Over the last decade big data and data mining has received growing interest and importance in game production to process and draw actionable insights from large volumes of player-related data in order to inform game design, to ensure customer satisfaction, to maximize revenues, and to drive technical innovation.
This volume seeks to provide a comprehensive overview of data mining applications pertaining to all aspects of gaming and entertainment. It is intended to serve as a reference volume for academics and practitioners alike. The book will be structured along four main themes, covering different aspects of data mining in games. Possible topics of interest for each of the themes are listed below. This list is meant to be suggestive, not exhaustive. If you have any interesting suggestion for an interesting chapter not covered here here please get in touch.
Themes and Topics of Interest
Introductory chapters to game data mining
Introductory chapters aimed at explaining common techniques used in the context of game data mining and data-driven game development. For example, overview chapters explaining data mining techniques such as clustering methods or pattern mining and their application in the gaming domain.
Data mining for games user research
Contributions pertaining to issues related to games analytics and directed towards understanding player behavior and informing games user research. Topics of interest include, among others, player profiling and modeling, behavioral analysis, understanding player communities and social structures, churn prediction and retention analysis, balancing of in-game economies, or monetization.
Data mining for game technology
Visualization of large-scale game data
Contributions dealing with the visualization of in-game data for the purpose of exploration, analysis, knowledge discovery, and communication. This includes, but is not limited to spatio-temporal visualization approaches, multi-modal data visualization, visual analytics tools, and time-based visualizations.
Research articles covering all aspects of data mining in gaming or entertainment. Such chapters may describe novel approaches, methods, or research findings. Chapters reviewing common techniques or discussing the state-of-the-art in game data mining are also within the scope of the book.
Case studies describing the application of data-mining technique in practical settings. We especially welcome case studies from industry experts. Case studies may cover one or multiple themes. For example, case studies may describe best practices or lessons learned, e.g., by highlighting what went right and wrong in data-driven game development (such as, for example, Gamasutra style post-mortems).
Proposals should not exceed 600 words and should include a tentative title, a short description/outline of the chapter, author names, affiliations and a brief biography. Submission should be previously unpublished and should not be under consideration for publication elsewhere. Please send your proposals via e-mail to firstname.lastname@example.org.
The final chapter should be around 20-25 double-spaced pages (incl. figures and tables). Templates for formatting the chapter itself will be provided in due time.
Proposals will be used to evaluate if the proposed chapter fits the topic of the book. All accepted chapters will then undergo a double-blind review process. For additional inquiries and advice on the potential suitability of any proposed chapter please contact the editor.
|Download a PDF version of the call for contributions.|