Analytics Magazine Analytics Magazine, November/December 2014 - Page 49

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 n o v e m b e r / d e c e m b e r 2 014 | 49