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Learning Applied Artificial Intelligence with Oracle

Oracle Database Tips by Donald Burleson


The highly-theoretical world of artificial intelligence (AI) has been the traditional bastion of academia, with graduate-level computer science students writing their dissertation on AI theory, using non-procedural languages (Lisp, Prolog, etc.), and mile-long algorithms.  I've been implementing expert systems, decision support systems and applied AI for over 20 years and I've seen the technology change dramatically, from incomprehensible multi-page "if statements" to complex neural networks. My Masters thesis was my first publication on expert systems, and I've been working with these systems since completing grad school:
"Design and Implementation of a Decision Support System for Academic Scheduling"

Applying science to analyzing Oracle

Many Oracle professional misunderstand the application of scientific principles to solving Oracle performance problems.

Some neophytes think that only hard-and-fast mathematical "proofs" are useful for analyzing and forecasting Oracle performance, and believe that if there is a single exception to a decision rule, then the rule is wrong and not useful.

Analyzing Oracle performance uses the exact same techniques as the Oracle Data Mining (ODM) product.  Oracle data warehouse experts know that decision rules can never be 100% accurate,  and it's all-about creating rules, analyzing their predictive probabilities and using heuristic techniques to solve real-world Oracle database problems!

For Oracle database analysis, applied AI falls into these categories:

Let's start with a quick review of AI technology and then see how it is applied to Oracle.

Basics of applied artificial intelligence

Back in the 1970's, very few people had ever seen a real computer.  In the days before PC's and hand calculators, computers cost millions of dollars and they were hidden in the "glass house".  Hence, most laymen had gross misconceptions about the real power of AI.  The movie "2001: A Space Odyssey (1968)" featured the HAL-2000, an AI computer with personality. 

Playing on this false public perception, the ELIZA project at MIT fooled hundreds of laymen.  Properly presented, most experimental subjects were impressed with ELIZA's intelligence!

In the real-world, AI software still struggles with the mundane, tasks like parsing and deriving meaning human English.  Let's take a look why the HAL-2000 failed to materialize according to Stanly Kubrick's premonition!

Today, artificial intelligence centers around functional disciplines such as human cognition and game theory, but we are starting to see some practical applications of complex decision rules.  Here is a great history of artificial intelligence over the centuries. 

As of 2015, there are three types of intelligent systems that utilize Oracle databases:

  • Decision Support Systems - By definition, a Decision Support System (DSS) attempts to assist in the solution of a "semi-structured" problem, a problem that has some decision rules,  but also relies on human intuition.  The DSS takes care of the structured component and leave the actual decision to the human expert.  MYCIN is a great example of a DSS, a tool that assists doctors in the selection of antibiotic medicines, and Ion, a DSS for Oracle DBA.
     
  • Expert Systems - Expert systems seek to codify expert knowledge and advice against a set of circumstances.  The well-structured decision rules are collected from a human expert and then applied and validated against real-world data.
     
  • Artificial Intelligence - AI differs from DSS and expert systems  in the sense that the AI software automatically learns from its empirical experiences. It's this experiential learning component that distinguishes AI, especially in the area of simulating human cognition.  For example, just like a toddler, an AI routine might classify all 4-legged animals as "doggie", until it refines it's decision rules.
     
  • Artificial Stupidity - Artificial stupidity (AS) is the use of automated technology to produce ludicrous output.  There are many primitive online systems for artificial stupidity, such as my Oracle Jargon Generator.  For offshoots, we also see artificial stupidity (See NASA, the national artificial stupidity association) and artificial artificial intelligence (a cute new Amazon name for a DSS). 

For real-world AI and DSS applications, the foremost newcomer is the Amazon mechanical Turk, a new Services Oriented Architecture (SOA) tool for incorporating human intelligence into web services:

"Amazon Mechanical Turk provides a web services API for computers to integrate "artificial artificial intelligence" directly into their processing by making requests of humans.

Developers use the Amazon Mechanical Turk web service to submit tasks to the Amazon Mechanical Turk web site, approve completed tasks, and incorporate the answers into their software applications."

The Amazon mechanical Turk works like a decision support system, using human experts to manage the semi-structured and unstructured components of problem solving:

"Humans are much more effective than computers at solving some types of problems, like finding specific objects in pictures, evaluating beauty, or translating text. . .

Amazon Mechanical Turk is being used to increase the quality of A9's BlockView pictures that show users street-level pictures of businesses. These HITs ask people to select from several photographs the one that best presents the front of a business. "

Artificial Intelligence and expert systems using Oracle

However, there has been a AI research that uses databases as their "persistence layer".  Databases are required in several areas of AI:

  • Algorithm storage and versioning - In AI, complex decision rules must be stored, modified according to data inputs.
     
  • Data Universe storage - Most AI algorithms are validated using real-world heuristics.

Today, there are thousands of AI routines that store their rules and data inside Oracle databases.  Most of these applied artificial intelligence are used to interpret real-world data.  An example is the Oracle Data Mining ODM tool, which allows for the storage of chi-square multivariate algorithms and allows them to be run against datasets stored within Oracle.

Artificial Intelligence and expert systems for Oracle Management

Oracle DBA's have always been interested in automating their decision rules to build AI systems that simulate their real-world behavior.  In Oracle 10g, we see applied AI for the automated tools such as ADDM and AMM, where database performance feedback is sent to an AI engine which interprets and analyses the feedback and changes the database configuration to meet current processing demands.

We also see the application of decision rules to simulate human analysis in the STATSPACK analysis tools.  Oracle has developed intelligent advisors for the data buffer cache, pga, and pools, incorporating them into all 10g AWR time-series performance reports.

We also see work in expert systems to interpret time-series performance reports (STATSPACK and AWR (for 10g)).  The idea is to simulate the behavior of a Oracle tuning expert.  The decision rules:

  1. Rule Identification
  2. Rule correlation to other existing rules
  3. Rule is weighted for overall importance
  4. Rule is generalized to apply to all situations
  5. Rule is quantified and programmed into the engine

Early efforts to simulate a human expert provided limited success, but no researcher has yet been able to model the "human intuition" shown by human experts. 

New Research Initiative for Oracle performance analysis

Finding internal Oracle bottlenecks is very challenging.  Analyzing a thousand-line Oracle performance report is not trivial, and many dozens of observation points may be required to formulate a decision rule.  Further, the generalization of these complex rules may make them prone to "false positives", the reporting of a non-existent bottleneck.

The StatspackAnalyzer project shows great promise as an expert system because of the large user base where users submit their reports, evaluate the software's feedback, and suggest refinements to the decision rules.

We also see the Ion for Oracle, a decision support system for Oracle professionals. Ion displays time-series metrics, allowing the human expert to visualize hidden performance trends.

 

AI & Expert system databases in academia

In grad school, all IT and CS students take courses like Operations Research where they learn to develop complex decision rules and them apply them to real world datasets.  Using Oracle as the back-end storage of data and decision rules is a great way to prepare for real-world applications of expert systems, DSS and AI.  Also, advanced statistics courses (multivariate analysis) are a good way to prepare for a career in Oracle data mining and Business Intelligence (BI).

 

If you like Oracle tuning, you may enjoy the new book "Oracle Tuning: The Definitive Reference", over 900 pages of BC's favorite tuning tips & scripts. 

You can buy it direct from the publisher for 30%-off and get instant access to the code depot of Oracle tuning scripts.


 

 

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Note: This Oracle documentation was created as a support and Oracle training reference for use by our DBA performance tuning consulting professionals.  Feel free to ask questions on our Oracle forum.

Verify experience! Anyone considering using the services of an Oracle support expert should independently investigate their credentials and experience, and not rely on advertisements and self-proclaimed expertise. All legitimate Oracle experts publish their Oracle qualifications.

Errata?  Oracle technology is changing and we strive to update our BC Oracle support information.  If you find an error or have a suggestion for improving our content, we would appreciate your feedback.  Just  e-mail:  

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