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Don Burleson Blog 







Understanding Oracle knowledge engineering

Oracle Database Tips by Donald BurlesonOctober 1, 2015

Since the earliest days of business computing, the goal has been to identify solid, structured activities and automate them. As a result, the first business processes to be automated were well-structured redundant tasks such as payroll processing.


As the decades passed, information systems became more sophisticated at capturing and deploying human intelligence within computer systems, and now we see these types of systems:

  • Expert Systems - These online systems capture a structured task and mimic human processing.  An expert system makes the decision without the aid of any human intuition.  An example would be Mycin, a system that applies physician intelligence in analyzing blood samples.

  • Decision Support Systems - A DSS is a computerized system that recognizes that human intuition is difficult to quantify and automate.  In a DSS, the human makes the decision, guided by software that automates the more structured aspects of the problem domain.

The line between an expert system and a decision support system blurs in some cases when what is thought to be an intuitive process is actually a well-structured problem with extremely complex decision rules.

In one notable case, a major soup manufacturer was about to loose a long-term employee of forty years, who knew every intricacy of the tricky soup vats within the company. 

Initially setting out to create a DSS, the decision analyst quizzed the employee over a period of months and discovered that what was once thought to be intuition was actually the application of a large set of well structured decision rules.  When this soup vat expert would say something like "I have a feeling that the problem is X", it appeared to be human intuition to those less knowledgeable observers. 

However in reality it was the application of a long forgotten decision rule or an experiential case for which the individual had since lost conscious knowledge.  The application of the decision support system technology eventually led to an expert system.  This allowed the forty year worker to retire comfortably, with the knowledge that all of his years of decision rules had in fact been quantified, helping the soup company carry on without him making even faster and better decisions as a whole.

A knowledge engineering system for Oracle data cleansing

If we start by examining known data errors to find common patterns, a qualified software engineer can design Oracle-based programs to detect these types of errors and quickly clean-up a large amount of transposition errors, and successively refine the model to identify less obvious data anomalies.  We can also search for statistical "outliers", data that violates the norms of the database as-a-whole. 

By using well-understood best practices for Oracle data cleansing a robust and flexible system can be created to dramatically reduce data anomalies.  Using an iterative cycles of refining the decision rules, the DSS evolves to become increasingly accurate and powerful.


This is a DSS for Oracle data cleansing in a nutshell.  Note that we start by examining the "nature" of known data errors and seek "fishy" data (statistically valid outliners) for creating the suggestion lists for the human expert (the DQO).

The DQO then manually resolved the errors and works with the DBA to refine the decision rules until they are 100% complete and accurate using the "feedback loop" of successive rule refinement.  At that point, that component of the Oracle data cleansing is automated, becoming an "expert system" component of the DSS.

For expert Oracle data cleansing support and data scrubbing consulting, use an expert from BC.  We understand the powerful Oracle data unification tools, and we can aid in improving the data quality of any Oracle database, large or small.




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