• Proceedings of the Korea Information Processing Society Conference
• /
• 2014.04a
• /
• pp.319-321
• /
• 2014

카카오톡, 페이스북 등의 소셜 네트워크 서비스(Social Network Service: SNS)가 발전함에 따라 이를 기반으로 하는 소셜 네트워크 게임(Social Network Game: SNG)이 지인과 함께 가볍게 즐길 수 있다는 장점을 내세워 유저들로부터 많은 호응을 얻고 있다. 특히 SNG가 갖는 장점은 언제 어디서나 즐길 수 있는 스마트폰 앱에 접목되면서 그 효과가 극대화되고 있다. 이에 많은 개발사들이 앞다투어 SNG 개발에 참여를 하고 있는 상황이지만 다양한 SNS 서비스 플랫폼에 비해 SNG 개발을 위한 공개 엔진은 없는 상황이다. 본 논문에서는 다양한 SNS 플랫폼을 통해 공통으로 사용할 수 있는 통합 관리 소셜 네트워크 엔진을 개발하는 과정과 구현된 엔진을 사용하여 디바이스를 통한 그래픽 출력 성능을 실험하였으며, 실험 결과 기존의 SNG와의 속도차이가 없음을 보여 주어 게임 구현하기에 충분함을 입증하였다.

• Journal of Internet Computing and Services
• /
• v.13 no.1
• /
• pp.125-134
• /
• 2012

As the social networking service (SNS), Facebook, and Cyworld, is developing, the social network game and social business commerce based on this service is activated. Especially, the Social Network Game (SNG) is getting explosive interests and it becomes popular, because it is small scale and user can enjoy the game among close friends. The market for this game is getting larger every year, but still it has some limitations in developing the game. Especially, the current game engine is aiming for developing online or console game, and there is no exclusive game engine for developing SNG. Therefore, it takes lots of time for developing SNG with this game engine. In this paper, we described a design and development of the game engine optimized for developing SNG, which not only adapts the main characteristics of the previous game engine, but also considers the specific characteristics of the SNG. The engine also supports map for the simulation game that is the most popular game in SNG, and also provides modules and tools for developing character animation easily. The evaluation standard for the performance of the game engine is the output generation speed of image, text and character. And the results showed reasonable output speed for developing the SNG in generation of image, text, and character.

• Information Systems Review
• /
• v.12 no.1
• /
• pp.1-22
• /
• 2010

Demands onenterprise applications are changing drastically in terms of service and value. Currently enterprises have started to view these applications as service systems, as they combine technology with organizational networks designed to deliver services that satisfy the needs of customers and marketing operations. Social networking is playing a crucial role in this direction and provides organizations with the critical data that enable to build strong relationships with their customers and partners. Enterprises have started using this concept, by integrating social networking services with their enterprise applications such as CRM. In this paper, we combine an open source social networking engine with a CRM (Customer Relationship Management) application to constitute a social CRM system. This can bring the customers closer to the enterprise and facilitate better communication with them. Social Networking Analysis constructs were used to analyze the effectiveness of service system. In the current competitive and economically challenging conditions, salespeople needs to quickly and effectively establish meaningful communication with customers. Our approach can address this issue, by handling the changing customer demands in minimal time, and increases service quality and business value.

• Journal of Digital Convergence
• /
• v.11 no.2
• /
• pp.279-285
• /
• 2013

The development of information networks and computer technology has become a foundation to open up a sea of information and knowledge. The recent popularization of smart devices has been used as a tool to easily obtain the desired information and knowledge. In this paper, a knowledge-sharing system using information and social networks based on smart devices is proposed. The proposed system consists of functions of an Internet information search for user queries, accumulated knowledge, and social network response from acquaintances. An evaluation for user satisfaction was conducted to analyze the efficacy of the proposed system. According to the experiment, the knowledge-sharing system using smart device information results in significant satisfaction compared to the general information search engines.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.38B no.5
• /
• pp.385-393
• /
• 2013

In the past decade, a paradigm shift from machine-centered to human-centered and from technology-driven to user-driven has been witnessed. Consequently, Social search is getting more social and Social Network Service (SNS) is a popular Web service to connect and/or find friends, and the tendency of users interests often affects his/her who have similar interests. If we can track users' preferences in certain boundaries in terms of Web search and/or knowledge sharing, we can find more relevant information for users. In this paper, we propose a novel Topic Sensitive_Social Relationship Rank (TS_SRR) algorithm. We propose enhanced Web searching idea by finding similar and credible users in a Social Network incorporating social information in Web search. The Social Relation Rank between users are Social Relation Value, that is, for a different topics, a different subset of the above attributes is used to measure the Social Relation Rank. We observe that a user has a certain common interest with his/her credible friends in a Social Network, then focus on the problem of identifying users who have similar interests and high credibility, and sharing their search experiences. Thus, the proposed algorithm can make social search improve one step forward.

• Journal of Broadcast Engineering
• /
• v.18 no.1
• /
• pp.98-105
• /
• 2013

Patterns for generating and consuming contents are various in these days from conventional broadcasting contents to UCC. There are many researches on developing recommendation engines based on user's profile for providing desired contents. In this paper we propose a contents recommendation system using not only user's profile but other's profiles in closed user group of the social network based on patterns for user's consuming contents. The proposed recommendation agent update user's profile using usage history and other's profiles related to the user in the closed user group.

• Journal of the Korea Society of Computer and Information
• /
• v.14 no.10
• /
• pp.43-53
• /
• 2009

Knowledge retrieval service that uses collective intelligence which has special quality of open structure and can share the accumulative data is gaining popularity. However, acquiring the right needs for users from massive public knowledge is getting harder. Recently, search results from Google which is known for it's exquisite algorism, shows results for collective intelligence such as Wikipedia, Yahoo Q/A at the highest rank. Objective of this paper is to show that most answers come from human and to find the most influential people in on-line knowledge retrieval service. Hereupon, this paper suggest the influence value calculation algorism by analyzing user relation as centrality which social network is based on user activeness and reliance in Naver 지식iN. The influence value calculated by the suggested algorism will be an important index in distinguishing reliable and the right user for the question by ranking users with troubleshooting solutions in the knowledge retrieval service. This will contribute in search satisfaction by acquiring the right information and knowledge for the users which is the most important objective for knowledge retrieval service.

• Journal of Intelligence and Information Systems
• /
• v.18 no.3
• /
• pp.97-117
• /
• 2012

Nowadays, Internet users tend to do a variety of actions at the same time such as web browsing, social networking and multimedia consumption. While watching a video, once a user is interested in any product, the user has to do information searches to get to know more about the product. With a conventional approach, user has to search it separately with search engines like Bing or Google, which might be inconvenient and time-consuming. For this reason, a video annotation platform has been developed in order to provide users more convenient and more interactive ways with video content. In the future of smart TV environment, users can follow annotated information, for example, a link to a vendor to buy the product of interest. It is even better to enable users to search for information by directly discussing with friends. Users can effectively get useful and relevant information about the product from friends who share common interests or might have experienced it before, which is more reliable than the results from search engines. Social networking services provide an appropriate environment for people to share products so that they can show new things to their friends and to share their personal experiences on any specific product. Meanwhile, they can also absorb the most relevant information about the product that they are interested in by either comments or discussion amongst friends. However, within a very huge graph of friends, determining the most appropriate persons to ask for information about a specific product has still a limitation within the existing conventional approach. Once users want to share or discuss a product, they simply share it to all friends as new feeds. This means a newly posted article is blindly spread to all friends without considering their background interests or knowledge. In this way, the number of responses back will be huge. Users cannot easily absorb the relevant and useful responses from friends, since they are from various fields of interest and knowledge. In order to overcome this limitation, we propose a method to filter a user's friends for information search, which leverages semantic video annotation and social networking services. Our method filters and brings out who can give user useful information about a specific product. By examining the existing Facebook information regarding users and their social graph, we construct a user profile of product interest. With user's permission and authentication, user's particular activities are enriched with the domain-specific ontology such as GoodRelations and BestBuy Data sources. Besides, we assume that the object in the video is already annotated using Linked Data. Thus, the detail information of the product that user would like to ask for more information is retrieved via product URI. Our system calculates the similarities among them in order to identify the most suitable friends for seeking information about the mentioned product. The system filters a user's friends according to their score which tells the order of whom can highly likely give the user useful information about a specific product of interest. We have conducted an experiment with a group of respondents in order to verify and evaluate our system. First, the user profile accuracy evaluation is conducted to demonstrate how much our system constructed user profile of product interest represents user's interest correctly. Then, the evaluation on filtering method is made by inspecting the ranked results with human judgment. The results show that our method works effectively and efficiently in filtering. Our system fulfills user needs by supporting user to select appropriate friends for seeking useful information about a specific product that user is curious about. As a result, it helps to influence and convince user in purchase decisions.

• KIISE Transactions on Computing Practices
• /
• v.24 no.1
• /
• pp.40-45
• /
• 2018

With the advent of big data and social networks, large-scale graph processing becomes popular research topic. Recently, an optimization technique called Gorder has been proposed to improve the performance of in-memory graph processing. This technique improves performance by optimizing the graph layout on memory to have better cache locality. However, since it is designed for in-memory graph processing systems, the technique is not suitable for disk-based graph engines; also the cost for applying the technique is significantly high. To solve the problem, we propose a new graph ordering called I/O Order. I/O Order considers the characteristics of I/O accesses for SSDs and HDDs to improve the performance of disk-based graph engine. In addition, the algorithmic complexity of I/O Order is simple compared to Gorder, hence it is cheaper to apply I/O Ordering. I/O order reduces the cost of pre-processing up to 9.6 times compared to that of Gorder's, still its performance is 2 times higher compared to the Random in low-locality graph algorithms.

• The Journal of the Korea Contents Association
• /
• v.19 no.5
• /
• pp.187-193
• /
• 2019

Key-value storage engines are an essential component of growing demand in many computing environments, including social networks, online e-commerce, and cloud services. Recent key-value storage engines offer many features such as transaction, versioning, and replication. In a key-value storage engine, transaction processing provides atomicity through Write-Ahead-Logging (WAL), and a synchronous commit method for transaction processing flushes log data before the transaction completes. According to our observation, flushing log data to persistent storage is a performance bottleneck for key-value storage engines due to the significant overhead of fsync() calls despite the various optimizations of existing systems. In this article, we propose a group synchronization method to improve the performance of the key-value storage engine. We also design and implement a transaction scheduling method to perform other transactions while the system processes fsync() calls. The proposed method is an efficient way to reduce the number of frequent fsync() calls in the synchronous commit while supporting the same level of transaction provided by the existing system. We implement our scheme on the WiredTiger storage engine and our experimental results show that the proposed system improves the performance of key-value workloads over existing systems.