• 인터넷정보학회논문지
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• 제19권5호
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• pp.107-113
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• 2018

Object-oriented databases (OODBs) canbe used for many non-traditional database application areas such as computer-aided design, etc. Usually those application areas require advanced modeling power for expressing complicated relationships among data sets. OODBs have more distinguished features than the traditional relational database systems. One of the distinguished characteristics of OODBs is class hierarchy (also called inheritance hierarchy). A class hierarchy in an OODB means that a class can hand down the definitions of the class to the subclass of the class. In other words, a class is allowed to inherit the definitions of the class from the superclass. In this paper, we present performance evaluation metrics for class hierarchy in OODBs from a concurrency control perspective. The proposed performance metrics are developed to determine which concurrency control scheme in OODBs can be used for a given class hierarchy. In this study, in order to develop performance metrics, we use class hierarchy structure (both of single inheritance and multiple inheritance), and data access frequency for each class. The proposed performance metrics will be also used to compare performance evaluation for various concurrency control techniques.

• Journal of Information Science Theory and Practice
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• 제10권4호
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• pp.53-65
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• 2022

Altmetrics or alternative metrics gauge the digital attention received by scientific outputs from the web, which is treated as a supplement to traditional citation metrics. In this study, we performed a meta-analysis of correlations between classic citation metrics and altmetrics indicators of library and information science (LIS) articles. We followed the systematic review method to select the articles and Erasmus Rotterdam Institute of Management Guidelines for reporting the meta-analysis results. To select the articles, keyword searches were conducted on Google Scholar, Scopus, and ResearchGate during the last week of November 2021. Eleven articles were assessed, and eight were subjected to meta-analysis following the inclusion and exclusion criteria. The findings reported negative and positive associations between citations and altmetric indicators among the selected articles, with varying correlation coefficient values from -.189 to 0.93. The result of the meta-analysis reported a pooled correlation coefficient of 0.47 (95% confidence interval, 0.339 to 0.586) for the articles. Sub-group analysis based on the citation source revealed that articles indexed on the Web of Science showed a higher pooled correlation coefficient (0.41) than articles indexed in Google Scholar (0.30). The study concluded that the pooled correlation between citation metrics with altmetric indicators was positive, ranging from low to moderate. The result of the study gives more insights to the scientometrics community to propose and use altmetric indicators as a proxy for traditional citation indicators for quick research impact evaluation of LIS articles.

• Journal of Ecology and Environment
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• 제34권2호
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• pp.237-249
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• 2011

Land-use changes due to the socio-economic environment influence landscape patterns and processes, which affect habitats and biodiversity. This study considers the effects of such land-use changes, particularly on the traditional rural "Maeul" forested landscape, by analyzing landscape structure and vegetation changes. Three study areas were examined that have seen their populations decrease and age over the last few decades. Five types of plant life-forms (Raunkier life-forms) were distinguished to investigate ecosystem function. Principle component analysis was used to understand vegetation dynamics and community characteristics based on a vegetation similarity index. Ordination analysis transformed species-coverage data was introduced to clarify vegetation dynamics. Landscape indices, such as area metrics, edge metrics, and shape metrics, showed that spatial heterogeneity has increased over time in all areas. Pinus densiflora was the main land-use plant type in all study areas but decreased over time, whereas Quercus spp. increased. Over a decade, P. densiflora communities shifted to deciduous oak and plantation. These findings indicate that the impact of human activities on the Maeul landscape is twofold. While forestry activities caused heavy disturbances, the abandonment of traditional human activities has led to natural succession. Furthermore, it can be concluded that the type and intensity of these human impacts on landscape heterogeneity relate differently to vegetation succession. This reflects the cause and consequence of patch dynamics. We discuss an approach for sustainable landscape planning and management of the Maeul landscape based on traditional management.

• International Journal of Computer Science & Network Security
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• 제22권2호
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• pp.232-240
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• 2022

To fulfill user expectations, the rapid evolution of software techniques and approaches has necessitated reliable and flawless software operations. Aging prediction in the software under operation is becoming a basic and unavoidable requirement for ensuring the systems' availability, reliability, and operations. In this paper, an improved evolutionary computing-driven extreme learning scheme (ECD-ELM) has been suggested for object-oriented software aging prediction. To perform aging prediction, we employed a variety of metrics, including program size, McCube complexity metrics, Halstead metrics, runtime failure event metrics, and some unique aging-related metrics (ARM). In our suggested paradigm, extracting OOP software metrics is done after pre-processing, which includes outlier detection and normalization. This technique improved our proposed system's ability to deal with instances with unbalanced biases and metrics. Further, different dimensional reduction and feature selection algorithms such as principal component analysis (PCA), linear discriminant analysis (LDA), and T-Test analysis have been applied. We have suggested a single hidden layer multi-feed forward neural network (SL-MFNN) based ELM, where an adaptive genetic algorithm (AGA) has been applied to estimate the weight and bias parameters for ELM learning. Unlike the traditional neural networks model, the implementation of GA-based ELM with LDA feature selection has outperformed other aging prediction approaches in terms of prediction accuracy, precision, recall, and F-measure. The results affirm that the implementation of outlier detection, normalization of imbalanced metrics, LDA-based feature selection, and GA-based ELM can be the reliable solution for object-oriented software aging prediction.

• 지식경영연구
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• 제11권4호
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• pp.21-39
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• 2010

Recommender system is a core component of e-commerce. Correspondingly, metrics to evaluate the system performance have been developed and applied. However, even though we have lots of applications that have tried to adopt recommender systems, the dearth of successfully installed recommender systems for more than a decade leads us to a skeptical thinking that current metrics do not sufficiently indicate the recommender system success in business viability point of view. Hence, the purpose of this paper is to reconfigure measures for recommender system success. Adopting DeLone and McLean's amended model of information system success as the underlying framework, content analysis with intellectual properties on recommender systems was conducted to modify the currently used metrics. Then a model of recommender system success is proposed based on the newly identified metrics are compared with traditional metrics.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2019년도 춘계학술발표대회
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• pp.79-81
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• 2019

Collecting data in wireless sensor networks in minimum time is a traditional problem which is known NP-hard. Previous studies built the schedule using the node-based or link-based metrics to prioritize the transmissions. In this work, we combine the effect of both metrics to obtain a smaller aggregation time. We compare our work with state of the art schemes and report the improvement.

• 스마트미디어저널
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• 제12권2호
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• pp.66-75
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• 2023

최근 기업 및 공공기관 정보시스템의 클라우드 전환이 가속화되면서 클라우드 환경에서 운영되는 웹 애플리케이션이 증가하고 있다. 클라우드 웹 애플리케이션에 대한 전통적인 네트워크 공격은 대량의 패킷으로 네트워크 자원을 고갈시키는 DoS(Denial of Service) 공격이 대표적이지만, 최근에는 애플리케이션 자원을 고갈시키는 HTTP DoS 공격도 증가하고 있어 이에 대응하기 위한 보안기술 마련이 필요하다. 특히, HTTP DoS 공격 중, 저대역폭으로 수행되는 공격은 네트워크 자원을 고갈시키지 않기 때문에 네트워크 메트릭을 모니터링 하는 전통적인 보안 솔루션으로 탐지하는 것이 어렵다. 본 논문에서는 클라우드 웹 애플리케이션에 HTTP DoS 공격을 주입하면서 웹 서버의 애플리케이션 메트릭을 수집하고, 이를 머신러닝 기반으로 학습하여 공격을 탐지하는 새로운 탐지 모델을 제안한다. 애플리케이션 메트릭으로는 아파치 웹 서버의 18종을 수집하였고, 5종의 머신러닝 모델과 2종의 딥러닝 모델을 사용하여 수집한 데이터를 학습하였다. 또한, 6종의 네트워크 메트릭을 추가로 수집 및 학습하고, 제안된 애플리케이션 메트릭 기반 모델과 성능을 비교함으로써 애플리케이션 메트릭 기반 머신러닝 모델의 우수성을 검증한다. HTTP DoS 공격 중, 저대역폭으로 수행되는 RUDY 공격과 고대역폭으로 수행되는 HULK 공격을 제안된 모델로 탐지한 결과, 두 공격 탐지에 있어서 애플리케이션 메트릭 기반 머신러닝 모델의 F1-Score가 네트워크 메트릭 기반의 모델보다 각각 약 0.3, 0.1 높은 것을 확인하였다.

• Journal of information and communication convergence engineering
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• 제15권3호
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• pp.165-169
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• 2017

Altmetrics is an emergent research area whereby social media is applied as a source of metrics to evaluate scientific impact. Recently, the interest in altmetrics has been growing. Traditional scientific impact evaluation indictors are based on the number of publications, citation counts and peer reviews of a researcher. As research publications were increasingly placed online, usage metrics as well as webometrics appeared. This paper explores the potential benefits of altmetrics and the deep relationship between each metrics. Firstly, we found a weak-to-medium correlation among the 11 altmetrics and visualized such correlation. Secondly, we conducted principal component analysis and exploratory factor analysis on altmetrics of social media, divided the 11 altmetrics into four feature sets, confirming the dispersion and relative concentration of altmetrics groups and developed the altmetrics evaluation model. We can use this model to evaluate the scientific impact of articles on social media.

• 대한인간공학회지
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• 제34권3호
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• pp.235-245
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• 2015

Objective: The purpose of this study is to develop visual complexity metrics based on theoretical bases. Background: With the development of IT technologies, drivers process a large amount of information caused by automotive human-machine interface (HMI), such as a cluster, a head-up display, and a center-fascia. In other words, these systems are becoming more complex and dynamic than traditional driving systems. Especially, these changes can lead to the increase of visual demands. Thus, a concept and tool is required to evaluate the complicated systems. Method: We reviewed prior studies in order to analyze the visual complexity. Based on complexity studies and human perceptual characteristics, the dimensions characterizing the visual complexity were determined and defined. Results: Based on a framework and complexity dimensions, a set of metrics for quantifying the visual complexity was developed. Conclusion: We suggest metrics in terms of perceived visual complexity that can evaluate the in-vehicle displays. Application: This study can provide the theoretical bases in order to evaluate complicated systems. In addition, it can quantitatively measure the visual complexity of In-vehicle information system and be helpful to design in terms of preventing risks, such as human error and distraction.

• 산업경영시스템학회지
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• 제38권3호
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• pp.159-168
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• 2015

Aggregate Production Planning determines levels of production, human resources, inventory to maximize company's profits and fulfill customer's demands based on demand forecasts. Since performance of aggregate production planning heavily depends on accuracy of given forecasting demands, choosing an accurate forecasting method should be antecedent for achieving a good aggregate production planning. Generally, typical forecasting error metrics such as MSE (Mean Squared Error), MAD (Mean Absolute Deviation), MAPE (Mean Absolute Percentage Error), and CFE (Cumulated Forecast Error) are utilized to choose a proper forecasting method for an aggregate production planning. However, these metrics are designed only to measure a difference between real and forecast demands and they are not able to consider any results such as increasing cost or decreasing profit caused by forecasting error. Consequently, the traditional metrics fail to give enough explanation to select a good forecasting method in aggregate production planning. To overcome this limitation of typical metrics for forecasting method this study suggests a new metric, WACFE (Weighted Absolute and Cumulative Forecast Error), to evaluate forecasting methods. Basically, the WACFE is designed to consider not only forecasting errors but also costs which the errors might cause in for Aggregate Production Planning. The WACFE is a product sum of cumulative forecasting error and weight factors for backorder and inventory costs. We demonstrate the effectiveness of the proposed metric by conducting intensive experiments with demand data sets from M3-competition. Finally, we showed that the WACFE provides a higher correlation with the total cost than other metrics and, consequently, is a better performance in selection of forecasting methods for aggregate production planning.