R “Ray” Wang:
These characteristics highlight the importance and complexity required to solve context in big data.
Viscosity – Viscosity measures the resistance to flow in the volume of data. This resistance can come from different data sources, friction from integration flow rates, and processing required to turn the data into insight. Technologies to deal with viscosity include improved streaming, agile integration bus’, and complex event processing.
Virality – Virality describes how quickly information gets dispersed across people to people (P2P) networks. Virality measures how quickly data is spread and shared to each unique node. Time is a determinant factor along with rate of spread.
While I don’t feel like adding all the V-words to the 3 or 4 V’s definition of Big Data, these new two, viscosity and virality, sound intriguing.
Original title and link: Viscosity and Virality: Beyond the Three V’s of Big Data ( ©myNoSQL)