Streaming Analytics with kdb+: Detecting card counters in Blackjack

17 May 2017 | , , , , , , , ,
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With the growth and acceleration of the Internet of Things (IoT), data collection is increasingly being performed by sensors and smart devices. Consuming and analyzing the massive amounts of data transmitted from sensing devices is considered the next Big Data challenge.

A key problem in processing large quantities of data in real-time is the detection of event patterns, and this is why streaming analytics, also known as Event Stream Processing (ESP), is becoming a mainstream solution in IoT.

Kx engineers Caolan Rafferty and Krishan Subherwal recently wrote a technical paper that highlights the use case of kdb+ with ESP.

Caolan and Krishan write:

There are a number of measures used to protect casinos against card counters. These countermeasures constrain the card counters that may be playing in the casino at any given time but heavily tax the casino’s efficiency, costing the casino industry millions of dollars every year. Examples of these countermeasures are shuffling more frequently, reducing the number of hands played, and not permitting mid-game entry.

The purpose of this paper is to highlight a use case of kdb+ with ESPs to detect card counters in the game of Blackjack in real-time. kdb+ offers an unrivalled performance advantage when it comes to capturing, analyzing and storing massive amounts of data in a very short space of time, making it the data-storage technology of choice for many financial institutions across the globe, and more recently other industries such as telecommunications and pharma.

To read the rest of their white paper, which includes kdb+ coding examples, please follow the link to the left.

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