Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11861/5798
Title: A system for collecting and analyzing data from existing game selling platforms
Authors: Dr. YUEN Man-Ching, Connie 
Chan, Siu-Lung 
Leung, Ho-Tung 
Wu, Pak-Lun 
Yip, Pui-Yi 
Issue Date: 2019
Source: In Liu, Y., Wang, L., Zhao, L., & Yu, Z. (Eds.) (2019). Advances in natural computation, fuzzy systems and knowledge discovery (pp. 443-450).
Conference: 15th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery 2019 
Abstract: Nowadays, game becomes an important element in the lives of many people. People of different ages and background play various types of games using different types of devices for entertainment. People can even meet up with new friends when playing games. In recent years, many game selling platforms offer an easy and fast way for game players to buy games. Since many new games are released every day, different game selling platforms provide different part of game information of the same game, and these platforms offer different prices for the same game too. Therefore, game players need to browse all popular game selling platforms to consolidate all game information of a particular game before making decision whether to buy the game. It is very time consuming and inefficient. To address the above problems, we would like to build up a website to let people know the game information that consolidated from different game selling platforms. The information not only includes the comparison among selling prices of various game selling platforms, but also other information such as publishers, system requirements. With the large amount of consolidated data, we can also provide a search area for users to search their preferred game by using some searching criteria, and analyze the consolidated data to speculate the development tendency of games, for example, the game type of the next game may be released on a particular game selling platform. For data analysis, we mainly focus on over one thousands of games released on Stream, the most popular game selling platform. Our result shows that we can use game information to predict the tendency of game type of the next game to be released. © 2020, Springer Nature Switzerland AG.
Type: Conference Paper
URI: http://hdl.handle.net/20.500.11861/5798
ISBN: 9783030325909
DOI: 10.1007/978-3-030-32591-6_47
Appears in Collections:Journalism & Communication - Publication

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