Interstage Big Data Complex Event Processing Server (hereafter, referred to as "this product") is software that analyzes and assesses massive volumes of event data in real time.
In recent years, there has been a growing demand from companies wanting to use ever-changing event data generated in massive volumes, such as location information sent from smart phones and machines' operation logs, in order to leverage their business activities.
The need to process these kinds of event data in real time has drawn attention to the CEP (Complex Event Processing) technique, which analyzes and assesses massive volumes of data with faster response times than ever before.
The inclusion of a high-performance complex event processing engine (hereafter, referred to as the "high-performance CEP engine"), which integrates a unique high-speed filter processing technique with the complex event processing technique so suitable for processing massive volumes of event data and provides the enhanced processing performance and convenience it needs to support real-time use of massive volumes of event data in corporate systems.
Some scenarios for using this product are described below.
Provide real-time services by utilizing location information
This product allows high-speed matching of real-time customer location information with information registered in the master data, such as customer information and store information. This allows companies to instantly provide services to suit the attributes of customers, such as "provide store coupons to people visiting the vicinity of a store, in real time".
Improve service by monitoring the operation status of sold products
This product allows real-time monitoring the fault prediction of hardware sold to customers, by collecting the operation logs of hardware. This enhances machine availability by allowing preventive maintenance to be performed, which previously may have been impossible in periodic maintenance due to cost or other factors.
The operation logs collected by this product can also be accumulated and analyzed in a Hadoop system, which allows the detection of more refined prediction patterns. Reflecting these patterns in the complex event processing rules allows the implementation of more efficient maintenance services.
