Databases Reference
In-Depth Information
Volume
Ambiguity
Viscosity
Big Data
Variety
Velocity
Virality
FIGURE 2.10
Additional Big Data characteristics.
●
Graph processing capabilities
●
Video and audio processing capabilities
From the discussions on the three V's associated with Big Data, you can see why there is intense
complexity in processing Big Data. Along with the three V's, there also exists ambiguity, viscosity,
and virality (the latter two have been contributed by an independent analyst community;
Figure 2.10
).
●
Ambiguity
—a lack of metadata creates ambiguity in Big Data. For example, in a photograph
or in a graph, M and F can depict gender or can depict Monday and Friday. This characteristic
manifests in the volume-variety category most times.
●
Viscosity
—measures the resistance (slow down) to flow in the volume of data. Resistance can
manifest in dataflows, business rules, and even be a limitation of technology. For example, social
media monitoring falls into this category, where a number of enterprises just cannot understand
how it impacts their business and resist the usage of the data until it is too late in many cases.
●
Virality
—measures and describes how quickly data is shared in a people-to-people (peer) network.
Rate of spread is measured in time. For example, re-tweets that are shared from an original tweet
is a good way to follow a topic or a trend. The context of the tweet to the topic matters in this
situation.
SUMMARY
In this chapter, we discussed the complexity associated with processing Big Data and the underly-
ing characteristics of Big Data. Several examples in this chapter have been used in discussing the
characteristics and there are several more that will be available in the companion website to this topic
(http://booksite.elsevier.com/9780124058910)
.
Chapter 3 will focus on Big Data processing architec-
ture and techniques.
