Every day we are challenged with more data, more use cases and an ever increasing demand for analytics. In this talk Bjorn will explain how autonomous data management and machine learning help innovators to more productive and give examples how to deliver new data driven projects with less risk at lower costs.

1. Bjoern Staender
Analytics & Data Innovation
Oracle Deutschland B.V. & Co KG
September 17, 2019
How to be more productive with
Autonomous Data Management and
Machine Learning

2. Safe harbor statement
The following is intended to outline our general product direction. It is intended for information
purposes only, and may not be incorporated into any contract. It is not a commitment to deliver
any material, code, or functionality, and should not be relied upon in making purchasing
decisions.
The development, release, timing, and pricing of any features or functionality described for
Oracle’s products may change and remains at the sole discretion of Oracle Corporation.
© 2019 Oracle

3. Precision
Agriculture
Marketing
Analytics
Data
Monetization
Customer
Experience
Higher
Education
Smart
Cities
Factory of
the Future
Gene Therapy
© 2019 Oracle
How to managa and analyze all this data ?

4. Manage and Analyze All Your Data Assets
Big Data SQL / R
R / Python / SQL
Object
Store
“Engineered Features”
Derived attributes that
reflect domain
knowledge—key to best
models e.g.:
• Counts
• Totals
• Changes
over time
Boil down the Data Lake
Architecturally, Many
Options and
Flexibility
* OML4Py – coming soon© 2019 Oracle

5. Algorithms Operate on Data
ML and AI are just “Algorithms”
Move the Algorithms - Not the Data! It Changes Everything!
© 2019 Oracle

6. CLASSIFICATION
• Naïve Bayes
• Logistic Regression (GLM)
• Decision Tree
• Random Forest
• Neural Network
• Support Vector Machine
• Explicit Semantic Analysis
CLUSTERING
• Hierarchical K-Means
• Hierarchical O-Cluster
• Expectation Maximization (EM)
ANOMALY DETECTION
• One-Class SVM
TIME SERIES
• State of the art forecasting using
Exponential Smoothing
• Includes all popular models
e.g. Holt-Winters with trends,
seasons, irregularity, missing data
REGRESSION
• Linear Model
• Generalized Linear Model
• Support Vector Machine (SVM)
• Stepwise Linear regression
• Neural Network
• LASSO *
ATTRIBUTE IMPORTANCE
• Minimum Description Length
• Principal Comp Analysis (PCA)
• Unsupervised Pair-wise KL Div
• CUR decomposition for row & AI
ASSOCIATION RULES
• A priori/ market basket
PREDICTIVE QUERIES
• Predict, cluster, detect, features
SQL ANALYTICS
• SQL Windows, SQL Patterns,
SQL Aggregates
• OAA (Oracle Data Mining + Oracle R Enterprise) and ORAAH combined
• OAA includes support for Partitioned Models, Transactional, Unstructured, Geo-spatial, Graph data. etc,
Oracle Machine Learning Algorithms
FEATURE EXTRACTION
• Principal Comp Analysis (PCA)
• Non-negative Matrix Factorization
• Singular Value Decomposition
(SVD)
• Explicit Semantic Analysis (ESA)
TEXT MINING SUPPORT
• Algorithms support text
• Tokenization and theme extraction
• Explicit Semantic Analysis (ESA) for
document similarity
STATISTICAL FUNCTIONS
• Basic statistics: min, max,
median, stdev, t-test, F-test,
Pearson’s, Chi-Sq, ANOVA, etc.
R PACKAGES
• CRAN R Algorithm Packages
through Embedded R Execution
• Spark MLlib algorithm integration
EXPORTABLE ML MODELS
• REST APIs for deployment
© 2019 Oracle

7. STATISTICAL FUNCTIONS
• Descriptive statistics
(e.g. median, stdev, mode, sum,
etc.)
• Hypothesis testing
(t-test, F-test, Kolmogorov-
Smirnov test, Mann Whitney
test, Wilcoxon Signed Ranks
test)
• Correlations analysis
(parametric and nonparametric
e.g.
Pearson’s test for
correlation, Spearman's rho
coefficient, Kendall's tau-b
correlation coefficient)
• Ranking functions
• Cross Tabulations with Chi-
square statistics|
• Linear regression
• ANOVA (Analysis of variance)
• Test Distribution fit
(e.g. Normal distribution
test, Binomial test, Weibull
test, Uniform
test, Exponential test, Poisson
test, etc.)
• Statistical Aggregates (min,
max, mean, median, stdev,
mode, quantiles, plus x
sigma, minus x sigma, top n
outliers, bottom n outliers)
Oracle SQL Functions
ANALYTICAL SQL
• SQL Windows
• SQL Aggregate functions
• LAG/LEAD functions
• SQL for Pattern Matching
• Additional approximate query
processing: APPROX_COUNT,
APPROX_SUM,
APPROX_RANK
• Regular Expressions
© 2019 Oracle

8. SQL Statistical Functions
SELECT SUBSTR(cust_income_level, 1, 22) income_level,
AVG(DECODE(cust_gender, 'M', amount_sold, null)) sold_to_men,
AVG(DECODE(cust_gender, 'F', amount_sold, null)) sold_to_women,
STATS_T_TEST_INDEPU(cust_gender, amount_sold, 'STATISTIC', 'F') t_observed,
STATS_T_TEST_INDEPU(cust_gender, amount_sold) two_sided_p_value
FROM customers c, sales s
WHERE c.cust_id = s.cust_id
GROUP BY ROLLUP(cust_income_level)
ORDER BY income_level, sold_to_men, sold_to_women, t_observed;
Simple SQL Syntax—Statistical Comparisons (t-tests)
STATS_T_TEST_INDEPU (SQL) Example;
P_Values < 05 show statistically
significantly differences in the amounts
purchased by men vs. women
Compare AVE Purchase Amounts Men vs. Women Grouped_By INCOME_LEVEL
© 2019 Oracle

9. OML for SQL Model Build & SQL Apply Prediction
BEGIN
DBMS_DATA_MINING.CREATE_MODEL(
model_name => 'BUY_INSUR1',
mining_function => dbms_data_mining.classification,
data_table_name => 'CUST_INSUR_LTV',
case_id_column_name => 'CUST_ID',
target_column_name => 'BUY_INSURANCE',
settings_table_name => 'CUST_INSUR_LTV_SET');
END;
/
Simple SQL Syntax—Classification Model
Select prediction_probability(BUY_INSUR1, 'Yes'
USING 3500 as bank_funds, 825 as checking_amount, 400 as credit_balance, 22 as age,
'Married' as marital_status, 93 as MONEY_MONTLY_OVERDRAWN, 1 as house_ownership)
from dual;
ML Model Build (PL/SQL)
Model Apply (SQL query)
© 2019 Oracle

10. OML for SQL Model Build
BEGIN
DBMS_DATA_MINING.CREATE_MODEL(
model_name => 'BUY_INSURANCE_AI',
mining_function => DBMS_DATA_MINING.ATTRIBUTE_IMPORTANCE,
data_table_name => 'CUST_INSUR_LTV',
case_id_column_name => 'cust_id',
target_column_name => 'BUY_INSURANCE',
settings_table_name => 'Att_Import_Mode_Settings');
END;
/
Simple SQL Syntax—Attribute Importance
SELECT attribute_name, explanatory_value, rank
FROM BUY_INSURANCE_AI
ORDER BY rank, attribute_name;
ML Model Build (PL/SQL)
Model Results (SQL query)
ATTRIBUTE_NAME RANK ATTRIBUTE_VALUE
BANK_FUNDS 1 0.2161
MONEY_MONTLY_OVERDRAWN 2 0.1489
N_TRANS_ATM 3 0.1463
N_TRANS_TELLER 4 0.1156
T_AMOUNT_AUTOM_PAYMENTS 5 0.1095
A1A2A3A4 A5 A6 A7
© 2019 Oracle

11. OML for R Model Build
> ore.odmAI (BUY_INSURANCE ~ ., CUST_INSUR_LTV)
Call:
ore.odmAI(formula = BUY_INSURANCE ~ ., data = CUST_INSUR_LTV)
Simple R Language Syntax—Attribute Importance
ML Model Build (R)
Model Results (R)
Importance:
importance rank
BANK_FUNDS 0.2161187797 1
MONEY_MONTLY_OVERDRAWN 0.1489347141 2
N_TRANS_ATM 0.1463026512 3
N_TRANS_TELLER 0.1155879786 4
T_AMOUNT_AUTOM_PAYMENTS 0.1095178647 5
A1A2A3A4 A5 A6 A7
© 2019 Oracle

12. OML for Python* Model Build
> ai_mod = ai(**setting) # Create AI model object
> ai_mod = ai_mod.fit(train_x, train_y)
Simple Python Language Syntax—Attribute Importance
ML Model Build (Python)
Model Results (Python)
Importance:
variable importance rank
BANK_FUNDS 0.2161187797 1
MONEY_MONTLY_OVERDRAWN 0.1489347141 2
N_TRANS_ATM 0.1463026512 3
N_TRANS_TELLER 0.1155879786 4
T_AMOUNT_AUTOM_PAYMENTS 0.1095178647 5
A1A2A3A4 A5 A6 A7
* OML4Py – coming soon
© 2019 Oracle

13. Database Developer to Data Scientist Journey
• Data extraction Typically 80% of the work
• Data wrangling
• Deriving new attributes
(“feature engineering”)
…
…
• Import predictions & insights
• Translate and deploy ML models Eliminate or minimize with Oracle
• Automate
You Are Probably Already Doing Most of This Work!
1 https://www.infoworld.com/article/3228245/data-science/the-80-20-data-science-dilemma.html
Most data scientists
spend only 20 percent of
their time on actual data
analysis and 80 percent
of their time finding,
cleaning, and
reorganizing huge
amounts of data, which
is an inefficient data
strategy1
Data Management platform becomes
a combined/hybrid DM + machine learning platform
Where the Machine Learning “Magic” Happens
© 2019 Oracle

14. Additional Data Management Challenges
Maintenance
72% of IT Budget is spent
on Generic Maintenance
Tasks vs Innovation
- ComputerWorld
Cost and Complexity Reliability
91% Experience Unplanned
Data Center Outages
- Healthcare IT News
Database downtime costs
$7,900 / minute
- DB Maestro
¾ Cost of Database
Management
spent on labor
- IDC
91%72% 75%
© 2019 Oracle

15. Journey to Autonomous Database
Focus on innovation, not maintenance
• Automatic Query Rewrite
• Automatic Undo Management
• Autonomous Health Framework
• Automatic Diagnostic
Framework
• Automatic Refresh of Clones
• Automatic SQL Tuning
• Automatic Workload Capture / Replay
• Automatic SQL Plan Management
• Automatic Capture of SQL Monitor
• Automatic Data Optimization
• Automatic Memory Management
• Automatic Segment Space Mgmt
• Automatic Statistics Gathering
• Automatic Storage Management
• Automatic Workload Repository
• Automatic Diagnostic Monitor
• Automatic Columnar Flash
• Automatic IM population
• Automatic Application
Continuity
9i 10g
11g
12c
18c
Examples of automation capabilities
introduced through releases
19c
• Automatic Indexing
© 2019 Oracle

16. Autonomous
Database
Automated
Data Center Operations
and Machine Learning
Complete
Infrastructure
Automation
Complete
Database
Automation
Oracle Cloud
Oracle Autonomous Database | Components
Brings full automation to entire database lifecycle
© 2019 Oracle

17. Using Machine Learning to Drive Autonomous
Automatically Adapts
to Changing Workloads
WORKLOAD OPTIMIZATIONS
“The big ticket item for 2018 & 2019 is the use of ML and AI in the
DBMS allowing the DBMS to maintain itself – the DBMS becomes
Self-Driving. The job of the DBA evolves to use their skills for
tasks with greater business value.”
- Donald Feinberg, Distinguished Analyst, Gartner (Oracle Webcast)
Protects Against
External Malicious Attacks
SECURITY
Detects Anomalies
and Fixes Known Issues
MONITORING & DIAGNOSTICS
© 2019 Oracle

18. •Tasks Specific to the Business
– Architecture, planning, data modeling
– Data security and data lifecycle management
– Application-related tuning
– End-to-End service level management
•Tactical Operations
– Configuration and tuning of systems, network, storage
– Database provisioning, patching
– Database backups, H/A, disaster recovery
– Database optimization
Value Scale
Innovation
Maintenance
© 2019 Oracle
What Autonomous Database Means for DBAs

19. •Tasks Specific to the Business
– Architecture, planning, data modeling
– Data security and data lifecycle management
– Application-related tuning
– End-to-End service level management
•Tactical Operations
– Configuration and tuning of systems, network, storage
– Database provisioning, patching
– Database backups, H/A, disaster recovery
– Database optimization
Value Scale
Innovation
Maintenance
Removes tactical drudgery, more time to innovate
© 2019 Oracle
What Autonomous Database Means for DBAs

20. All Analytic Workloads
Data Warehouse, Data Mart,
Data Lakes
AUTONOMOUS
DATA WAREHOUSE
Online TP & Mixed Workloads
Transactions, Mixed Workloads,
Application Development
AUTONOMOUS
TRANSACTION PROCESSING
ORACLE AUTONOMOUS DATABASE
© 2019 Oracle
Select an Autonomous Database Solution
that Meets Your Workload Needs

21. Autonomous Data Warehouse - Key Use Cases
Query All DataMachine Learning
Data Marts / Warehouses Sandboxes for Data Scientists Data Lakes
Business Analytics
© 2019 Oracle

22. Autonomous Transaction Processing - Key Use Cases
Innovate Faster
Real-Time Analytics and
Machine Learning
Departmental or Mission
Critical Applications1
Mixed
Workloads
Application
Development
Support Business Operations
Analytics
Transactions
1 Coming in Calendar Year 2019
© 2019 Oracle

23. http://oracle.com/cloud/free
© 2019 Oracle

24. Thank you !
Bjoern.Staender@oracle.com
Analytics & Data Innovation
Oracle Germany
© 2019 Oracle