Canonical model to normalize disparate persistent data sources

Data processing: artificial intelligence – Neural network – Learning method

Reexamination Certificate

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Reexamination Certificate

active

07461039

ABSTRACT:
A semantic database transaction monitor is provided that monitors database transactions by taking advantage of database replication technology. The invention receives one or more event streams of transaction data from one or more database replication software agents, originally from transaction logs, and then classifies each transaction, utilizing an inference engine populated with one or more source ontologies and a canonical ontology so that transaction metadata are normalized. The invention then can be utilized to create a data store across multiple databases for reporting and analysis. The invention can also be used to feed normalized database transactions to real-time graphics software for real-time reporting or alerting. Because the process obtains data from event streams, it does not significantly drain the resources of the databases and can provide virtually real-time monitoring. Moreover, it does not require recoding for updates to the databases, but only changes to the ontologies read at runtime.

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