hypothesis much more quickly. Some examples of such applications include:
• A product company getting realtime feedback for its new releases using
data from social media in real-time, postproduct launch.
• Real-time recommendations for
food and entertainment based on a customer’s location.
• Traffic signal operations based on
real-time information of traffic volumes.
• E-commerce websites and credit
firms detecting customer transactions
being authentic or fraudulent in real time.
• Providing more targeted coupons
based on customers recent purchases
and location.
From a technology architecture perspective, a cloud-based ecosystem can
enable users to build an application that
detects, in real time, fraudulent customers based on their demographic information and prior financial history. Multiple
algorithms help detect fraud, and the output is aggregated to improve prediction
accuracy.
But Why Use the Cloud?
A system that allows the development
of applications capable of churning out results in real-time needs multiple services
running in tandem and is highly resource
intensive. By deploying the system in the
a na l y t i c s
cloud, maintenance and load balancing
of the system can be handled efficiently
and cost effectively. In fact, most cloud
systems function as ”pay as you go” and
only charge the user for actual usage vs.
maintenance and monitoring costs. “Intelligent” cloud systems also provide recommendations to users to dial up/down
resources available to run the fraud detection algorithms without worrying about
the data-engineering layer.
Since multiple algorithms are run on
the same data to enable fraud detection,
a real-time agent paradigm is needed to
run the algorithms. An agent is an autonomous entity that may expect inputs and
send outputs after performing a set of
instructions. In a real-time system, these
agents are wired together with directed
connections to form an agency. An agent
typically has two behaviors: cyclic and
triggered. Cyclic agents, as the name
suggests, run continuously in a loop and
do not need any input. These are usually the first agents in an agency and are
used for streaming data to the agency by
connecting to an external real-time data
source. In short their tasks are “well-defined and repetitive.”
A triggered agent, on the other hand,
runs every time it receives a message
from a cyclic agent or another triggered
agent. The “message” defines the function that the triggered agent needs to
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