Oracle vs SQL Server

Dr. Alex Wang

Oracle Text

• Oracle Text uses standard SQL to do almost everything.

• Full-text retrieval technology, deal with unstructured data.

• Data source could be database table, flat files, web sites.

• Index, search, analyze text and documents.

• Searching: keyword searching, context query, pattern matching, thematic queries, HTML/XML section searching.

• Use relevance-ranking to improve search quality.

• Supported formats: PDF, MS Office, HTML, XML

Search Operators used in Oracle

Context search• Near - return a score based on the proximity of two or more terms.

Pattern search• Fuzzy - spelled similar. • Soundex - sound alike. • Stem - search for all terms with the same root.

Use thesaurus • Preferred Term - replace query term with prefered term define in a thesaurus.• Related Term - Expand to all related term defined in a thesaurus. • Synonym - Expand to all terms defined as synonyms. • Narrow Term - Expand to all terms defined as the narrower/lower level terms. • Broader Term - Expand to all terms defined as broader/higher level terms. • Top Term -

Search Operators used in SQL Server

• CONTAINS can search for: • A word near another word. • The prefix of a word or phrase. • Soundex Function (for search sound alike).• A word inflectionally generated from another (for

example, the word drive is the inflectional stem of drives, drove, driving, and driven).

• A word that is a synonym of another word using thesaurus (for example, the word metal can have synonyms such as aluminum and steel).

Feature Oracle Microsoft

Available in SE, EE EE

Decision Tree Y Y

Support Vector Machine Y N

Neural Network N Y

Naive Bayes Y Y

Adaptive Bayes Network Y N

K-means Y Y

Expectation Maximization N Y

Orthogonal Clustering Y N

Path cluster N Y

Minimal Descriptor Length Y N

Time Series Y Y

Association Rules Y Y

Note: Minimal Descriptor Length, identifies the relative importance of an attribute in predicting a given outcome.

Oracle emphasize PL/SQL statement

Simple Prediction Query

Question:

Select all customers who have a high propensity to attrite (> 80% chance)

SQL Query:

SELECT A.cust_name, A.contact_infoFROM customers AWHERE PREDICTION_PROBABILITY(tree_model, ‘attrite’ USING A.*) > 0.8

An Example of Oracle Text Mining

• Building a DT Models

• CREATE TABLE dt_settings (setting_name VARCHAR2(30),setting_value VARCHAR2(30));

BEGIN-- Populate settings tableINSERT INTO dt_sample_settings VALUES(dbms_data_mining.algo_name, dbms_data_mining.algo_decision_tree);COMMIT;

DBMS_DATA_MINING.CREATE_MODEL(model_name => 'sales_type_model',mining_function => dbms_data_mining.classification,data_table_name => 'sales_dataset',case_id_column_name => 'sales_id',target_column_name => 'sales_type',settings_table_name => 'dt_settings');END;

An Example of SQL Server Text Mining

• A Tutorial for Text Classification using SQL Server

2005 Beta2 Data Mining

• Peter Pyungchul Kim

• SQL Business Intelligence

• Microsoft Corporation

• http://www.sqlserverdatamining.com/dmcommunity/_tutorials/688.aspx

Data Source

• 5000 postings from 5 news groups

• We know which posting belong to which group

• Flat text file

• Goal: create a model based on these data to classify each posting to its group

• Randomly chose 70% for training, 30% for testing.

SQL Server

• You can do it by click through SQL Server GUI tools.

• 1. SQL Mgmt Studio - Create database, import the data

• 2. Business Intelligence Development Studio – Build a dictionary, term vectors.

• 3. Build/Test data mining models

Compare Classification Results