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A System for Collecting and Analyzing Data from Existing Game Selling Platforms

  • Man-Ching YuenEmail author
  • Siu-Lung Chan
  • Ho-Tung Leung
  • Pak-Lun Wu
  • Pui-Yi Yip
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1075)

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.

Keywords

Data mining Data analysis Data consolidation Big data applications Game selling 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Man-Ching Yuen
    • 1
    Email author
  • Siu-Lung Chan
    • 2
  • Ho-Tung Leung
    • 2
  • Pak-Lun Wu
    • 2
  • Pui-Yi Yip
    • 2
  1. 1.Hong Kong Shue Yan UniversityHong KongChina
  2. 2.Vocational Training CouncilHong KongChina

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