Oracle's BigData solutions consist of a number of new products and solutions to support customers looking to gain maximum business value from data sets such as weblogs, social media feeds, smart meters, sensors and other devices that generate massive volumes of data (commonly defined as ‘Big Data’) that isn’t readily accessible in enterprise data warehouses and business intelligence applications today.
15. What is ?
• Brings R’s statistical functionality to the Oracle Database
• Eliminates R’s memory constraints
• Allows R to run on very large data sets
• Oracle R is architected for enterprise production infrastructure
• Automatically exploits database parallelism without requiring
parallel R programming
• Oracle R leverages the latest R algorithms and packages
• R is an embedded component of the DBMS server
• Part of Oracle Advanced Analytics (+ODM)
16. Oracle R Architecture
R workspace console
Function push-down Oracle statistics engine
OBIEE, Web
– data transformation & Services
statistics
Development Production Consumption
• Leverages SQL for data prep, analysis and enhanced statistics engine
• R engine runs on database nodes for production enablement of R models
• Leverages Exadata—Oracle R workloads run in-database and can be bound to
database nodes for workload isolation
• Enriches OBIEE dashboards with Oracle R statistics and analytics
17. Oracle Data Mining (ODM) Data mining can answer questions
that cannot be addressed through
simple query and reporting techniques.
• Data Mining: Insight from discovering relationships
• Knowledge about what happened in the past
• Characterization, segmentation, comparisons, discrimination
• Descriptive models of patterns
• Predictive Analytics: Making better decisions and
forecasts
• Knowledge about what is happening right now and in the future
• Classification and prediction of patterns
• Rule-and-model driven
18. Data Mining – Some Definitions
Supervised Learning
Problem Classification Sample Problem
Classification Predict customer response to an affinity
card program
Regression Predict customer’s age
Attribute Importance Find the most significant predictors, data
preparation
A1 A2 A3 A4 A5 A6 A7
19. Data Mining – Some Definitions
Unsupervised Learning
Problem Classification Sample Problem
Anomaly Identify customer purchasing behavior that is
Detection significantly different from the norm
Association Find the items that tend to be purchased
Rules together and specify their relationship –
market basket analysis
Clustering Segment demographic data into clusters and
rank the probability that an individual will
belong to a given cluster
Feature Group the attributes into general
Extraction characteristics of the customers
F1 F2 F3 F4
While it could never be described as a sleepy business since there have been several profound changes in the course of its evolution, it doesn’t really take an industry pundit to observe that the current Analytics market is marked by an accelerating pace of change. Comparable changes taking place now in a matter of a few years took decades to play out in the early days of BI and EPM. So, in the 80s we saw database reporting tools rule the roost. And most applications shipped with some sort of hardwired reporting capabilities built in, providing visibility but no subsequent interactivity. You could get your first question answered really well. But if you had a follow-on question, you were out of luckCome the 90s and most BI platforms evolved to 3 tier architectures, supporting more users and subject area specific data marts and BI environments for functional areas such as marketing, sales and supply chain.The broad-based adoption of the internet saw BI tools in the 2000s increase their footprint to be true analytical platforms deployed on enterprise data warehouses. These data warehouses supported the decision support needs of all users of an extended enterprise with capabilities that spanned production reporting to highly interactive ad hoc analysisBig changes, no doubt, but played out over a 20+ year time horizon. In the last 2-3 years, though, we are seeing technology disruptions opening up new possibilities in Analytics at a pace that is nothing short of breathtaking:-There is an explosion of business relevant data now on the internet. It is incredibly varied, generated at great velocity and already enormous in volume. How will it be analyzed?-Apple and others have revolutionized the tablet as an internet and general content consumption device that is now well ensconced with corporations, certainly at the highest echelons. What will analytics on these smaller and intensely personal devices come to mean?-The real cost of in-memory technology has declined dramatically. What transformative power could this hold for companies looking to live – and win – “in the moment”?-The maturity and consequent acceptance of the cloud has introduced a low friction delivery model for software delivered as a service to enterprises. How will Analytics be transformed, or how might it transform, the Cloud?These dramatic changes are sweeping through the enterprise computing landscape now. They each come with their own set of challenges but for those who view them, instead , as opportunities, we believe that tremendous competitive advantage can be unlocked. And we believe that Oracle Business Analytics provides you with the tools to do just that.
While it could never be described as a sleepy business since there have been several profound changes in the course of its evolution, it doesn’t really take an industry pundit to observe that the current Analytics market is marked by an accelerating pace of change. Comparable changes taking place now in a matter of a few years took decades to play out in the early days of BI and EPM. So, in the 80s we saw database reporting tools rule the roost. And most applications shipped with some sort of hardwired reporting capabilities built in, providing visibility but no subsequent interactivity. You could get your first question answered really well. But if you had a follow-on question, you were out of luckCome the 90s and most BI platforms evolved to 3 tier architectures, supporting more users and subject area specific data marts and BI environments for functional areas such as marketing, sales and supply chain.The broad-based adoption of the internet saw BI tools in the 2000s increase their footprint to be true analytical platforms deployed on enterprise data warehouses. These data warehouses supported the decision support needs of all users of an extended enterprise with capabilities that spanned production reporting to highly interactive ad hoc analysisBig changes, no doubt, but played out over a 20+ year time horizon. In the last 2-3 years, though, we are seeing technology disruptions opening up new possibilities in Analytics at a pace that is nothing short of breathtaking:-There is an explosion of business relevant data now on the internet. It is incredibly varied, generated at great velocity and already enormous in volume. How will it be analyzed?-Apple and others have revolutionized the tablet as an internet and general content consumption device that is now well ensconced with corporations, certainly at the highest echelons. What will analytics on these smaller and intensely personal devices come to mean?-The real cost of in-memory technology has declined dramatically. What transformative power could this hold for companies looking to live – and win – “in the moment”?-The maturity and consequent acceptance of the cloud has introduced a low friction delivery model for software delivered as a service to enterprises. How will Analytics be transformed, or how might it transform, the Cloud?These dramatic changes are sweeping through the enterprise computing landscape now. They each come with their own set of challenges but for those who view them, instead , as opportunities, we believe that tremendous competitive advantage can be unlocked. And we believe that Oracle Business Analytics provides you with the tools to do just that.
Enables Map-Reduce style R calculations with the Big Data Appliance and HDFSSupports compute-intensive parallelism for simulationsORCH provides optimized R algorithms that are robust, numerically accurate and linearly scalable on Hadoop and the Big Data Appliance. More cores achieve a proportional decrease in run times and matches R user experience.Linear Models and Logistic ModelsGeneral feed-forward Neural Networks Regression ModelsMatrix Factorization (algorithms for large-scale Matrix problems)K-Means ClusteringPCA (Principal Component Analysis)Correlations