NonStop Systems Introduction

Requirements of ZLE Systems
NonStop Systems Introduction—527825-001
2-15
Decision Support
The following sequence summarizes how ZLE data is extracted for data mining, then
routed back to the ZLE system:
1. Customers interact with the ZLE system throughout the day.
2. Their interactions are downloaded to a mining system (running on a separate
platform).
3. Mining tools are used to develop predictive models based on these interactions; for
example, a model predicting the likelihood that particular customers will buy
particular products.
4. The predictive models are then routed back to the ZLE hub, where they can be
used in subsequent interactions with customers.
For example, company executives for a chain of retail stores must offer the products
customers want to buy, and must make sure they have the right amounts of those
products in their shelves—enough to satisfy demand but not an oversupply that would
result in outdated stock that could not be sold.
Figure 2-5. Decision Support in a ZLE Framework
Local
data
Local
data
Local
data
Operational systems
NonStop server
vst014.vsd
Data store
ZLE hub
Data mining and analysis
Data
warehouse
JDBC
(database
connectivity)
NonStop
SQL/MX
transactions
extraction
new
models