• International journal of advanced smart convergence
• /
• v.8 no.3
• /
• pp.161-166
• /
• 2019

Viewed in the rate of change in the web environment, it is very difficult to remain the initial planning at the time until the time of launch, and there is a need for a method to accommodate changes and satisfy market demands during the development process. Unlike the traditional waterfall approach of maintaining initial planning, scrum is one of the agile methodologies that enables flexibility to respond to changes in the market and customers' needs and drive customer satisfaction and business success. However, to apply the scrum to a project in actual, the practice method itself is relatively simple but not easy to apply. The reason is that the members of the organization need to understand and participate in scrum's philosophy and principles and the continuous observation and change management should be carried out. Therefore, in this paper, we presented the feature dashboard and customized scrum methodology to enable continuous observation and change management using visibility, and we shared the case that periodically reflected inspection and adaptation with the explanation of the main points. Also, based on the experience with participants, the strengths and weakness of the feature dashboard and the customized scrum methodology are summarized.

• The Journal of Bigdata
• /
• v.1 no.2
• /
• pp.9-19
• /
• 2016

Since ICT convergence became a major issue, German government has carried forward a policy 'Industry 4.0' that triggered ICT convergence with manufacturing. Now this trend gets into our stride. From this facts, we can expect great leap up to quality perfection in low cost. Recently Korean government also enforces policy with 'Manufacturing 3.0' for upgrading Korean manufacturing industry with being accelerated by many related technologies. We, in the paper, developed a custom-made operational big data analysis platform for the implementation of operational intelligence to improve industry capability. Our platform is designed based on spring framework and web. In addition, HDFS and spark architectures helps our system analyze massive data on the field with streamed data processed by process mining algorithm. Extracted knowledge from data will support enhancement of manufacturing performance.

• Korean Journal of Construction Engineering and Management
• /
• v.10 no.2
• /
• pp.14-25
• /
• 2009

Many companies have managed their business performance in order to achieve their enterprise purpose. Performance management which applied concept of BSC (Balanced Scorecard) is widely used all over the world. In the construction industry, BSC-based performance management is currently introduced with needs of balanced performance evaluation. However, most companies actually have intermediate level of adapting BSC. It is important to understand its process or and structure. Therefore, this paper is focused on making performance management process and defining each phase of it. In addition, the model and system are established with putting them together. With developing performance process in construction, the construction companies are supposed to detect the deficiencies of the current performance management systems and take some opportunity to be helped for supporting their decision-making. In conclusion, this paper will provide the construction industry with the opportunities to enhance the values of performance management system and construction application.

• International Journal of Computer Science & Network Security
• /
• v.22 no.2
• /
• pp.406-412
• /
• 2022

Storing large amounts of data has always been a big problem from the beginning of computing history. Big Data has made huge advancements in improving business processes by finding the customers' needs using prediction models based on web and social media search. The main purpose of big data stream processing frameworks is to allow programmers to directly query the continuous stream without dealing with the lower-level mechanisms. In other words, programmers write the code to process streams using these runtime libraries (also called Stream Processing Engines). This is achieved by taking large volumes of data and analyzing them using Big Data frameworks. Streaming platforms are an emerging technology that deals with continuous streams of data. There are several streaming platforms of Big Data freely available on the Internet. However, selecting the most appropriate one is not easy for programmers. In this paper, we present a detailed description of two of the state-of-the-art and most popular streaming frameworks: Apache Ignite and Hazelcast. In addition, the performance of these frameworks is compared using selected attributes. Different types of databases are used in common to store the data. To process the data in real-time continuously, data streaming technologies are developed. With the development of today's large-scale distributed applications handling tons of data, these databases are not viable. Consequently, Big Data is introduced to store, process, and analyze data at a fast speed and also to deal with big users and data growth day by day.

• IE interfaces
• /
• v.25 no.1
• /
• pp.52-63
• /
• 2012

Up-to-date enlargement of the scale of global outsourcing has brought about the need of systematic and efficient tools for competitive supplier discovery located in various areas. A web-based business supporting system, referred to as Excellent Manufacturer Scouting System(EMSS), is being developed to serve core business functions including supplier discovery, negotiation, and collaboration between overseas buyers and domestic suppliers throughout the process of supply chain formation. In this paper, a supplier assessment model devoted to evaluation of core manufacturing capability is proposed by targeting small and medium sized mold companies. The assessment model will eventually be loaded to EMSS. Even if many well-designed models for supplier assessment have been presented in literature, most of them limit the evaluation criteria to somewhat general information on a given supplier, such as cost, delivery time, quality, rather than core manufacturing capability itself. This research is pioneering work on supplier assessment from the viewpoint of manufacturability. The proposed assessment model classifies assessment indices into six criteria, which have been drawn by intensive survey and analysis of the mold industry. Actual assessment indices for each criterion are also presented along with an exemplary evaluation result.

• The Journal of the Korea Contents Association
• /
• v.17 no.7
• /
• pp.1-11
• /
• 2017

The best business strategy leading to innovation and productivity can be achieved by carefully analyzing internal and external environments of a company. Many companies often require, but difficult to find a tool to determine their own internal/external environmental factors including strengths, weaknesses, opportunities and threats(SWOT). SWOT is one analytical base model that is utilized in this research to design semi-automated environmental analysis process. This study investigates on SWOT generation system that is built on existing analysis database created by experts in each field. Companies can search and choose their best expressing environmental elements that are stored in the database. This semi-automated SWOT tool is expected to contribute that companies can recognize their internal capabilities more accurately, and help consider external environment changes around them.

• Asia pacific journal of information systems
• /
• v.19 no.1
• /
• pp.1-31
• /
• 2009

This paper empirically examines factors that potentially influence the success of a Web-based semantic search engine. A research model has been proposed that shows the impact of quality-related factors upon the effectiveness of a semantic search engine, based on DeLone and McLean's(2003) information systems success model. An empirical study has been conducted to test hypotheses formulated around the research model, and statistical methods were applied to analyze gathered data and draw conclusions. Implications for academics and practitioners are offered based on the findings of the study. The proposed model includes three quality dimensions of a Web-based semantic search engine-namely, information quality, system quality and service quality. These three dimensions each have measures designed to collectively assess the respective dimension. The model is intended to examine the relationship between measures of these quality dimensions and measures of two dependent constructs, including individuals' net benefit and user satisfaction. Individuals' net benefit was measured by the extent to which the user's information needs were adequately met, whereas user satisfaction was measured by a combination of the perceived satisfaction with search results and the perceived satisfaction with the overall system. A total of 23 hypotheses have been formulated around the model, and a questionnaire survey has been conducted using a functional semantic search website created by KT and Hakia, so as to collect data to validate the model. Copies of a questionnaire form were handed out in person to 160 research associates and employees working in the area of designing and developing semantic search engines. Those who received the form, 148 respondents returned valid responses. The survey form asked respondents to use the given website to answer questions concerning the system. The results of the empirical study have indicated that, of the three quality dimensions, information quality was found to have the strongest association with the effectiveness of a Web-based semantic search engine. This finding is consistent with the observation in the literature that the aspects of the information quality should serve as a basis for evaluating the search outcomes from a semantic search engine. Measures under the information quality dimension that have a positive effect on informational gratification and user satisfaction were found to be recall and currency. Under the system quality dimension, response time and interactivity, were positively related to informational gratification. On the other hand, only one measure under the service quality dimension, reliability was found to have a positive relationship with user satisfaction. The results were based on the seven hypotheses that have been accepted. One may wonder why 15 out of the 23 hypotheses have been rejected and question the theoretical soundness of the model. However, the correlations between independent variables and dependent variables came out to be fairly high. This suggests that the structural equation model yielded results inconsistent with those of coefficient analysis, because the structural equation model intends to examine the relationship among independent variables as well as the relationship between independent variables and dependent variables. The findings offer some useful implications for owners of a semantic search engine, as far as the design and maintenance of the website is concerned. First, the system should be designed to respond to the user's query as fast as possible. Also it should be designed to support the search process by recommending, revising, and choosing a search query, so as to maximize users' interactions with the system. Second, the system should present search results with maximum recall and currency to effectively meet the users' expectations. Third, it should be capable of providing online services in a reliable and trustworthy manner. Finally, effective increase in user satisfaction requires the improvement of quality factors associated with a semantic search engine, which would in turn help increase the informational gratification for users. The proposed model can serve as a useful framework for measuring the success of a Web-based semantic search engine. Applying the search engine success framework to the measurement of search engine effectiveness has the potential to provide an outline of what areas of a semantic search engine needs improvement, in order to better meet information needs of users. Further research will be needed to make this idea a reality.

• The KIPS Transactions:PartD
• /
• v.10D no.2
• /
• pp.233-246
• /
• 2003

The mediator-based system integrates heterogeneous information systems with the flexible manner. But it does not give much attention on the query optimization issues, especially for the query reusing. The other thing is that it does not use standardized metadata for schema matching. To improve this two issues, we propose mediator-based Distributed Information Integration System (DIIS) which uses query caching regarding performance and uses ISO/IEC 11179 metadata registry in terms of standardization. The DIIS is designed to provide decision-making support, which logically integrates the distributed heterogeneous business information systems based on the Web environment. We designed the system in the aspect of three-layer expression formula architecture using the layered pattern to improve the system reusability and to facilitate the system maintenance. The functionality and flow of core components of three-layer architecture are expressed in terms of process line diagrams and assembly line diagrams of Eriksson Penker Extension Model (EPEM), a methodology of an extension of UML. For the implementation, Supply Chain Management (SCM) domain is used. And we used the Web-based environment for user interface. The DIIS supports functions of query caching and query reusability through Query Function Manager (QFM) and Query Function Repository (QFR) such that it enhances the query processing speed and query reusability by caching the frequently used queries and optimizing the query cost. The DIIS solves the diverse heterogeneity problems by mapping MetaData Registry (MDR) based on ISO/IEC 11179 and Schema Repository (SCR).

• The Journal of the Korea institute of electronic communication sciences
• /
• v.7 no.5
• /
• pp.1153-1164
• /
• 2012

In this paper, we propose an efficient car management system based on object-oriented modeling using car number recognition and smart phone. The proposed system perceives car number of repair vehicle after recognizing the licence plate using an IP camera in real time. And then, existing repair history information of the recognized car is be displayed in DID. In addition, maintenance process is shooting video while auto maintenance mechanic repairs car through IP-camera. That will be provide customer car identification and repairs history management function by sending key frames extracted from recorded video automatically. We provide user graphic interface based on web and mobile for your convenience. The module design of the proposed system apply software design modeling based on granular object-oriented considering reuse and extensibility after implementation. Car repairs center and maintenance companies can improve business efficiency, as well as the requested vehicle repair can increase customer confidence.

• Journal of Intelligence and Information Systems
• /
• v.21 no.2
• /
• pp.1-18
• /
• 2015

Collaborative filtering(CF) algorithm has been popularly used for recommender systems in both academic and practical applications. A general CF system compares users based on how similar they are, and creates recommendation results with the items favored by other people with similar tastes. Thus, it is very important for CF to measure the similarities between users because the recommendation quality depends on it. In most cases, users' explicit numeric ratings of items(i.e. quantitative information) have only been used to calculate the similarities between users in CF. However, several studies indicated that qualitative information such as user's reviews on the items may contribute to measure these similarities more accurately. Considering that a lot of people are likely to share their honest opinion on the items they purchased recently due to the advent of the Web 2.0, user's reviews can be regarded as the informative source for identifying user's preference with accuracy. Under this background, this study proposes a new hybrid recommender system that combines with users' review mining. Our proposed system is based on conventional memory-based CF, but it is designed to use both user's numeric ratings and his/her text reviews on the items when calculating similarities between users. In specific, our system creates not only user-item rating matrix, but also user-item review term matrix. Then, it calculates rating similarity and review similarity from each matrix, and calculates the final user-to-user similarity based on these two similarities(i.e. rating and review similarities). As the methods for calculating review similarity between users, we proposed two alternatives - one is to use the frequency of the commonly used terms, and the other one is to use the sum of the importance weights of the commonly used terms in users' review. In the case of the importance weights of terms, we proposed the use of average TF-IDF(Term Frequency - Inverse Document Frequency) weights. To validate the applicability of the proposed system, we applied it to the implementation of a recommender system for smartphone applications (hereafter, app). At present, over a million apps are offered in each app stores operated by Google and Apple. Due to this information overload, users have difficulty in selecting proper apps that they really want. Furthermore, app store operators like Google and Apple have cumulated huge amount of users' reviews on apps until now. Thus, we chose smartphone app stores as the application domain of our system. In order to collect the experimental data set, we built and operated a Web-based data collection system for about two weeks. As a result, we could obtain 1,246 valid responses(ratings and reviews) from 78 users. The experimental system was implemented using Microsoft Visual Basic for Applications(VBA) and SAS Text Miner. And, to avoid distortion due to human intervention, we did not adopt any refining works by human during the user's review mining process. To examine the effectiveness of the proposed system, we compared its performance to the performance of conventional CF system. The performances of recommender systems were evaluated by using average MAE(mean absolute error). The experimental results showed that our proposed system(MAE = 0.7867 ~ 0.7881) slightly outperformed a conventional CF system(MAE = 0.7939). Also, they showed that the calculation of review similarity between users based on the TF-IDF weights(MAE = 0.7867) leaded to better recommendation accuracy than the calculation based on the frequency of the commonly used terms in reviews(MAE = 0.7881). The results from paired samples t-test presented that our proposed system with review similarity calculation using the frequency of the commonly used terms outperformed conventional CF system with 10% statistical significance level. Our study sheds a light on the application of users' review information for facilitating electronic commerce by recommending proper items to users.