TY - CHAP
T1 - Clouds and Continuous Analytics Enabling Social Networks for Massively Multiplayer Online Games
AU - Iosup, Alexandru
AU - Lascateu, Adrian
PY - 2011
Y1 - 2011
N2 - Many of the hundreds of millions Massively Multiplayer Online Games (MMOGs) players are also involved in the social networks built around the MMOGs they play. Through these networks, these players exchange game news, advice, and expertise, and expect in return support such as player reports and clan statistics. Thus, the MMOG social networks need to collect and analyze MMOG data, in a process of continuous MMOG analytics. In this chapter we investigate the use of CAMEO, an architecture for Continuous Analytics for Massively multiplayEr Online games on cloud resources, to support the analytics part of MMOG social networks. We present the design and implementation of CAMEO, with a focus on the cloud-related benefits and challenges. We also use CAMEO to do continuous analytics on a real MMOG community of over 5,000,000 players, thus performing the largest study of an online community, to-date. © 2011 Springer-Verlag Berlin Heidelberg.
AB - Many of the hundreds of millions Massively Multiplayer Online Games (MMOGs) players are also involved in the social networks built around the MMOGs they play. Through these networks, these players exchange game news, advice, and expertise, and expect in return support such as player reports and clan statistics. Thus, the MMOG social networks need to collect and analyze MMOG data, in a process of continuous MMOG analytics. In this chapter we investigate the use of CAMEO, an architecture for Continuous Analytics for Massively multiplayEr Online games on cloud resources, to support the analytics part of MMOG social networks. We present the design and implementation of CAMEO, with a focus on the cloud-related benefits and challenges. We also use CAMEO to do continuous analytics on a real MMOG community of over 5,000,000 players, thus performing the largest study of an online community, to-date. © 2011 Springer-Verlag Berlin Heidelberg.
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U2 - 10.1007/978-3-642-20344-2_12
DO - 10.1007/978-3-642-20344-2_12
M3 - Chapter
SN - 9783642203435
VL - 352
T3 - Studies in Computational Intelligence
SP - 303
EP - 328
BT - Next Generation Data Technologies for Collective Computational Intelligence
ER -