Wednesday, 19 March 2014

Stream Computing for Real-time Analytics: Overview, Integration and Use Cases

Stream computing delivers real-time analytic processing on constantly changing data in motion. It enables descriptive and predictive analytics to support real time decisions. Stream computing allows you to capture and analyze all data - all the time, just in time. Relational databases and warehouses find information stored on disk. Streams analyses data before you store it. Key points here are -


1) Stream is the right capability when the primary big data challenge is analyze data that is in motion (Velocity) – because the business imperative requires a real-time response/action based on analyzing the data or the data is very large and want to more cost-effectively filter and remove data before moving into your data warehouse or Hadoop system.  It can handle continuous or bursty streams of data – millions of events per second with microsecond latency.
2) Streams can process any type of data (Variety) – audio, video, network logs, sensors, social media such as Twitter, in addition to structured data.
3) And, Streams is designed to scale to process any size of data from Terabytes to Zetabytes per day

Stream computing changes where, when and how much data you can analyze. Store less, analyze more, and make better decisions, faster with stream computing. The benefits of streaming analytics are immediately obvious.  Dramatic cost savings by analyzing data and only storing what is necessary.  The ability to detect and make real-time decisions, results in customer retention to detect fraud to cross-selling a product. 

IBM InfoSphere Streams for Stream Computing

IBM InfoSphere Streams is an advanced analytic platform that allows user-developed applications to quickly ingest, analyze and correlate information as it arrives from real-time sources. InfoSphere Streams is designed to handle very high data throughput rates, up to millions of events per second. A market leader in providing sophisticated analytics for IoT, IBM received the 2013 Ventana Research award for Operational Intelligence in the IT Innovation category for InfoSphere Streams.

Core highlights are -

  • Perform advanced real-time analytics on data in motion
  • Rapidly ingest, correlate and continuously analyze a massive volume and variety of structured and unstructured streaming data as it arrives from thousands of sources
  • Make real-time predictions and discoveries as data arrives
  • Visualize data easily with drag-and-drop development tools
  • Detect and respond to critical events immediately
  • Learn and update models for future analysis and trend prediction with cognitive computing

InfoSphere Streams helps you:
  • Analyze data in motion—provides sub-millisecond response times, allowing you to view information and events as they unfold. Tools facilitate sophisticated analytics, such as geospatial, voice, image and text, and also update models on the fly.
  • Simplify development of streaming applications—uses an Eclipse-based integrated development environment (IDE). Developers are able to easily and rapidly build applications and connect to new data sources. Drag-and-drop editors, wizards, visualization tools, and runtime monitoring and debuggers are available.
  • Extend the value of existing systems—integrates with your applications, and supports both structured and unstructured data sources. The supporting infrastructure adapt to rapidly changing data formats, types and messaging protocols. It also read from and writes to a vast number of data sources. A massively parallel architecture is designed to deliver unlimited compute potential.
IBM Infosphere Streams capabilities are designed to work together and with existing bigdata & analytics applications such as BI and predictive analytics.  Here’s an example scenario:

1)     Historic data is stored in the DB/warehouse (DB2, Infosphere Warehouse, Informix, Oracle, solidDb, MySQL, SQLServer, Netezza etc.) where interesting patterns are detected using database toolkit operators, such as the pattern of credit card transactions that would indicate possible fraud. Support for XML allows developers to fuse a broader range of traditional and untraditional data.

2)     IBM SPSS leverages IBM SPSS Modeler to develop and build predictive models, and then deploy them using the SPSS Scoring Operator. The PMML models are then imported into InfoSphere Streams Studio to generate Streams programs that are executed to score the incoming records in real time without suspending InfoSphere Streams applications.

3)     Additional data sources such as RFID tags, blogs, or other information might be used to improve the confidence levels of the scoring algorithms.

4)     These measures can be sent to Dashboards like IBM Cognos Real Time Monitoring or business process management (BPM) systems to trigger business processes to take immediate action as required.

5)     IBM InfoSphere BigInsights lets you store streaming data in an enterprise-class Hadoop environment for additional analysis or historic retention. InfoSphere Streams and InfoSphere BigInsights use the same advanced text analytics capabilities to simplify natural language processing applications for both data in motion and data at rest. In addition, InfoSphere BigInsights can be used to augment streaming sources with contextual information, and users can visualize InfoSphere Streams data in the InfoSphere BigInsights console.

6)     Streams real-time analytics can be integrated with ETL solutions like IBM DataStage helps get more timely results and offload some analytics load from the warehouse. IBM InfoSphere DataStage helps users perform deep analysis and gain additional insight using contextual and source data from other parts of the infrastructure.

7)     Messaging queues allow InfoSphere Streams to receive data from or send data to IBM WebSphere MQ, IBM MessageSight and Java Messaging System (JMS) offerings.

8)  IBM InfoSphere Data Explorer enables users to visualize InfoSphere Streams data in the InfoSphere Data Explorer CXO dashboard and add streaming data to the InfoSphere Data Explorer index. 
Stream computing use cases
When companies can analyze ALL of their available data, rather than a subset, they gain a powerful advantage over their competition. Many customers are seeing tangible ROI using IBM Stream solutions to address their big data challenges:
  • Healthcare: 20% decrease in patient mortality by analyzing streaming patient data
  • Telco: 92% decrease in processing time by analyzing networking and call data
  • Utilities: 99% improved accuracy in placing power generation resources by analyzing 2.8 petabytes of untapped data
Below are few cross-industry scenarios best suitable for stream computing –

1)     Know Everything about your Customers
·         Social media customer sentiment analysis
·         Promotion optimization
·         Segmentation
·         Customer profitability
·         Click-stream analysis
·         CDR processing
·         Multi-channel interaction analysis
·         Loyalty program analytics
·         Churn prediction

2)     Innovate New Products at Speed and Scale
·         Social Media - Product/brand Sentiment analysis
·         Brand strategy
·         Market analysis
·         RFID tracking & analysis
·         Transaction analysis to create insight-based product/service offerings

3)     Instant Awareness of Risk and Fraud - Lower risk, detect fraud and monitor cyber security in real time. Augment and enhance cyber security and intelligence analysis platforms with big data technologies to process and analyze new types (e.g. social media, emails, sensors) and sources of under-leveraged data to significantly improve intelligence, security and law enforcement insight.
·         Multimodal surveillance
·         Cyber security
·         Fraud modeling & detection
·         Risk modeling & management
·         Regulatory reporting

4)     Exploit Instrumented Assets
·         Network analytics
·         Asset management and predictive issue resolution
·         Website analytics
·         IT log analysis

5)     Run Zero Latency Operations
·         Smart Grid/meter management
·         Distribution load forecasting
·         Sales reporting
·         Inventory & merchandising optimization
·         Options trading
·         ICU patient monitoring
·         Disease surveillance
·         Transportation network optimization
·         Store performance
·         Environmental analysis
·         Experimental research

Here’s few usecases in industries to get an idea about the breadth of possibilities that stream technology along with other bigdata products can offer. To explore more details, click on the industry title below.


  • Data warehouse optimization
  • Predictive asset optimization
  • Connected vehicle
  • Actionable customer insight


  • Optimize offers and cross sell
  • Contact center efficiency and problem resolution
  • Payment fraud detection and investigation
  • Counterparty credit risk management

Consumer Products

  • Optimized promotions effectiveness
  • Micro-market campaign management
  • Real-time demand forecast

Energy and Utilities

  • Distribution load forecasting and scheduling
  • Create targeted customer offerings
  • Condition-based maintenance
  • Enable customer energy management
  • Smart meter analytics


Geospatial analysis requires complex mathematics such as set theory and geospatial geometry. It is used for location intelligence and location-based services for security and surveillance, geographic information systems, traffic patterns and more. The city of Dublin, Ireland, uses InfoSphere Streams to analyze 50 bus locations per second for its fleet of roughly 1,000 buses. Check it out -
  • Threat prediction and prevention
  • Social program fraud, waste and errors
  • Tax compliance - fraud and abuse
  • Crime prediction and prevention


  • Measure and act on population health
  • Engage consumers in their healthcare
  • Health monitoring and intervention


Knowing the order of events can have profound impacts, for example in predicting the path of a natural disaster or picking the next best stock trade. InfoSphere Streams helps insurance companies plan for natural disasters and enables real-time public alerts. It also performs real-time analysis of sensor data collected from the Hudson River, one of the most instrumented bodies of water in the world. Check this out -
  • Claims fraud detection
  • Next best action and customer retention
  • Catastrophe risk modeling
  • Usage-based insurance
  • Portfolio management
  • Producer optimization

Oil & Gas

  • Advanced condition monitoring
  • Drilling surveillance & optimization
  • Production surveillance & optimization


  • Merchandise optimization
  • Actionable customer insight


Telecommunications service providers continue to experience a huge growth in smartphone and mobile device use. Growing text and data usage creates a deluge of context- and time-sensitive data. InfoSphere Streams enables telecommunications providers to analyze billions of call data records per day to detect fraud, ensure high asset utilization and create accurate customer profiles for heightened customer service and retention. Using InfoSphere Streams, Sprint reduced storage costs by 90 percent. Check this out -
  • Pro-active call center
  • Smarter campaigns
  • Network analytics
  • Location-based services

Travel & Transportation

  • Customer analytics and loyalty marketing
  • Capacity & pricing optimization
  • Predictive maintenance optimization


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