Machine and sensor data generated by the Internet of Things, sensors and computer processes provide a fantastic source for a completely new generation of Big Data applications. Imagine the possibilities if you apply machine learning and predictive analytics in real time to continuously optimize a large number of connected devices and the application to current challenges such as traffic, health or ecology. Starting with a short overview about the use cases and the important general concepts for real time streaming analytics, the following live demo will give a practical insight into its realization. The live demo will demonstrate how R models are applied to real time data feeds using InfoSphere Streams, and will also cover advanced concepts such as dynamic model update. (The live demo is not included in the slides, so no screenshots ...)

1. © 2013 IBM Corporation
Take action on sensor data in
real-time based on analytics in R
Easy streaming analytics with InfoSphere Streams
Stephan Reimann – IT Specialist Big Data – stephan.reimann@de.ibm.com @stereimann
Wilfried Hoge – IT Architect Big Data – hoge@de.ibm.com @wilfriedhoge

2. © 2014 IBM Corporation
Traditional approach
– Historical fact finding
– Analyze persisted data
– (Micro-) Batch philosophy
– PULL approach
Streaming analytics
– Analyze the current moment / the now
– Analyze data directly “in Motion” – without
storing it
– Analyze data at the speed it is created
– PUSH approach
Streaming analytics is a paradigm shift from pull to push analytics in
real time, directly „on the wire“, data does not need to be persisted
Repository InsightAnalysisData InsightAnalysisData

3. © 2014 IBM Corporation
InfoSphere Streams is the result of an IBM research project, designed
for high-throughput, low latency and to make streaming analytics easy
Scale out
Millions of Events per Second
Complex Data & Analytics
All kinds of data
Complex analytics: Everything you
can express via an algorithm
Low Latency
Analyzes data at the speed it is
created
Latencies down to µs
Immediate action in real time
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InfoSphereStreams
Capabilities
Howitworks
– Define apps as flow graphs consisting of
sources (inputs), operators & sinks (outputs)
– Extend the functionality with your code if
required for full flexibility
– The clustered, distributed runtime on
commodity HW scales nearly limitless
– GUIs for rapid development and
operations make streaming analytics easy

4. © 2014 IBM Corporation
LIVE - DEMO

5. © 2014 IBM Corporation
Free Quickstart Edition Developer Community
Streaming analytics is about analyzing all the data, continously, just
in time, it enables a completely new generation of big data apps
www.ibmdw.net/streamsdev/ibm.co/streamsqs
Stop just dreaming of real time big data
Start with streaming analytics!!!
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Radio astronomyHealthcareTelCoTransport Smart GridIoT
Streaming Analytics is already reality
... and is a key component of many
innovations
...
Tutorials,
Labs,
Forum, ...
Connected Car
GitHub Community
github.com/IBMStreams
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Toolkits,
Toolkits,
Toolkits