To successfully operate online games, gaming companies are introducing the systematic customer relationship management model. Particularly, churn analysis is one of the most important issues, because preventing a customer from churning is often more cost-efficient than acquiring a new customer. Churn prediction models should, thus, consider maximizing not only accuracy but also the expected profit derived from the churn prevention. We, thus, propose a churn prediction method for optimizing profit consisting of two main steps: first, selecting prediction target, second, tuning threshold of the model. In online games, the distribution of a user's customer lifetime value is very biased that a few users contribute to most of the sales, and most of the churners are no-paying users. Consequently, it is cost-effective to focus on churn prediction to loyal customers who have sufficient benefits. Furthermore, it is more profitable to adjust the threshold of the prediction model so that the expected profit is maximized rather than maximizing the accuracy. We applied the proposed method to real-world online game service, Aion, one of the most popular online games in South Korea, and then show that our method has more cost-effectiveness than the prediction model for total users when the campaign cost and the conversion rate are considered.

Game companies avoid sharing their game data with external researchers. Only a few research groups have been granted limited access to game data so far. The reluctance of these companies to make data publicly available limits the wide use and development of data mining techniques and artificial intelligence research specific to the game industry. In this paper, we developed and implemented an international competition on game data mining using commercial game log data from one of the major game companies in South Korea: NCSOFT. Our approach enabled researchers to develop and apply state-of-the-art data mining techniques to game log data by making the data open. For the competition, data were collected from Blade & Soul, an action role-playing game, from NCSOFT. The data comprised approximately 100 GB of game logs from 10 000 players. The main aim of the competition was to predict whether a player would churn and when the player would churn during two periods between which the business model was changed to a free-to-play model from a monthly subscription. The results of the competition revealed that highly ranked competitors used deep learning, tree boosting, and linear regression.

Online game involves a very large number of users who are interconnected and interact with each other via the Internet. We studied the characteristics of exchanging virtual goods with real money through the processes called "real money trading (RMT)". This exchange might influence online game user behaviors and cause damage to the reputation of game companies. We examined in-game transactions to reveal RMT by constructing a social graph of virtual goods exchanges in an online game and identifying network communities of users.

Many online games have their own ecosystems, where players can purchase in-game assets using game money. Players can obtain game money through active participation or “real money trading” through official channels: converting real money into game money. The unofficial market for real money trading gave rise to gold farming groups (GFGs), a phenomenon with serious impact in the cyber and real worlds. GFGs in massively multiplayer online role-playing games (MMORPGs) are some of the most interesting underground cyber economies because of the massive nature of the game. To detect GFGs, there have been various studies using behavioral traits. However, they can only detect gold farmers, not entire GFGs with internal hierarchies. Even worse, GFGs continuously develop techniques to hide, such as forming front organizations, concealing cyber-money, and changing trade patterns when online game service providers ban GFGs. In this paper, we analyze the characteristics of the ecosystem of a large-scale MMORPG, and devise a method for detecting GFGs. We build a graph that characterizes virtual economy transactions, and trace abnormal trades and activities. We derive features from the trading graph and physical networks used by GFGs to identify them in their entirety. Using their structure, we provide recommendations to defend effectively against GFGs while not affecting the existing virtual ecosystem.

Nowadays, vehicles have a lot of Electronic Control Unit (ECU). In order to check ECU software reprogramming and ECU assembly line inspection, international standards are developed. Among the standards the Diagnostic Communication over Internet Protocol (DoIP) is a typical protocol for remote diagnostic of vehicles which consists of International Standard Organization (ISO) 13400 parts. The DoIP just consider in-vehicle scenario where the communication range is a Local Area Network (LAN). In this paper, we propose a new communication interface extends the communication range to Wide Area Network (WAN) to implement remote vehicle diagnosis. The simulation results show that the CPU Utilization Threshold Area (CUTA) is between -20.70266 and 20.70266 which implies remote vehicle diagnosis is possible from the long distance site.

The smart grid is added ICT (Information & Communication Technology) into the existing electrical grid to facilitate the exchange of information between the power supplier and consumer. The V2G (Vehicle to Grid) is an application of the smart grid. The EV (Electric Vehicle) is connected to the an EVSE (Electric Vehicle Supply Equipment). Communication between EV and smart grid uses V2G. The V2G uses PLC (Power Line Communication) technology. The PLC uses HPGP (Home Plug Green PHY) in order to charging an EV with EVSE. The V2G communication interface uses IP protocols at devices in the smart grid and EVs. An EV gets IPv6 address. On the other hand, the smart grid primarily uses IPv4 address. The IPv6 is not compatible with IPv4, they can't communicate. In this research, we propose a new link adaptation layer of HPGP in order to V2G communication interface.