Leveraging your Connectivity, Big Data and Predictive Analytics Infrastructure to Drive Top Line Revenue.

1. © 2014 IBM Corporation
Actionable Insights
Leveraging your Connectivity, Big Data and Predictive
Analytics infrastructure to drive top line revenue
Ben Thompson
Chief Architect IBM Integration Bus
July 2014

2. © 2014 IBM Corporation
Capture what’s
happening in
real-time
Generate model of future
Predict most
likely outcome
Proactively
optimize business
Tap into relevant
data with context
.
.
Unleash real-time data
flowing throughout enterprise
Actionable Insights
What’s the Big Idea?

3. © 2014 IBM Corporation
An Example of Actionable Insight
MQTT DICOM
PACS
Predicting Deciding
Integrating
Imaging
Modality
Patient
Report
Monitoring
Electronic
Medical Record
Alert
Doctor
ODBC
JDBC
SMS

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Industrial Process Control – Machinery Failure
OPC OPC
Predicting Deciding
Integrating
Power
Consumption
Monitoring
Temperature
SCADA
SAP
BAPI
Vibration
RPM
Order
Part

5. © 2014 IBM Corporation
Solutions at Scale
What Big Data means for …
Connected
Appliances
Connected Cars
Smartphones
Internet TVs
Home Hubs
Smart Meters
Home health devices
Connectivity & Integration
The Internet of Things
Analytics

6. © 2014 IBM Corporation

7. © 2014 IBM Corporation
MQTT
A transport for driving the Internet of Things
Lossy or
Constrained
Network
Lossy or
Constrained
Network
Real-World Aware Business Processing
High volumes of data/events
1999 Invented by Dr. Andy Stanford-Clark (IBM),
Arlen Nipper (now Cirrus Link Solutions)
2011 - Eclipse PAHO MQTT
open source project
2004 MQTT.org open community
2013 – MQTT Technical
Committee formed
Cimetrics, Cisco, Eclipse, dc-Square,
Eurotech, IBM, INETCO Landis &
Gyr, LSI, Kaazing, M2Mi, Red Hat,
Solace, Telit Comms, Software AG,
TIBCO, WSO2
• MQTT is a lightweight publish-subscribe protocol
with reliable bi-directional message delivery
• Open Source
• Standards

8. © 2014 IBM Corporation
Passing Data from Things into the Enterprise
The Power of MQTT and IBM MessageSight
• MQTT’s very compact wire format, results in lower
network costs than an HTTP equivalent
• Lightweight footprint – protocol will run on low power
devices
• Clients: C = 80kb; Java = 100kb JavaScript = 80kb
• Recovery, store and forward, and publish/subscribe
are all provided by the MQTT implementations, and
don’t have to be coded into application logic
• Simple set of verbs, easy for developers to learn
• Easy integration with Systems of Record
Lower development costsLower development costs
Lower running costsLower running costs
• Near real-time push of information
• Minimal battery usage
• Store and forward messaging
• Exactly once delivery (where required)
• MQTT’s Event-Driven design point means that a
single server can support a million connected users or
devices
• Publish/Subscribe allows additional functionality to
be added without change to existing application code
More Flexibility and ScaleMore Flexibility and Scale
Improved User ExperienceImproved User Experience

9. © 2014 IBM Corporation
Action HTTP MQTT
Get single piece of data 302 bytes 69 bytes (<4 times)
Send single piece of data 320 bytes 47 bytes (<6 times)
Get 100 pieces of data 12600 bytes 2445 bytes (<5 times)
Send 100 pieces of data 14100 bytes 2126 bytes (<6 times)
Characteristics HTTP MQTT
Style Document-centric, request/response Data-centric, publish/subscribe
Verbs GET/POST/POST/DELETE
complex spec
Pub/Sub/Unsub
simple protocol, easy to learn
Message size Large message, lots of data in headers 2 bytes in minimum header
Quality of Service None, requires custom coding in
application
3 levels:
best-effort, at-least-once, exactly once
Data distribution No distribution mechanism (1-to-1 only) 1-to-none, 1-to-1, 1-to-n
Deliver Relevant Information
Optimizing network with event-driven notification

10. © 2014 IBM Corporation
• Analytics is the discovery and communication of meaningful
patterns in data
• Predictive analytics uses statistical techniques to build a model
that describes key relationships in data
• Predictive models are applied to new observations to estimate
the likelihood or values of unknown (usually future) events
Predictive Analytics
Discovering trends in real-time data in flight
15 Petabytes of big
data generated daily
95%of Mobile traffic
is data by 2015
15bdevices
connected by 2020
420m wearable
health monitors by 2014
Big Data from
Internet of Things

11. © 2014 IBM Corporation
Analysing the Past, Present and Future
Discovering trends in real-time data in flight
PAST
• Applying analytical techniques to past, archived events
• Correlation & Filtering
• Advanced Queries – “where is my transaction?”
• Data Analyser and Observer!
PRESENT
• Analysing current in-flight events
• Correlation, aggregation, metrics
• Calculation of KPIs, real-time dashboard display
• Reporter and Observer!
FUTURE
• Predictive Analytics
• Invoke predictive models, trained on past data
• Trigger actions based on predicted outcomes
• Participant!

12. © 2014 IBM Corporation
Monitoring the Past and Present with IIB
Accounting & Statistics, Monitoring and Record & Replay

13. © 2014 IBM Corporation
Message flow statistics - One record is created for each message flow in a server:
– Message flow, Server (Execution Group) and Node (Broker) name and UUID
– Type of data collected (snapshot or archive)
– Processor and elapsed time spent processing messages
– Processor and elapsed time spent waiting for input
– Number of messages processed
– Minimum, maximum, and average message sizes
– Number of threads available and maximum assigned at any time
– Number of messages committed and backed out
– Accounting origin
Thread statistics - One record is created for each thread assigned to the message flow:
– Thread number (this has no significance and is for identification only)
– Processor and elapsed time spent processing messages
– Processor and elapsed time spent waiting for input
– Number of messages processed
– Minimum, maximum, and average message sizes
Node statistics - One record is created for each node in the message flow:
– Node name and Node type (for example MQInput)
– Processor time spent processing messages
– Elapsed time spent processing messages
– Number of times the node is invoked
– Number of messages processed
– Minimum, maximum, and average message sizes
Terminal statistics - One record is created for each terminal on a node:
– Terminal name and Terminal type (Input or Output)
– Number of times that a message is propagated to the terminal
Accounting & Statistics
Real-time publication of summarized system performance data

14. © 2014 IBM Corporation
Publication +
Subscription
Monitoring your Integrations
Publication of actual payload data for later analysis

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Business Transaction Monitoring versus Business Activity Monitoring
“Archive available for later queries” versus “Real-time view relating to pre-defined KPIs”
Both monitoring capabilities have their pros and cons:
– Real-time view gives you quicker insight but no post-event searching
– Archived view slower to produce insight but complex post-event searching is easy

16. © 2014 IBM Corporation
Data Analysis
Iterative Build-time Analysis of large XML documents
Create a Data Analysis project, select a set of sample XML documents for analysis, and IIB will generate
a Data Analysis Model. Views and filters are provided to navigate through the complex content in a variety
of ways.
Revealed elements whose content relates to a known code set translation (defined in a glossary) are
highlighted. Create Target Model (drag and drop items from the Data Analysis Model to the Target Model).
Make further edits to theTarget Model (either for output messages or output to a database). Generate
graphical maps which will convert input instance XML documents into instance XML documents which
conform to the Target Model. Generate maps for inserts into a database.
Use the generated subflow and associated resources in the normal way within the IIB Toolkit

17. © 2014 IBM Corporation
Right-click menu from a “Focus Element” in
the Data Analysis Model offers highlighting
options.
Choosing Highlight All Coexisting Elements:
The percentage in the square brackets
[nn%] shows the percentage of the instance
documents containing the Focus Element
which also include the element in question.
66.7% of those instance
documents containing
TopLevel_Element2 also
contain TopLevel_Element1
Data Analysis Tools
Highlight co-existing elements

18. © 2014 IBM Corporation
Descendant “0”
(Volume Element itself!)
Descendant “1” Elements
(e.g. Appendix)
Descendant “2” Elements
(e.g. Bibliography)
Descendant “3” Elements
(e.g. Bibliography)
Descendant “4” Elements
(e.g. Name, Author)
Data Analysis Tools
Highlighting the Min and Max Depth of Descendants

19. © 2014 IBM Corporation
Manufacturing Industry Scenarios
Discovering trends in real-time data in flight
Rig
Mine
Factory
ππππr2 h

20. © 2014 IBM Corporation
A Pattern for MessageSight Integration

21. © 2014 IBM Corporation
Provide business insight during integration data flows
– e.g. intelligent decision making; score then action in-flight request based on a business rule
– User creates (e.g.) if-then-else rules
– The bus acts on these rules in flow, e.g. for business level routing
New Decision Service node
– Identifies inputs to business rules from in-flight data
• e.g. the customers order from whole request
• e.g. the item price from key fields…
– Invokes the built-in rule engine
– Captures rules output for downstream processing
Create rules directly inside Integration Bus toolkit
– Significant rules authoring facility built-in
– Automatic package & deploy with integration assets
– Dynamically reconfigure business rule
– Optionally refer to business rules on external ODM decision server
– Exploit separate full ODM Decision Center for BRMS scenarios
Embedded rules engine for high performance
– Rule is executed in the same OS process as integration data flow
– Rule update notification ensures consistent rule execution
– Optional governance of rules through remote ODM Decision Center
Decision Management

22. © 2014 IBM Corporation
Applying Analytics to In-flight Data
Analytics node for model based decision making
– Find & express patterns in data with analytics models
– Analytics equivalent to Business Decision node
• Pluggable engine for e.g. R, SPSS, SAS…
– 2 key scenarios are “model score” and “model trend”
– e.g. %buy additional item, SKU lower than expected
Define the model in tools
– This is a high value skill; understand & express behaviour
– Use historic dataset; this is typically offline scenario
– Both built-in tooling and external model import/reference
Deploy/Change the Model
– Model is encoded into integration flow logic
– Deployed with integration solution
– Analytics policy for dynamic change without redeploy
– Optionally packaged as part of Shared Library Support
Using the model in real time
– Act on these models in integration flow
– Scoring: Synchronous use of model score real-time data
– Observing: Compare models in real-time for divergence
Key, related considerations
– Shared Libraries required with dynamic linkage
• All Applications using library “see” re-deploy

23. © 2014 IBM Corporation
Analytics Node
Demand is growing for analytics to be a real-time activity
As data flows through the enterprise, IIB has visibility to score it
against a predictive model
Data Scientist Role
– Prepares a model based on an analytics engine.
– For example R, SPSS, SAS
Integration Developer Role
– Formats a data stream and applies it to a model
Analytics Node
– R Scalar variable types: double, integer, character (string),
logical (Boolean)
– Data frames can be considered like database tables, consisting
of labelled and typed columns and unlimited rows
Configuration of input and output parameters
– XPath expressions point to locations in the input and output
trees
– Direction of Parameter allows a single properties table to control
tree copying and return results from the scoring process
Score

24. © 2014 IBM Corporation
Healthcare Industry Scenarios
Discovering trends in real-time data in flight
Operational
– KPI’s
– Retrospective view of performance
Clinical insights
– Real Time Analytic Processing
– Interventive care from insight into longitudinal care records
Cognitive Analytics
– Assisted treatment/diagnosis
Data Baby!

25. © 2014 IBM Corporation
Almost 25% of the population is over 65, and that number is growing
Medical advances mean people are living longer
Services for the elderly account for almost 50% of the social services budget
Many more elderly people are choosing to remain at home, even when
they are alone
Ensure their safety and provide needed services but the city had to find a
cost effective way to know when its people needed help
A mesh-network of sensors that monitor the home environment—
temperature, CO2, water leaks, etc.—of elderly citizens living alone
Additional home remote medical interaction with medical professionals,
saving trips to the doctor
It all works with a little help from “angels” (relatives or friends of the user)
who are alerted if there is a problem
A new model of social and health service that operates on existing budgets
and resources, even as the elderly population increases
Provides a technological, but still human, system of care via the remote
“angels”—the user can be independent, but not feel alone
Social service and health staff can concentrate on people who really need a
physical presence with them, while those in the monitoring program
maintain an excellent quality of life
http://www.youtube.com/watch?v=kDvW8R4BL0I

26. © 2014 IBM Corporation
Waste Management
Combining the Internet of Things, Big Data, Analytics and Mobile!
• Weight and type of waste
• Excess of waste
• Optimization of the collection path
• Exception management (bins in
wrong places, need of additional
bins, replacement of bins etc.)
• Send/receive working orders
to/from SAP
Central Acquisition System
Field Management
System
SAP
DB2
IIB
WAS (J2EE app.)
MQTT client
Worklight
Application
GPS
BPM ODM
MessageSight
HTTP(s)
MQTT
GPRS/3G
RFID
reader

27. © 2014 IBM Corporation
Slope aware power train
optimization
Flooding/Slippery risk aware
Driving alert
100
Dynamic/Variable Speed Limit
alert & speed control
Bus
Signal status aware speed
control going thru crossing
Height/load limit aware fleet
driving alert & detouring
Accident/congestion aware
detouring & navigation
Dynamic parking space
availability navigation
Passenger crowd aware bus
dynamic speed management
Environment pollution surveillance
traffic fencing control & fleet alert
!
!
Co2
!
:-)
!!
Low Bridge
The Connected Car
Location Awareness: tracking where things are and how things move!

28. © 2014 IBM Corporation