1. Big Data Analytics Classical BI (DW and reporting) Visualization
Big Data Discovery Data Science (incl. AI, Datamining + Machine Learning)
Big Data needs to be processed.
Big Data almost always goes hand-in-hand with Big Data Analytics.

2. Big Data Analytics and Data Science
How does all this relate to classical BI?
OAA
“Big Data”
(all DB technologies)
BDD
“R”
(only statistics)
Visual Analyzer
(partially)
NEW!!!
Fuzzy Set Theory
Fuzzy Logic
Neural-
Computing (BIC)
Visualization AI
Pattern
Recognition
Machine
Learning
In its simplest form
Data Science
Statistics
(Probability & Possibility)
(Set and Fuzzy Set Theories)
Applications embedding “intelligence:
Customer eXperience, CRM,
Sales Performance,
Marketing (BlueKai),
Talent Management,
Financial Management,
BID management …
KDD
(Discovery)
Data
Mining
Databases
and
Data Processing

3. Data Mining vs. Machine Learning
CODE-centric
DATA-centric ✔ ✘
Machine Learning
(not from Wikipedia )
(Wikipedia definition) Machine learning is a subfield of computer science[1] that evolved from the study of pattern recognition and computational learning theory in artificial intelligence.[1] In 1959, Arthur Samuel defined machine learning as a "Field of study that gives computers the ability to learn without being explicitly programmed".[2] Machine learning explores the study and construction of algorithms that can learn from and make predictions on data.[3] Such algorithms operate by building a model from example inputs in order to make data-driven predictions or decisions,[4]:2 rather than following strictly static program instructions.
Data Mining + =
Supervised
Unsupervised 3

4. Use Cases and Application Areas
for the sexiest technology of the 21st century…
Anomaly detection, preventive/predictive maintenance etc.
Fraud detection cases in telco, tax, finance , security, …
Any “smart” operations, marketing and CX customization or extensions, retention/membership/loyalty, …
IOT/ITS related use cases, smart “intelligent” homes, smart planning, DW/reporting/BI extensions, …
Solutions requiring “reasoning” and automation, … like ROBOTICS
A good deal of the Big Data cases!
Don´t forget GRAPH and that it offers REASONING!
What about automated integrations?

5. from https://www.kaggle.com/
Fun & More
See also:
Communications of the ACM, Evolutionary Robotics:
Lecture notes UiO/IfI, Evolutionary Robotics:
Lecture notes UiO/IfI, Biologically Inspired Computing:
All slides:
Machine Learning (Safari Books):
Future Perspectives on AI:
READ
Brendan Tiernay (Racle Ace Director, OAA), “Predictive Analytics Using Oracle Data Miner”
MUST WATCH!
CNBC article and interview with a robot: Would you fall in love with this robot?
Ex Machina trailer: The ultimate Turing test!

6. Nano-robots in medicine
Transforming life from the inside
March 2016, Dr. M. Naci Akkøk
But nano-robots (pico/micro versions) are already in production, and not only in medicine… Relevance? An interesting one: Think that each has a role, nano-programmed, and co-operate like synapses… Generating (Big) Data that needs to be “actionable”
Machine intelligence, robotics, ...? Positronic brain? RIP, Feyman and Asimov!
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