• Journal of the Korea Society of Computer and Information
• /
• v.14 no.5
• /
• pp.167-174
• /
• 2009

The meaning of Database in order to manage the data which is huge in the meeting of the record which logically had become the fire tube or file 'efficiently' is widely used from the place which controls a many double meaning data. Like this data base it creates, it manages, the programs which send an answer back according to demand of the user as DBMS it calls. Like this it will be able to grasp the quality level of the data base software product which is important index from the research which index it buys it defined. Also, in order to produce the result of index it selects the collection item which is necessary and collection and analysis it leads and what kind of defect types occur substantially mainly, and it confirmed and the test and evaluation model in about data base software and a tentative instance it developed it analyzed.

• Journal of Intelligence and Information Systems
• /
• v.23 no.2
• /
• pp.39-70
• /
• 2017

Data-drive analytics techniques have been recently applied to public surveys. Instead of simply gathering survey results or expert opinions to research the preference for a recently launched product, enterprises need a way to collect and analyze various types of online data and then accurately figure out customer preferences. In the main concept of existing data-based survey methods, the sentiment lexicon for a particular domain is first constructed by domain experts who usually judge the positive, neutral, or negative meanings of the frequently used words from the collected text documents. In order to research the preference for a particular product, the existing approach collects (1) review posts, which are related to the product, from several product review web sites; (2) extracts sentences (or phrases) in the collection after the pre-processing step such as stemming and removal of stop words is performed; (3) classifies the polarity (either positive or negative sense) of each sentence (or phrase) based on the sentiment lexicon; and (4) estimates the positive and negative ratios of the product by dividing the total numbers of the positive and negative sentences (or phrases) by the total number of the sentences (or phrases) in the collection. Furthermore, the existing approach automatically finds important sentences (or phrases) including the positive and negative meaning to/against the product. As a motivated example, given a product like Sonata made by Hyundai Motors, customers often want to see the summary note including what positive points are in the 'car design' aspect as well as what negative points are in thesame aspect. They also want to gain more useful information regarding other aspects such as 'car quality', 'car performance', and 'car service.' Such an information will enable customers to make good choice when they attempt to purchase brand-new vehicles. In addition, automobile makers will be able to figure out the preference and positive/negative points for new models on market. In the near future, the weak points of the models will be improved by the sentiment analysis. For this, the existing approach computes the sentiment score of each sentence (or phrase) and then selects top-k sentences (or phrases) with the highest positive and negative scores. However, the existing approach has several shortcomings and is limited to apply to real applications. The main disadvantages of the existing approach is as follows: (1) The main aspects (e.g., car design, quality, performance, and service) to a product (e.g., Hyundai Sonata) are not considered. Through the sentiment analysis without considering aspects, as a result, the summary note including the positive and negative ratios of the product and top-k sentences (or phrases) with the highest sentiment scores in the entire corpus is just reported to customers and car makers. This approach is not enough and main aspects of the target product need to be considered in the sentiment analysis. (2) In general, since the same word has different meanings across different domains, the sentiment lexicon which is proper to each domain needs to be constructed. The efficient way to construct the sentiment lexicon per domain is required because the sentiment lexicon construction is labor intensive and time consuming. To address the above problems, in this article, we propose a novel product reputation mining algorithm that (1) extracts topics hidden in review documents written by customers; (2) mines main aspects based on the extracted topics; (3) measures the positive and negative ratios of the product using the aspects; and (4) presents the digest in which a few important sentences with the positive and negative meanings are listed in each aspect. Unlike the existing approach, using hidden topics makes experts construct the sentimental lexicon easily and quickly. Furthermore, reinforcing topic semantics, we can improve the accuracy of the product reputation mining algorithms more largely than that of the existing approach. In the experiments, we collected large review documents to the domestic vehicles such as K5, SM5, and Avante; measured the positive and negative ratios of the three cars; showed top-k positive and negative summaries per aspect; and conducted statistical analysis. Our experimental results clearly show the effectiveness of the proposed method, compared with the existing method.

• Journal of Distribution Science
• /
• v.22 no.5
• /
• pp.1-10
• /
• 2024

Purpose: The study aims to identify the key factors that influence consumers' propensity to utilize community group buying platforms, employing the Technology Acceptance Model (TAM) as a theoretical framework. Research design, data and methodology: The research design involved selecting 192 consumers with experience in community group buying and analyzing the data statistically using SPSS 23.0. Hypotheses were tested utilizing the structural equation modeling software AMOS. Results: Key findings indicate that the attributes of products offered on community group buying platforms significantly enhance consumers' perceptions of usefulness and ease of use. Furthermore, these perceptions directly correlate with consumers' intentionsto use the platform. Conclusions: Thisresearch, grounded in the TAM, delves into how external factors of the community group buying platform impact perceived usefulness and ease of use, and subsequently, how these perceptions affect consumers' purchasing intentions. Based on these insights, several recommendations can be proposed for the platform's development: The platform should strive to enhance product quality and cultivate a positive reputation. Strategic promotional initiatives should be designed to attract new users while retaining existing customers. Continuous optimization of platform functionalities is necessary to augment users' perception of usefulness. These measures are anticipated to foster user engagement, increase adoption rates, and contribute to the overall success and sustainability of the community group buying platform.

• The KIPS Transactions:PartD
• /
• v.11D no.1
• /
• pp.133-142
• /
• 2004

The next generation of web applications will need to be larger, more complex, and flexible Agent-oriented systems have great potential for these e-commerce applications. Agents can dynamically discover and compose e-services and mediate interactions. Development of software agents with CBD (Component Based Development) has proved to be successful in increasing speed to market of development Projects, lowering the development cost and providing better qualify. In this thesis, we propose a systemic development process for software agents using component and UML (Unified Modeling Language). We suggest a etA (e-business Agent) CBD reference architecture for layer the related components through identification and classification of general agent and e-business agent. We also propose the ebA-CBD process that is a guideline to consider the best features of existing agent oriented software engineering methodologies, while grounding agent-oriented concepts in the same underlying semantic framework used by UML. We first developed the agent components specification and modeled it with Goal, Role, Interaction, and Architecture Model. Based on this, we developed e-CPIMAS (e-Commerce Product Information Mailing Agent System) as a case study that provides the product information's mailing service according to proposed process formality. We finally describe how these concepts may assist in increasing the efficiency reusability, productivity and quality to develop the business application and e-business agent.

• Journal of Distribution Science
• /
• v.17 no.10
• /
• pp.61-71
• /
• 2019

Purpose - This study aims to explore the impact of factors on Korean consumer goods purchase decision of Vietnam's Generation Z. Research design, data, and methodology - A mixed research method was utilized in this study including focus group, in-depth interview, pilot study, and official study. The conceptual model and hypothesis were tested using data collected cross-sectional by questionnaire, from a sample of 439 respondents, by both electronic and paper surveys with non-probability and convenience sampling. The SPSS 20 and AMOS 20 software were employed to analyze the data. Results - Results showed that Vietnam's Generation Z was strongly impacted by social media, Hallyu, country of origin, social norms, and perceived usefulness. Besides, Korean consumer goods purchase decision of Vietnam's Generation Z also were impacted by intermediary factors such as trust, social norms, product involvement, perceived quality, perceived usefulness, attitude, and buying intention. There were differences in factors affecting the purchase decision of the boy and girl Generation Z group. Conclusions - The factors impacting on Korean consumer goods decision of Vietnam's Generation Z are very important for Korean firms and government. The findings provide Korean firms opportunity for appropriate to be carried out factors impacting Korean consumer goods to generation Z in Vietnam successful.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.39B no.8
• /
• pp.518-527
• /
• 2014

This study is to estimate the learning curve based on the consequences of reduced voice of customer from each telecommunication service products. We used Exponential Decay Model, which is the most popular among the learning curve models. We attempted to add how VOC changes in accordance with seasonal factors, human resource input, application of software, and the investment. The results of the empirical analysis of each service product as follows: First, as learning curve, customer complaints decreased. Second, human resource input, Network fault make increase or decrease customer complaints(VOC). Third, even though increasing the customer's quality of experience, VOC would not decrease due to service paradox.

• KIISE Transactions on Computing Practices
• /
• v.20 no.12
• /
• pp.664-669
• /
• 2014

As the prevalence of cloud computing increases, various cloud service types have emerged, such as IaaS, PaaS, and SaaS. The growth and diversification of these cloud services has also resulted in the development of technology for cloud service brokers (CSBs), which serve as intermediate cloud services that can assist cloud tenants (users) in deploying services that fit their requirements. In order to broker cloud services, CSBs require the specification of structural models in order to facilitate the analysis and search for cloud services. In this study, we propose a variability-based service analysis model (SAM) that can be used to describe various cloud services. This model is based on the concept of variability in the software product line and represents the commonality and variability of cloud services by binding variants to each variation point that exists in the specification, quality, and pricing of the services. We also propose a virtual cloud bank architecture as a CSB that serves as an intermediate to provides tenants with appropriate cloud services based on the SAM.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.24 no.4
• /
• pp.57-64
• /
• 2024

The introduction of Smart Factories has transformed manufacturing towards more objective and efficient line management. However, most companies are not effectively utilizing the vast amount of sensor data collected every second. This study aims to use this data to predict product quality and manage production processes efficiently. Due to security issues, specific sensor data could not be verified, so semiconductor process-related training data from the "SAMSUNG SDS Brightics AI" site was used. Data preprocessing, including removing missing values, outliers, scaling, and feature elimination, was crucial for optimal sensor data. Oversampling was used to balance the imbalanced training dataset. The SVM (rbf) model achieved high performance (Accuracy: 97.07%, GM: 96.61%), surpassing the MLP model implemented by "SAMSUNG SDS Brightics AI". This research can be applied to various topics, such as predicting component lifecycles and process conditions.

• Knowledge Management Research
• /
• v.24 no.1
• /
• pp.31-49
• /
• 2023

TikTok, as a leading short video platform, has attracted many users, and the resulting attention generates immense business value as a platform to diffuse knowledge. As a qualitative and explorative approach, this study reviews the knowledge payment industry and discusses the influential factors of users' payment decisions regarding knowledge products on TikTok. By conducting in-depth interviews with ten participants and observing 95 knowledge providers' videos, we find that TikTok has significant business potential in the knowledge payment industry. By using the ATLAS. ti software to code the data collected from these interviews, this study finds that demander characteristics (personal needs), product characteristics (product quality), provider characteristics (the key opinion leader effect), and platform characteristics (platform management) are the four core categories that influence users' payment decisions regarding knowledge products on TikTok. A theoretical model consisting of the ten variables of emotional needs, professional needs, quality, price, helpfulness, value, charisma, user trust, service guarantee, and scarcity is proposed based on the grounded theory. The theoretical and practical implications of the study findings are also discussed.

• Journal of Technology Innovation
• /
• v.23 no.3
• /
• pp.287-316
• /
• 2015

The trend of R&D in automotive electronics industry is shifting towards ECU(Electronic Control Units) based on softwares which requires technology convergence to accommodate customers' requests on safety and convenience. The trend requires systemized R&D paradigm which reflects increased role of softwares. As the softwares became the core components in automotive innovation, there has been wide range of efforts to introduce software R&D processes and methodologies such as CMMI, A-SPICE and ISO-26262 etc. However, R&D departments in the industry fields are confronted with conflicts which arise from discrepancies among the individual process. In this study, we focus on suggesting our integrated and systematic R&D process with the aim of alleviating the conflicts and confusions. For this purpose, we analyze the cases of Korean automotive electronics companies to compare various R&D processes in the field and their relationships. Based on the analysis, we derive and suggest our model of R&D process which effectively integrate ISO/TS-16949 for manufacturing quality and CMMI, A-SPICE, ISO-26262 for system with softwares.