Recent
years have seen
emergence of cloud
computing platforms which can be used
as utility infrastructures for performing large-scale
data analytics. Many data analytics applications require processing of large-scale graph data. As the
sources, types, and amounts of data continue to expand, so will the need for
different kinds of analytics to make something of that data. Unfortunately,
there is not a one-size-fits-all approach to analytics -- no magic pill that
will get your organization the insight it needs to stay competitive. Graph
analytics has emerged as the new hot topic, but to what end? What is the impact
of graph analytics technology on organizations seeking to discover the cause,
effect, and influence of events on business outcomes? Graph Analytics is a
rapidly developing area where a combination of graph-theoretic, statistical,
and database techniques are applied to model, store, retrieve, and perform
analyses on graph-structured data. These techniques enable us to understand the
structure of a network and how it changes in different conditions. They can
also find paths between pairs of entities that satisfy different constraints,
identify clusters or closely interacting subgroups inside a graph, or find
subgraphs that are similar to a given pattern.
Link to article: https://infocus.emc.com/william_schmarzo/how-can-graph-analytics-uncover-valuable-insights-about-data/
1) why we use graph ?
2) Computer software for graphs?
3) Graph analysis will push big data evolution to the next plateau of scale and sophistication?