What Are They Thinking? With Oracle Application Express and

Oracle Data Miner

Brendan TierneyRoel Hartman

Agenda Who are we The Scenario Oracle Data Miner & DBA tasks APEX the Poor/Smart mans BI tool Live Demo

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 the!OOW Book

Store

Available in eBook & Print formats

Book Signing Wed @1pm 4

The Scenario

But ? Is there an Alternative?

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The Scenario We have a number of products We know the opinions from some sectors Can we use Data Mining to predict opinions Can we build interactive dashboards in the DB

! Data Mining & Interactive Dashboards with APEX all in side the Database

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

How is it done with Oracle Text & Oracle Advanced Analytics

Product Review

Human Labelling Tokenization Stop Word Punctuation

Text Ready for DM

New Product Reviews

Machine Learning Algorithms

Evaluation Model

Sentiment Score

Visualisation / Presentation

Actionable Insights

Let us have a closer look at what Oracle Text does

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.

Today 28 Sept we are at OUF Sunday .

Stop Words

For analyzing twitter we can include hash tags !e.g. #OOW14

Stop Words

Today 28 Sept we are at OUF Sunday .

For analyzing twitter we can include hash tags !e.g. #OOW14

Punctuations

Characters that are defined as punctuations are removed from a token before text indexing . , : ; @ ~ # { } [ ] + = - _ ( ) * & ^ % $ ! ` \ | / ?

Product Review Tokenization Stop Word Punctuation

Text Ready for DM

Human Labelling

Today 28 Sept OUF Sunday .

Oralytics

Oracle Advanced Analytics Option

Advanced Analytics Option Technique Algorithms ApplicabilityClassification Logistic Regression (GLM)

Decision Trees Nave Bayes Support Vector Machine

Classical Statistical Technique Popular / Rules / Transparency Embedded Wide / Narrow Data / TextRegression ! Multiple Regression Support Vector Machine Classical Statistical Technique Wide / Narrow Data / Text

Anomaly Detection ! One Class SVM Lack ExamplesAttribute Importance ! Minimum Descriptive Length Attribute Reduction Identify Useful Data

Reduce Data Noise

Association Rules ! Apriori Market Basket Analysis Link AnalysisClustering 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

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''

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

All(of(these(are(

FREE((with(the(Database(

These(are(o:en(forgo

The models are first class objects in the DB Just like calling any other function They are fast

Built a model on 550,000 records in 2 minutes Scored 1.2M records in 52 seconds (on a mid spec development sever)

>80M records per hour without using the Parallel Option

APEX - POOR MANS BI TOOL

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+ any JavaScript charting engine you like

And then there is Interactive Reports

DEMO

- Create a visualisation of your model - Dashboard - Use your model for workflow decisions

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APEX -

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POORSMARTMANS BI TOOL

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All inside the DatabaseBrendan TierneyRoel Hartman

brendan.tierney@oralytics.com @brendantierney

roel@apexconsulting.nl @roelh