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narrate a story. In the Big Data Analytics architecture, the moment a customer
walks into a retail store or connects with a call center, the orchestrator uses
identifying information to pull all the relevant information about this customer.
The third task is to
score and prioritize alternatives
to establish the focus
area. It always fascinates me in U.S. football when half a dozen players wrestle
with each other to stop the ball. The commentator has the tough task of watch-
ing the ball and the signiicant players while ignoring the rest. Similarly, in the
Big Data Analytics architecture, we may be dealing with hundreds of predictive
models. In a relatively very short time (less than one second in most cases), the
analysis system must score these models on available data to compare the most
important alternative and pass it on for further action. In online advertising, the
bidding process may conclude in less than 100 milliseconds. The Demand Side
Platform (DSP) must view a number of competing advertisement candidates and
select the one that is most likely to be clicked by the customer.
The last task is to
package all the real-time evidence
. The information is
turned over to the orchestration layer for storage and future discovery. The
conversation layer can now focus on the next task, while the orchestration layer
annotates the data and sends it to the discovery layer.
A number of software products are emerging to provide technical capabil-
ities for real-time identiication, data synthesis, and scoring commonly referred
to as
stream computing
. Stream computing is a new paradigm. In “traditional”
processing, one can think of running analytic queries against historical data—for
instance, calculating the distance walked last month from a data set of subscrib-
ers who transmit GPS location data while walking. With stream computing, one
can identify and count, as well as ilter and associate, events from a number of
unrelated streams to score alternatives against previously speciied predictive
models. IBM's InfoSphere Streams has been successfully applied to the conver-
sation layer for low-latency, real-time analytics.
5.2 Orchestration and Synthesis Using Analytics Engines
Nowadays, it is impossible to imagine a live television program without orches-
tration. A highly productive team has replaced what used to be a “sportscaster”
in the early days of sports coverage. A typical television production involves a
number of cameras offering a variety of angles to the players, in addition to stock
footage, commentators, commercial breaks, and more. The director provides the
orchestration, assisted by a team of people who organize the resources and facili-
tate the live event.
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