• Journal of Information Technology Services
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• v.8 no.4
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• pp.141-150
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• 2009

There are no objective standards for selection and improvement of SLA evaluation metrics for IT service. In this study, we analyze the current IT maturity models for selection and improvement of the metrics and then we derive them according to the maturity levels and propose the redesigned maturity model. To verify whether the model is applicable, we execute a case study based on the D company. We apply the proposed evaluation metrics of the maturity models to the D company and evaluate the metrics. We select a proper level of the D company and an improvement line after measuring evaluation metrics in the maturity level 2. We propose improvement guidelines of evaluation metrics which score is less than the improvement line's and derive SLA evaluation metrics. By using the SLA evaluation metrics for a year, we prove that the way of selection and improvement is useful.

• International Journal of Computer Science & Network Security
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• v.22 no.2
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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.

• The KIPS Transactions:PartD
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• v.16D no.4
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• pp.541-548
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• 2009

Many companies have recently apply SLA and execute IT service by using SLA. However, there are no objective standards for selection and improvement of SLA evaluation metrics. We derive and present measurement attributes that are criteria for selection and improvement of SLA evaluation metrics as measurement metrics. We execute a case study based on D company in order to verify whether the measurement metrics are applicable. We apply and evaluate the measurement metrics that are applicable to D company, and then we designate an improvement line. We propose improvement guidelines of the measurement metrics which score is less than the improvement line's and derive SLA evaluation metrics. We prove that the way of selection and improvement is useful by applying SLA evaluation metrics to D company.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.33 no.1
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• pp.80-90
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• 2010

Performance management is very popular in business area and seems to be an exciting topic. Despite significant research efforts and myriads of performance metrics, performance management today as a rigorous approach is still in an immature state and metrics are often selected based on intuitive and heuristic approach. In a R&D sector, the difficulty to select the proper performance metrics is even more increasing due to the natural characteristics of R&D such as unique or domain-specific problems. In this paper, we present a way of presenting R&D performance framework using ontology language. Based on this, the specific metrics can be derived by reusing or inheriting the context in the framework. The proposed ontological framework is formalized using OWL(Ontology Web Language) and metrics selection rules satisfying the characteristics of R&D are represented in SWRL(Semantic Web Rule Language). Actual metrics selection procedure is carried out using JESS rule engine, a plug-in to Prot$\acute{e}$g$\acute{e}$, and illustrated with an example, incorporating a prevalent R&D performance model : TVP(Technology Value Pyramid).

• KIISE Transactions on Computing Practices
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• v.24 no.1
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• pp.46-52
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• 2018

Software metrics as a way to evaluate software play a significant role in reducing software development costs and improving quality. However, the emergence of various software metrics creates the problem that the user must select the correct metric. Various strategies have been studied to solve this problem. However, existing studies still have difficulties in selecting metrics by requiring high user interventions. Therefore, in this paper, we propose a method that helps to select the right metric and the metric tools by using their various characteristics as selection criteria, instead of using weighted expressions to minimize user intervention.

• Journal of Applied Reliability
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• v.16 no.2
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• pp.134-146
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• 2016

Purpose: Many domestic companies have to make out quantitative evaluation table in their proposal when they conduct the software R&D project. However, most of companies have a difficulty to select the evaluation items and criteria, also to derive a quantitative results. Therefore, we propose a method to derive the quantitative evaluation index by utilizing the ISO/IEC 9126-2. Methods: Analyzing ISO/IEC 9126-2, and we classify the quality metrics as high-classification and sub-classification for Web/App software, Embedded software and Installation software. Next, Conduct the metrics selection survey depending on importance and necessity. Then, carry out the case study. Verify the correspondence between evaluation items and criteria from original suggestion of company and from outcome by utilizing the ISO/IEC 9126-2 quality metrics. Results: It is possible to classify into two metrics, one for common software or one another for only special software. Furthermore, there is quality metrics that is more important and more necessary depending upon characteristics of the software. Conclusion: ISO/IEC 9126-2 quality metrics can be used to make an evaluation items and criteria for quantitative evaluation table of software product.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.14 no.1
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• pp.79-90
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• 2003

• Journal of Digital Convergence
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• v.10 no.8
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• pp.39-44
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• 2012

The objective of this paper is to provide the conceptual selection framework of SLA metrics to maximize the operation efficiency and satisfaction of IT outsourcing and how to select most efficient auto service call center and system. With these metrics, both customers and service providers can measure service performance of IT outsourcing service. Hence, it is expected to boost operation efficiency and customers' satisfaction. In that sense, this study gives the value to both outsourcing and outsourced companies through suggesting the proper SLA metrics selection framework which provides the standards of service performance measurement and the management of IT outsourcing service in accordance with their business strategy quantitatively and qualitatively. Also we perform a survey for two customers in real business to prove the logicality of this selection framework is working and to find out relationship between SLA practice and customers' satisfaction while they outsource their IT service.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.6
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• pp.3074-3091
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• 2019

With the widespread use of the Internet, services for providing large-capacity multimedia data such as video-on-demand (VOD) services and video uploading sites have greatly increased. VOD service providers want to be able to provide users with high-quality keyframes of high quality videos within a few minutes after the broadcast ends. However, existing keyframe extraction tends to select keyframes whose quality as a keyframe is insufficiently considered, and it takes a long computation time because it does not consider an HD class image. In this paper, we propose a keyframe selection method that flexibly applies multiple keyframe quality metrics and improves the computation time. The main procedure is as follows. After shot boundary detection is performed, the first frames are extracted as initial keyframes. The user sets evaluation metrics and priorities by considering the genre and attributes of the video. According to the evaluation metrics and the priority, the low-quality keyframe is selected as a replacement target. The replacement target keyframe is replaced with a high-quality frame in the shot. The proposed method was subjectively evaluated by 23 votes. Approximately 45% of the replaced keyframes were improved and about 18% of the replaced keyframes were adversely affected. Also, it took about 10 minutes to complete the summary of one hour video, which resulted in a reduction of more than 44.5% of the execution time.

• Asia-Pacific Journal of Business
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• v.2 no.2
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• pp.1-35
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• 2011

Facing information overload in today's complex environments, managers look to a concise set of marketing metrics to provide direction for marketing decision making. While there have been several papers dealing with the theoretical aspects of dashboard creation, no research creates and tests a dashboard using scientific techniques. This study develops and demonstrates an empirical approach to dashboard metric selection. In a fast moving consumer goods category, this research selects leading indicators for national-brand and store-brand sales and revenue premium performance from 99 brand-specific and relative-to-competition variables including price, brand equity, usage occasions, and multiple measures of awareness, trial/usage, purchase intent, and liking/satisfaction. Plotting impact size and wear-in time reveals that different kinds of variables predict sales at distinct lead times, which implies that managerial action may be taken to turn the metrics around before performance itself declines.