This document discusses the challenges of applying R in streaming and business intelligence applications and introduces TIBCO's Enterprise Runtime for R (TERR) as a solution. TERR is an enterprise-grade R engine that allows users to develop code in open source R and deploy it on a commercially supported platform without rewriting code. This enables easy integration of R into applications for real-time analytics on streaming data and embedding R functionality in tools like Spotfire for business intelligence. Examples are provided of using TERR for predictive maintenance of oil and gas equipment and transportation logistics optimization.
2. Analytic Challenges for Enterprises
• Big Data
– More and more data, and the expectation to
do something with it
• Competitive Pressures
– Deeper insights into data--Apply Advanced
Analytics
– Smarter Decisions--Broaden analytic usage
to wider community beyond Data Scientists
– Faster Decisions—both human and
automated
• Agile response to evolving opportunities
and threats
– Answers (and the questions to ask) change
rapidly
3. • Agile
– Easy prototyping of new models
and analysis
• Deeper insights
– Huge array of analytic methods
available
– The “best” method to solve a
given problem is likely available
• Performance
• Not designed for real time or Big Data
applications
• Broader usage
• Hard for non-Data Scientist to use
directly
• Challenging to integrate into enterprise
applications
• Performance, commercial support and
Intellectual Property concerns
• Compromises which impact Agility
• Recode in a new, less agile environment
• Rewrite, use specialized R packages to
solve one problem better
R can help… …but has it’s own challenges
4. What would the ideal solution look like?
• A single environment that would allow you to prototype in R, and deploy to
production in R
– Without recoding, without delay, without compromises
– Enable agile response to changing opportunities and threats
Requires
• Analytic flexibility, power and breadth of R
• High performance, scalable, robust platform
• Easy to embed in Business Intelligence, Real time and custom applications
• Fully supported for mission critical applications
• Allows R users to continue to work in their preferred development environments (e.g., RStudio)
5. TIBCO Enterprise Runtime for R (TERR)
• Unique, enterprise-grade engine for the R
language, built from the ground up by TIBCO
– Based on TIBCO’s long history and expertise with S+
– Better performance and memory management than open
source R
• Designed for R language compatibility
– Wide range of built-in analytic methods
– Compatible with thousands of CRAN packages (dplyr,
data.table, etc.)
• Designed for commercial embeddability
– TIBCO licensed & supported product
– Not GPL, not a repackaging of the Open source R engine
• TERR extends the reach of R in the enterprise
– Develop code in open source R
– Deploy on a commercially-supported and robust platform
– Without the delay and cost of rewriting your code
– Embed in Data Discovery, BI and real time applications
6. Example 1: Embedded TERR in Spotfire
• Spotfire: Data Discovery and Visualization platform for Business Users and Analysts
– Separate analytics platform, independent of TERR/R
• Easily enhance Spotfire analyses and applications with R language scripts
– Extend the impact of the Data Scientist/R by making their analytic insights available to a wider audience
Write R code directly in Spotfire;
TERR executes locally or on server
Manage TERR analytics locally or
in Server to reuse across
community
Deploy TERR-powered
applications to the web
8. Advanced Analytic Applications in Spotfire
Customer Churn:
• Retain your most profitable customers
• Increase upsell, decrease churn
Fraud Detection:
• Reduce losses due to fraudulent
transactions
Supply Chain Optimization:
• Anticipate peaks and lulls
• Optimize distribution centers
HR Planning:
• Predict employee attrition and optimize
retention
9. • Real-time advanced analytics
– Apply predictive model in response to some triggering event
Sensors on industrial equipment trending negative; customer walks into your store or purchases online,
etc.
– Trigger the right decision in response
Extend a mobile offer to a customer; stop a fraudulent transaction in process; alert the equipment
operator or shut down the equipment
Example 2: TERR and Streaming Data
Model
Develop model
Deploy via TERR in
TIBCO Streambase
Act
Automatically monitor
real-time transactions
Automatically trigger
action
Analyze
Analyze data in Spotfire
Uncover patterns,
trends & correlations
11. • Port Congestion Detection
– Real time system triggers TERR
– Analyzes port congestion
– Recommends reduction of speed if
no berths available
• Maritime Abnormality Detection
– Based on Automatic Identification
System info, TERR calculates
likelihood of deviation from normal
sailing routes
– Alerts carrier & operator
Transportation and Logistics Optimization
12. Use TERR in your familiar tools
RStudio IDE
– Free, open source IDE widely used by the
R Community
– Fully compatible with TERR Developer
Edition
KNIME
– Free, open source workflow tool for data
management and analysis
– TERR fully compatible with KNIME
Interactive R Statistics Integration nodes
13. TERR is R for the Enterprise
• Develop code in open source R, deploy on commercially-supported, and
robust platforms
– Without recoding, without compromises
– Save time & money, quickly respond to new threats and opportunities
• Tightly & efficiently embed R language functionality
• Extend the power of R to a wider audience, more applications
14. • TERR Community at community.tibco.com
– Resources, Documentation, R compatibility, FAQs, Forums
– Predictive Analytics overview and resources
• Free TERR Developer Edition
– Full version of TERR engine for testing code prior to deployment
– Supported through TIBCO Community, download via tap.tibco.com
• Spotfire Free Trial: http://spotfire.tibco.com/trial
• R Consortium Founding Member www.r-consortium.org
Learn more and Try it yourself
Editor's Notes
… What is TERR?
Since the demo wraps up with the idea of deploying the model to real time systems, it is a good segue
Supply Chain Optimization: simulate production and shipping scenarios to anticipate peaks and lulls
HR Retention: Predict employee attrition and optimize retention
Example use case: real-time correlations for action
Automated manufacturing yield analysis.
Analyze manufacturing data in Spotfire
Deploy model
Compare live data to models of good behavior
When actual manufacturing usage breaks the model, Spotfire used to understand why