Detecting and monitoring game bots based on large-scale user-behavior log data analysis in multiplayer online games

2015 ◽  
Vol 72 (9) ◽  
pp. 3572-3587 ◽  
Author(s):  
YeonJun Choi ◽  
SungJune Chang ◽  
YongJun Kim ◽  
HunJoo Lee ◽  
WookHo Son ◽  
...  
Keyword(s):  
Data Analysis ◽  
Large Scale ◽  
User Behavior ◽  
Online Games ◽  
Log Data ◽  
Multiplayer Online Games ◽  
Log Data Analysis

Towards Automated Log Parsing for Large-Scale Log Data Analysis

2018 ◽  
Vol 15 (6) ◽  
pp. 931-944 ◽  
Author(s):  
Pinjia He ◽  
Jieming Zhu ◽  
Shilin He ◽  
Jian Li ◽  
Michael R. Lyu
Keyword(s):  
Data Analysis ◽  
Large Scale ◽  
Log Data ◽  
Log Data Analysis

scholarly journals Quality assurance of geometric accuracy based on an electronic portal imaging device and log data analysis for Dynamic WaveArc irradiation

2018 ◽  
Vol 19 (3) ◽  
pp. 234-242
Author(s):  
Hideaki Hirashima ◽  
Yuki Miyabe ◽  
Mitsuhiro Nakamura ◽  
Nobutaka Mukumoto ◽  
Takashi Mizowaki ◽  
...  
Keyword(s):  
Quality Assurance ◽  
Data Analysis ◽  
Electronic Portal Imaging Device ◽  
Geometric Accuracy ◽  
Imaging Device ◽  
Log Data ◽  
Portal Imaging ◽  
Electronic Portal Imaging ◽  
Log Data Analysis

A scoping review of the use of log data for evaluating mobile Apps (Preprint)

2021 ◽  
Author(s):  
Ane van Schalkwyk ◽  
Sara Grobbelaar ◽  
Euodia Vermeulen
Keyword(s):  
Mixed Methods ◽  
Data Analysis ◽  
Scoping Review ◽  
Mobile Apps ◽  
Grey Literature ◽  
Research Approach ◽  
Log Data ◽  
Data Framework ◽  
Log Data Analysis ◽  
Google Search

BACKGROUND There is a growing trend in the potential benefits and application of log data for the evaluation of mHealth Apps. However, the process by which insights may be derived from log data remains unstructured, resulting in underutilisation of mHealth data. OBJECTIVE We aimed to acquire an understanding of how log data analysis can be used to generate valuable insights in support of realistic evaluations of mobile Apps through a scoping review. This understanding is delineated according to publication trends, associated concepts and characteristics of log data, framework or processes required to develop insights from log data, and how these insights may be utilised towards evaluation of Apps. METHODS The PRISMA-ScR guidelines for a scoping review were followed. The Scopus database, the Journal of Medical Internet Research (JMIR), and grey literature (through a Google search) delivered 105 articles of which 33 articles were retained in the sample for analysis and synthesis. RESULTS A definition for log data is developed from its characteristics and articulated as: anonymous records of users’ real-time interactions with the application, collected objectively or automatically and often accessed from cloud-based storage. Publications for theoretical and empirical work on log data analysis have increased between 2010 and 2021 (100% and 95% respectively). The research approach is distributed between inductive (43%), deductive (30%), and a hybrid approach (27%). Research methods include mixed-methods (73%) and quantitative only (27%), although mixed-methods dominate since 2018. Only 30% of studies articulated the use of a framework or model to perform the log data analysis. Four main focus areas for log data analysis are identified as usability (40%), engagement (15%), effectiveness (15%), and adherence (15%). An average of one year of log data is used for analysis, with an average of three years from the launch of the App to the analysis. Collected indicators include user events or clicks made, specific features of the App, and timestamps of clicks. The main calculated indicators are features used or not used (24/33), frequency (21/33), and duration (18/33). Reporting the calculated indicators per ‘user or user group’ was the most used reference point. CONCLUSIONS Standardised terminology, processes, frameworks, and explicit benchmarks to utilise log data are lacking in literature. Thereby, the need for a conceptual framework that is able to standardise the log analysis of mobile Apps is determined. We provide a summary of concepts towards such a framework. CLINICALTRIAL NA

Reservoir Modeling of Lam Sands (Madbi Formation) Using Seismic Inversion with Core and Log Data Analysis in Prospect D Field, Block 18, Sab’ Atyan Basin - Yemen

2018 ◽  
Author(s):  
Ahmed Saleh ◽  
Amin Al Maktari ◽  
Abdulkhalek Alogily ◽  
Adel Haygana ◽  
Jaber Al Adhashi ◽  
...  
Keyword(s):  
Data Analysis ◽  
Seismic Inversion ◽  
Reservoir Modeling ◽  
Log Data ◽  
Log Data Analysis

Focused on the prize: Characteristics of experts in massive multiplayer online games

First Monday ◽  
2011 ◽  
Author(s):  
Jing Wang ◽  
David A. Huffaker ◽  
Jeffrey W. Treem ◽  
Lindsay Fullerton ◽  
Muhammad A. Ahmad ◽  
...  
Keyword(s):  
Achievement Motivation ◽  
Large Scale ◽  
Role Playing ◽  
Online Games ◽  
Personality Characteristics ◽  
Multiplayer Online Games ◽  
Role Playing Game ◽  
Demographic Attributes ◽  
The Relationship ◽  
Game Performance

This study is the first large–scale multi–method attempt to empirically examine the characteristics leading to development of expertise in EverQuest II, a popular massively multi–player online role–playing game (MMOs). Benefiting from the unprecedented opportunity of obtaining game log data matched with survey data, the project investigated the relationship between player motivations and in–game behavior, personality characteristics, and demographic attributes with game performance and achievement, which we refer to as game “expertise.” Players who were high on achievement motivation or social motivation had higher game expertise, while those high on immersion motivation had lower expertise. Game experts were also characterized by focusing their game time on completing tasks. Younger players showed a slim advantage over older players. Male and female players exhibited similar expertise levels in this MMO.

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