Volume (Societal Scale - It's about just everyone.)
Variety (High Heterogeneity - They come in all formats.)
We have seen the explosive growth of social network and it
provides a huge amount of data. We have millions of tweets
about the brands, thousands of Facebook “likes,” hundreds
of thousands of check-ins on Foursquare. So what are we
going to do with this large data? How to construct the
huge data fragment? What underlying knowledge can we
explore from this huge data? Thus, we derive the problem
of user identity mapping across social network. Basically,
The key challenge is to construct a discriminative social signature based on a variety of elements in different social network.
Velocity (Real-time - Now or never...)
Value (Bring Value across Online and Offline.)
The identification of a user’s core community benefits in the following aspects. First, it is observed on Twitter that a user’s online social network, i.e., the part of the follow network excluding the core community, is more informative of the user’s interest, hobby, life-style, etc. Second, the core community, on the other hand, contributes to a more robust and accurate profiling of the user’s interests. This is because one’s closest friends in real life are likely to be of a similar kind. Finally, the discovery of the core community gives insight into the different characteristics of a user’s online and off-line social network, which is interesting in itself.
Gaming is an essential part of human activity that brings enjoyment to players. To derive pleasure from gaming, a player has to be able to gain sufficient expertise to achieve the desired outcomes.
This gaming expertise is usually accumulated through playing or watching many game instances.
Because of the recent advances in database systems and collaborative web sites, it is possible to record
massive amount of game data generated by player. One can then apply data analytics to discover game
strategies, player performance, and important behavior patterns.