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

Choi, Yeon-Jun; Chang, SungJune; Kim, Yong-Jun; Lee, Hunjoo; Son, Wookho; Jin, Seong-Il

doi:10.1007/s11227-015-1545-2

Cited by 13 publications

(3 citation statements)

References 9 publications

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“…Log data analyses are currently used in diversified domains, such as education [ 12 , 13 ], human-computer interaction [ 14 , 15 ], and network security [ 16 ] where they are mainly used to analyze system performance and acceptance based on models for information retrieval. However, technology has evolved and the aim of behavior change technologies is not only to provide information, but rather to stimulate and support people in their process of behavior change [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

Opening the Black Box of Electronic Health: Collecting, Analyzing, and Interpreting Log Data

Sieverink¹,

Kelders²,

Poel³

et al. 2017

JMIR Res Protoc

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In electronic health (eHealth) research, limited insight has been obtained on process outcomes or how the use of technology has contributed to the users’ ability to have a healthier life, improved well-being, or activate new attitudes in their daily tasks. As a result, eHealth is often perceived as a black box. To open this black box of eHealth, methodologies must extend beyond the classic effect evaluations. The analyses of log data (anonymous records of real-time actions performed by each user) can provide continuous and objective insights into the actual usage of the technology. However, the possibilities of log data in eHealth research have not been exploited to their fullest extent. The aim of this paper is to describe how log data can be used to improve the evaluation and understand the use of eHealth technology with a broader approach than only descriptive statistics. This paper serves as a starting point for using log data analysis in eHealth research. Here, we describe what log data is and provide an overview of research questions to evaluate the system, the context, the users of a technology, as well as the underpinning theoretical constructs. We also explain the requirements for log data, the starting points for the data preparation, and methods for data collection. Finally, we describe methods for data analysis and draw a conclusion regarding the importance of the results for both scientific and practical applications. The analysis of log data can be of great value for opening the black box of eHealth. A deliberate log data analysis can give new insights into how the usage of the technology contributes to found effects and can thereby help to improve the persuasiveness and effectiveness of eHealth technology and the underpinning behavioral models.

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Section: Introductionmentioning

confidence: 99%

Opening the Black Box of Electronic Health: Collecting, Analyzing, and Interpreting Log Data

Sieverink¹,

Kelders²,

Poel³

et al. 2017

JMIR Res Protoc

View full text Add to dashboard Cite

show abstract

“…Last decade has witnessed the expansion of the scale and complexity of log data. Log data reflect the operation of the user during the operation of the program, or the user's access behaviour generated during the interaction with the computer system [1]. The effort of collecting, storing and analysing a large number of logs is aggraded more when the logs are diverse, heterogeneous and dynamic.…”

Section: Introductionmentioning

confidence: 99%

Log Data Real Time Analysis Using Big Data Analytic Framework with Storm and Hadoop

Wang

2018

MATEC Web Conf.

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The log data real-time processing platform which is built using Storm On YARN integrated MapReduce and Storm that use MapReduce to complete large-scale off-line data global knowledge extraction, sudden knowledge extraction of small-scale data in Kafka buffers through Storm, and continuous real-time calculation of streaming data in combination with global knowledge. We tested our technique with the well-known KDD99 CUP data set. The experimentation results prove the system to be effective and efficient.

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“…To achieve that goal, we need firstly obtain a clear picture of the changed status of a graph of streaming application and then decide how to optimize it, and which vertices of the graph needed to be online rescheduled. More importantly, to achieve the scheduling fairness of multiple applications [8] [9]. Currently, most of the existing research works have focused on application scheduling.…”

Section: Introductionmentioning

confidence: 99%

Rethinking elastic online scheduling of big data streaming applications over high-velocity continuous data streams

et al. 2017

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Online scheduling plays a key role for big data streaming applications in a big data stream computing environment, as the arrival rate of high velocity continuous data stream might fluctuate over time. In this paper, an elastic online scheduling framework for big data streaming applications (E-Stream) is proposed, exhibiting the following features. (1) Profile mathematical relationships between system response time, multiple application fairness, and online features of high velocity continuous stream. (2) Scale out or scale in a data stream graph by quantifying computation and communication cost, and the vertex semantics for arrival rate of data stream, and adjust the degree of parallelism of vertices in the graph. Sub-graph is further constructed to minimize data dependencies among the sub-graphs. (3) Elastically schedule a graph by a priority based earliest finish time first online scheduling strategy, and schedule multiple graphs by a max-min fairness strategy. (4) Evaluate the low system response time and acceptable applications fairness objectives in a realworld big data stream computing environment. Experimental results conclusively demonstrate that the proposed E-Stream provides better system response time and applications fairness compared to the existing Storm framework.

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

Cited by 13 publications

References 9 publications

Opening the Black Box of Electronic Health: Collecting, Analyzing, and Interpreting Log Data

Opening the Black Box of Electronic Health: Collecting, Analyzing, and Interpreting Log Data

Log Data Real Time Analysis Using Big Data Analytic Framework with Storm and Hadoop

Rethinking elastic online scheduling of big data streaming applications over high-velocity continuous data streams

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