013). Enterprise 2.0 is being used as a collaborative tool to prop up individual creativity and group brain power by combining Web 2.0 technologies such as blog, Wiki, RSS and tag with business software (McAfee, 2006). As Tweeter gets popular, Enterprise Microblog (EMB), which is an example of Enterprise 2.0 for business, has been developed as equivalent to Tweeter in business circle and SaaS (Software as a Service) such as Yammer was introduced The studies of EMB mainly focus on demonstrating its usability in terms of intra-firm communication and knowledge management. However existing studies lean too much towards large-sized companies and certain departments, rather than a company as a whole. Therefore, few studies have been conducted on small and medium-sized companies that have difficulty preparing separate resources and supplying exclusive workforce to introduce knowledge management. In this respect, the present study placed its analytic focus on small-sized companies actually equipped with EMB to know how they use it. And, based on the findings, this study examined their knowledge management strategies for EMB from the point of codification and personalization. Hypothesis -"as a company grows, it shifts EMB strategy from codification to personalization'- was established on the basis of reviewing precedent studies and literature. To demonstrate the hypothesis, this study analyzed the usage of EMB by small companies that have used it from foundation. For case study, the duration of the use was divided into 2 spans and longitudinal analysis was employed to examine the contents of the blogs. Using the key findings of the analysis, this study is aimed to propose practical implications for the operation of knowledge management of small-sized company and the suitable application of knowledge management system for operation Knowledge Management Strategy can be classified by codification strategy and personalization strategy (Hansen et. al., 1999), and how to manage the two strategies were always studied. Also, current studies regarding the knowledge management strategy were targeted mostly for major companies, resulting in lack of studies in how it can be applied on SMEs. This research, with the knowledge management strategy suited for SMEs, sets an Enterprise Microblog (EMB), and with the EMB applied on SMEs' Knowledge Management Strategy, it is reviewed on the perspective of SMEs' Codification and Personalization Strategies. Through the advanced research regarding Knowledge Management Strategy and EMB, the hypothesis is set that "Depending on the development of the company, the main application of EMB alters from Codification Strategy to Personalization Strategy". To check the hypothesis, SME that have used the EMB called 'Yammer' was analyzed from the date of their foundation until today. The case study has implemented longitudinal analysis which divides the period when the EMBs were used into three stages and analyzes the contents. As the result of the study, this suggests a substantial implication regarding the application of Knowledge Management Strategy and its Knowledge Management System that is suitable for SME.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.1-25
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• 2020

In this paper, we suggest an application system architecture which provides accurate, fast and efficient automatic gasometer reading function. The system captures gasometer image using mobile device camera, transmits the image to a cloud server on top of private LTE network, and analyzes the image to extract character information of device ID and gas usage amount by selective optical character recognition based on deep learning technology. In general, there are many types of character in an image and optical character recognition technology extracts all character information in an image. But some applications need to ignore non-of-interest types of character and only have to focus on some specific types of characters. For an example of the application, automatic gasometer reading system only need to extract device ID and gas usage amount character information from gasometer images to send bill to users. Non-of-interest character strings, such as device type, manufacturer, manufacturing date, specification and etc., are not valuable information to the application. Thus, the application have to analyze point of interest region and specific types of characters to extract valuable information only. We adopted CNN (Convolutional Neural Network) based object detection and CRNN (Convolutional Recurrent Neural Network) technology for selective optical character recognition which only analyze point of interest region for selective character information extraction. We build up 3 neural networks for the application system. The first is a convolutional neural network which detects point of interest region of gas usage amount and device ID information character strings, the second is another convolutional neural network which transforms spatial information of point of interest region to spatial sequential feature vectors, and the third is bi-directional long short term memory network which converts spatial sequential information to character strings using time-series analysis mapping from feature vectors to character strings. In this research, point of interest character strings are device ID and gas usage amount. Device ID consists of 12 arabic character strings and gas usage amount consists of 4 ~ 5 arabic character strings. All system components are implemented in Amazon Web Service Cloud with Intel Zeon E5-2686 v4 CPU and NVidia TESLA V100 GPU. The system architecture adopts master-lave processing structure for efficient and fast parallel processing coping with about 700,000 requests per day. Mobile device captures gasometer image and transmits to master process in AWS cloud. Master process runs on Intel Zeon CPU and pushes reading request from mobile device to an input queue with FIFO (First In First Out) structure. Slave process consists of 3 types of deep neural networks which conduct character recognition process and runs on NVidia GPU module. Slave process is always polling the input queue to get recognition request. If there are some requests from master process in the input queue, slave process converts the image in the input queue to device ID character string, gas usage amount character string and position information of the strings, returns the information to output queue, and switch to idle mode to poll the input queue. Master process gets final information form the output queue and delivers the information to the mobile device. We used total 27,120 gasometer images for training, validation and testing of 3 types of deep neural network. 22,985 images were used for training and validation, 4,135 images were used for testing. We randomly splitted 22,985 images with 8:2 ratio for training and validation respectively for each training epoch. 4,135 test image were categorized into 5 types (Normal, noise, reflex, scale and slant). Normal data is clean image data, noise means image with noise signal, relfex means image with light reflection in gasometer region, scale means images with small object size due to long-distance capturing and slant means images which is not horizontally flat. Final character string recognition accuracies for device ID and gas usage amount of normal data are 0.960 and 0.864 respectively.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.7
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• pp.4573-4579
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• 2014

In the 21st century, university education is changing from an objective knowledge and information to critical thinking and problem-solving ability. Moreover, university education should change rapidly towards a learner-centered educational environment because it has an educational goal to have college students experience authentic tasks they will be in charge of after graduation, and improves self-directed learning ability and cooperative learning ability. PBL is a pedagogical strategy for posing significant, contextualized, real world situations, and providing resources, guidance, and instruction to learners as they develop content knowledge and problem-solving skills. In problem based learning, the students collaborate to study the issues of a problem as they strive to create viable solution. For these advantages of PBL, the application of PBL in school has been enlarged. On the other hand, the application of PBL in engineering education has not been enlarged. To improve these instruction methods, the development or applications of new instructional methods will be needed. This study examined the PBL instructional design of a creative engineering design subject, which aims to foster talent. The PBL model developed in this study consists of Analysis, Design, Development, Implementation, and Evaluation. A plan of creative engineering design subject was developed based on PBL, and focused on the process of PBL. To determine the effects of this model, studies applying this instructional design to many lecturers should be implemented.

• Journal of KIISE:Databases
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• v.34 no.6
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• pp.528-545
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• 2007

Recently ontology has been an attractive technology along with the business strategy of providing a plenty of more intelligent services. The essential problem in application domains using ontology is that all members, agents, and application programs in the domains must share the same ontology concepts. However, a variety of mobile devices, sensing devices, and network components manufactured by various companies, a variety of common carriers, and a variety of contents providers make multiple heterogeneous ontologies more likely to coexist. We can see many past researches fallen into resolving this semantic interoperability. Such methods can be broadly classified into by-mapping, by-merging, and by-translation. In this research, we focus on by-translation among them which uses a translation rule directly made between two heterogeneous ontology data like OntoMorph. However, the manual composition of the direct translation rule is not convenient by itself and if there are N ontologies, the direct method has the rule composition complexity of $O(N^2)$ in the worst case. Therefore, in this paper we introduce the cascade composition of translation rules based on web openness in order to improve the complexity. The research result made us recognize some important factors in an ontology translation system, that is speediness of translation, and conveniency of translation rule composition, and some experiments and comparing analysis with existing methods showed that our cascade method has more conveniency with insuring the speediness and the correctness.