What Are They Thinking? With Oracle Application Express and Oracle Data Miner

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What Are They Thinking? With

Oracle Application Express and

Oracle Data Miner

Brendan Tierney

Roel Hartman

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Agenda

▪ Who are we

▪ The Scenario

▪ Graphs & Charts in APEX -‐ Live Demo

▪ Oracle Data Miner & DBA tasks

▪ Including Oracle Data Mining in APEX

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▪ Currently:

▪ Lecturer

▪ DBA

▪ Data Mining Consultant

▪ BI & Data Architect

▪ Trainer

▪ Working with Oracle products since 1992/1993

▪ Oracle version 5 up to 11g

▪ Oracle Reports (RPT), ReportWriter I, RPT, …

▪ Forms 2.3…

▪ Oracle Data Miner since 2005

▪ Data Warehousing since 1997

▪ Data Mining since 1998

▪ Analytics since 1993

Brendan Tierney

Available in

eBook

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The Scenario

But ?

Is there an Alternative?

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The Scenario

▪ We have a number of products

▪ We got the opinions from Amazon (star rating)

▪ Can we use Data Mining to predict opinions

▪ Can we build interactive dashboards in the DB

▪ Data Mining & Interactive Dashboards with APEX

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APEX -‐ POOR MAN’S BI TOOL

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DEMO

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ORACLE ADVANCED ANALYTICS

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Technique Algorithms Applicability

Classification Logistic Regression (GLM)

Decision Trees

Naïve Bayes

Support Vector Machine

Classical Statistical Technique

Popular / Rules / Transparency

Embedded

Wide / Narrow Data / Text Regression

Multiple Regression

Support Vector Machine

Classical Statistical Technique

Wide / Narrow Data / Text

Anomaly Detection

One Class SVM Lack Examples

Attribute Importance

Minimum Descriptive Length Attribute Reduction

Identify Useful Data

Reduce Data Noise Association Rules

Apriori Market Basket Analysis

Link Analysis Clustering Enhanced K-‐Means

O-‐Cluster

Expectation Maximization

Product Grouping

Text Mining

Gene and Protein Analysis Feature Extraction Non-‐Negative Matrix Factorization

Principal Components Analysis

Singular Vector Decomposition

Text Analysis

Feature Reduction

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Oracle'Data'Mining'

! 

PL/SQL'Package'

! 

DBMS_DATA_MINING'

! 

DBMS_DATA_MINING_TRANSFORM'

! 

DBMS_PREDICTIVE_ANALYTICS'

! 

SQL'FuncBons'

– 

PREDICTION'

– 

PREDICTION_PROBABILITY'

– 

PREDICTION_BOUNDS'

– 

PREDICTION_COST'

– 

PREDICTION_DETAILS'

– 

PREDICTION_SET'

– 

CLUSTER_ID'

– 

CLUSTER_DETAILS'

– 

CLUSTER_DISTANCE'

– 

CLUSTER_PROBABILITY'

– 

CLUSTER_SET'

– 

FEATURE_ID'

– 

FEATURE_DETAILS'

– 

FEATURE_SET'

– 

FEATURE_VALUE'

! 

12c'–'PredicBve'Queries'

! 

aka''Dynamic'Queries'

! 

TransiBve'dynamic'Data'Mining'models'

! 

Can'scale'to'many'100+'models'all'in'one'

statement''

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Sta$s$cal(Func$ons(in(Oracle(

All(of(these(are(

FREE

((

with(the(Database(

These(are(o:en(

forgo<en(about(

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Text Mining in Oracle

▪ Natural language processing

– It deals with the actual text element. It transforms it into a format that the

machine can use.

▪ Artificial intelligence / Machine Learning

▪ It uses the information given by the NLP and uses a lot of maths to determine

whether something is negative or positive.

▪ All done in Oracle Data Miner (using Oracle Text)

▪ Allows Data Analysts to do this

▪ Isolated from the underlying complexity

Oracle Text

Oracle Data

Mining

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How is it done with Oracle Text & Oracle Advanced Analytics

Product

Review

Labelling

Human

Tokenization

Stop Word

Punctuation

Text Ready for

DM

New

Product

Reviews

Machine

Learning

Algorithms

Evaluation

Model

Sentiment

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Tokenization

▪ Tokenization is the process of breaking a stream of text up into words,

phrases, symbols, or other meaningful elements called tokens.

▪ The list of tokens becomes input for further processing such

as

parsing

or

text mining

▪ Tokens are separated by

whitespace

characters, such as a space or line break, or by

punctuation characters.

▪ Punctuation and whitespace may or may not be included in the resulting list of tokens.

Today 28 Sept we are at OUF Sunday.

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Stop Words

For analyzing twitter we

can include hash tags

e.g. #OOW14

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Stop Words

Today

28 Sept

we

are

at

OUF

Sunday

.

For analyzing twitter we

can include hash tags

e.g. #OOW14

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Punctuations

▪ Characters that are defined as punctuations are removed from a

token before text indexing

. , : ; ‘ @ ~ # { } [ ] + = -‐ _ ( ) * & ^ % $ £ € “ ! ` ¬ ¦ \

| / ?

Product

Review

Labelling

Human

Tokenization

Stop Word

Punctuation

Text Ready for

DM

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Using your Sentiment Analyzer

Add the view to

Physical layer of the

BI Repository

Then add to the

Business Model

layer

CASE_ID PRED PROBABILITY CUSTOMER_SENTIMENT SENTIMENT_V CUSTOMER CUSTOMER_SENTIMENT CUSTOMER_SENTIMENT CUSTOMER TRANSACTIONS CUSTOMERTRANSACTIONS TITLE NAME STATUS SEX AGE RATING LOCATION DEFAULTS REGION PRED PROBABILITY

What Are They Thinking? With Oracle Application Express and Oracle Data Miner