• Proceedings of the Korea Information Processing Society Conference
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• 2013.11a
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• pp.1156-1157
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• 2013

The mobile application market has been growing extensively in recent years. Currently, Apple's App Store has more than 400,000 applications and Google's Android Market has above 150,000 applications. Such growth in volumes of mobile applications has created a need to develop a recommender system that assists the users to take the right choice, when searching for a mobile application. In this paper, we study the recommendation system building tools based on collaborative filtering. Specifically, we present a study on comparison analysis of collaborative filtering in Java and R statistical software. We implement the collaborative filtering using Java's Apache Mahout and R's recommenderlab package. We evaluate both methods and describe the advantages and disadvantages of using them in order to implement collaborative filtering.

• Journal of Korea Multimedia Society
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• v.21 no.2
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• pp.232-241
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• 2018

Operational Transformation (OT) algorithms for real-time collaborative editing systems are becoming increasingly important due to the increased demand for collaborative data processing. The operational transformation algorithm is a technique for real-time concurrency control and consistency maintenance with non-locking technique, and many studies have been conducted to overcome three issues of convergence, causality-prevention, and intention-prevention. However, previous work has the disadvantage of wasting memory by storing all operations that occurred during an edit operation in the history buffer to solve this problem. Therefore, we propose a memory-saving real-time collaborative editing system that maintains a constant memory space and concurrency control through a method of applying the valid-time to each user-generated operation in order to reduce memory waste. This system prevents long-term memory occupation of client-generated operations, thus it reduces the space and time complexity even with low-rate of collaboration work, so that the performance degradation avoids.

• Journal of Ginseng Research
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• v.44 no.1
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• pp.14-23
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• 2020

Ginseng has been used as a popular herbal medicine in East Asia for at least two millennia. However, 20(R)-ginseng saponins, one class of important rare ginsenosides, are rare in natural products. 20(R)-ginseng saponins are generally prepared by chemical epimerization and microbial transformation from 20(S)-isomers. The C20 configuration of 20(R)-ginseng saponins are usually determined by ¹³C NMR and X-ray single-crystal diffraction. 20(R)-ginseng saponins have antitumor, antioxidative, antifatigue, neuroprotective, and osteoclastogenesis inhibitory effects, among others. Owing to the chemical structure and pharmacological and stereoselective properties, 20(R)-ginseng saponins have attracted a great deal of attention in recent years. In this study, the discovery, identification, chemical epimerization, microbial transformation, pharmacological activities, and metabolism of 20(R)-ginseng saponins are summarized.

• Journal of information and communication convergence engineering
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• v.1 no.2
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• pp.74-81
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• 2003

There are many researches on visual languages, but the most of them are difficult to support various collaborative interactions on a distributed multimedia environment. So, this paper suggests a collaborative visual language for interaction between multi-users. The visual language can describe a conceptual model for collaborative interactions between multi-users. Using the visual language, generated visual sentences consist of object icons and interaction operators. An object icon represents a user who is responsible for a collaborative activity, has dynamic attributes of a user, and supports flexible interaction between multi-users. An interaction operator represents an interactive relation between multi-users and supports various collaborative interactions. Merits of the visual language are as follows: supporting of both asynchronous interaction and synchronous interaction, supporting flexible interaction between multi-users according to participation or leave of users, supporting a user oriented modeling, etc. For example, an application to a workflow system for document approval is illustrated. So we could be found that the visual language shows a collaborative interaction.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.5
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• pp.1516-1539
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• 2022

This article shows a set of physical information fusion IoT systems that we designed for smart buildings. Its essence is a computer system that combines physical quantities in buildings with quantitative analysis and control. In the part of the Internet of Things, its mechanism is controlled by a monitoring system based on sensor networks and computer-based algorithms. Based on the design idea of the agent, we have realized human-machine interaction (HMI) and machine-machine interaction (MMI). Among them, HMI is realized through human-machine interaction, while MMI is realized through embedded computing, sensors, controllers, and execution. Device and wireless communication network. This article mainly focuses on the function of wireless sensor networks and MMI in environmental monitoring. This function plays a fundamental role in building security, environmental control, HVAC, and other smart building control systems. The article not only discusses various network applications and their implementation based on agent design but also demonstrates our collaborative information fusion strategy. This strategy can provide a stable incentive method for the system through collaborative information fusion when the sensor system is unstable in the physical measurements, thereby preventing system jitter and unstable response caused by uncertain disturbances and environmental factors. This article also gives the results of the system test. The results show that through the CPS interaction of HMI and MMI, the intelligent building IoT system can achieve comprehensive monitoring, thereby providing support and expansion for advanced automation management.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.718-723
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• 2005

In this paper, we propose the collaborative design system with which we can simultaneously modify the actual 3D CAD model by the remote control and have interaction on it. It enables instantaneous decision making among the multiple disciplines in distributed locations using the function of simultaneous access and video conference. It also provides the method of managing the consistency between design entities. This system consists of Collaborative Management Server, File Server, Solid Modeler Client, Remote Control Module and Client Module.

• Journal of applied mathematics & informatics
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• v.28 no.1_2
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• pp.439-450
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• 2010

In this paper, we propose a new idea to evaluate the prediction accuracy of user's preference generated by memory-based collaborative filtering algorithm before prediction process in the recommender system. Our analysis results show the possibility of a pre-evaluation before the prediction process of users' preference of item's transaction on the web. Classification functions proposed in this study generate a user's rating pattern under certain conditions. In this research, we test whether classification functions select users who have lower prediction or higher prediction performance under collaborative filtering recommendation approach. The statistical test results will be based on the differences of the prediction accuracy of each user group which are classified by classification functions using the generative probability of specific rating. The characteristics of rating patterns of classified users will also be presented.

• International Journal of Automotive Technology
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• v.7 no.2
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• pp.217-226
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• 2006

To maintain competitiveness in the modern automotive market, it is important to carry out process planning concurrently with new car development processes. Process planners need to make decisions concurrently and collaboratively in order to reduce manufacturing preparation time for developing a new car. Automated generation of a simulation model by using the integrated process plan database can reduce time consumed for carrying out a simulation and allow a consistent model to be used throughout. In this research, we developed a web-based system for concurrent and collaborative process planning and flow analysis for an automotive general assembly using web, database, and simulation technology. A single integrated database is designed to automatically generate simulation models from process plans without having to rework the data. This system enables process planners to evaluate their decisions quickly, considering various factors, and easily share their opinions with others. By using this collaborative system, time and cost put into the assembly process planning can be reduced and the reliability of the process plan would be improved.

• Journal of the Korea Society of Computer and Information
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• v.23 no.4
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• pp.89-95
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• 2018

It is not easy for the user to find the information that is appropriate for the user among the suddenly increasing information in recent years. One of the ways to help individuals make decisions in such a lot of information is the recommendation system. Although there are many recommendation methods for such recommendation systems, a representative method is collaborative filtering. In this paper, we design and implement the movie recommendation system on user-based collaborative filtering of apache mahout based on mongoDB. In addition, Pearson correlation coefficient is used as a method of measuring the similarity between users. We evaluate Precision and Recall using the MovieLens 100k dataset for performance evaluation.

• Journal of the Korean Data and Information Science Society
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• v.17 no.3
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• pp.717-726
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• 2006

This study is to investigate the MAE of prediction value by collaborative filtering algorithm originated by GroupLens and improved algorithm. To decrease the MAE on the collaborative recommender system on user based, this research proposes the improved algorithm, which reduces the possibility of over estimation of active user's preference mean collaboratively using other user’s preference mean. The result shows the MAE of prediction by improved algorithm is better than original algorithm, so the active user's preference mean used in prediction formula is possibly over estimated.