7 Predictive Analytics, Spark , Streaming use cases
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7 Predictive Analytics, Spark , Streaming use cases
- 1. Mark Palmer, SVP of Analytics, TIBCO https://about.me/mark.palmer 7 PREDICTIVE ANALYTICS, SPARK, STREAMING USE CASES
- 2. 7 Predictive Analytics, Spark, Streaming Use Cases 1. Live Train Time Tables: 40% Reduction in Spread (Dutch Railways) 2. Intelligent Equipment: Saving $40M / year (Oil & Gas – Many) 3. Algorithmic Loyalty: Finding the Jacket You Didn’t Know You Needed (North Face) 4. Predictive Risk & Compliance: Avoiding $440M in 40 Minutes of Loss (ConvergEx) 5. Live Flight Optimization: Get You Home on Time (United Airlines) 6. Continuous Transaction Optimization: Watch 20,000 Systems at Once (Morgan Stanley) 7. IoT Parcel Tracking: From 20% to 100% Real-Time (Royal Mail, UK)
- 3. Hadoop + Analytics: Discover, Automate, Act AUTOMATE: Inject predictive model into stream DISCOVER: Data scientists use interactive analytics to discover, score and model based on Hadoop / Spark data lakes Automation Case ManagementOperational Intelligence ACT: Automation, alerting and refinement, BPM
- 4. SPARK 1: CAPTURE STREAMS, NORMALIZE, PERSIST IN SPARK Kafka JMS - HDFS - Parquet - HBase A A A Cleanse Normalize Bin STREAMING DATA PREP StreamBase 2: DISCOVER MODEL Data scientists ANALYTICS REAL-TIME SPARK ACCELERATOR PATTERN POS Mobile Web Operations LIVE MONITORING & ANALYTICS Live Datamart, LiveView 6: LIVEVIEW 3: LOAD PREDICTIVE MODEL Model Stream Scoring STREAMING ANALYTICS StreamBase Real-Time Action 4: CONTINUOUS ALGORITHMIC ACTION Upsell Recommendation History 5: AUTO-RETRAINING MODEL TRACKING Real-time training StreamBase IMPALA
- 5. STREAMING DATA PREP Cleanse Normalize Bin BIG DATA STREAMING ANALYTICS Model execution Stream processing ANALYTICS Data discovery Model discovery Load model Messaging layer : - Kafka - HiveMQ - JMS - ActiveMQ - RabbitMQ - FTL - … Direct access : - Websocket - TCP/UDP - MQTT - HTTP - ... Public/private APIs : - Twitter, Faceboook,... - Google finance - ... HDFS, Hbase, Parquet, Avro SQL Data scientistsOperations The Spark Accelerator Pattern MODEL TRACKING Real-time model training Live monitoring CONNECTIVITY
- 6. Prebuilt building blocks to speed up Spark implementations Data capture Data analysis Model scoring Model training TIBCO Big Data Accelerator SIMPLIFYING SPARK
- 7. IBM HP MSFT Statistical Correlation “Buy HP @$92.97 and sell IBM @93.02 now?” The First Step Forward Toward an Algorithmic Computing
- 8. IBM Continuous, Not Real-Time HP IBM/ HP Spread “Buy HP @$92.97 and sell IBM @93.02 now?” Streaming Analytics
- 9. © Copyright 2000-2013 TIBCO Software Inc. StreamBase: Act on What’s Happening NOW AUTOMATION IS EVIL? IT DEPENDS ON YOUR PERSPECTIVE
- 10. 1. Live Train Time Tables: 40% Reduction in Spread (Dutch Railways) 2. Intelligent Equipment: Saving $40M / year (Oil & Gas – Many) 3. Algorithmic Loyalty: Finding the Jacket You Didn’t Know You Needed (North Face) 4. Predictive Risk & Compliance: Avoiding $440M in 40 Minutes of Loss (ConvergEx) 5. Live Flight Optimization: Get You Home on Time (United Airlines) 6. Continuous Transaction Optimization: Watch 20,000 Systems at Once (Morgan Stanley) 7. IoT Parcel Tracking: From 20% to 100% Real-Time (Royal Mail, UK 7 Predictive Analytics, Spark, Streaming Use Cases
- 11. © Copyright 2000-2016 TIBCO Software Inc. #1 Modern vehicles are mobile devices #2 Existing transportation systems rely on routes, schedules, work assignments: a “rear-view mirror” view approach #3 Millennials demand real-time insight into everything The Connected Vehicle Business Challenge
- 12. © Copyright 2000-2016 TIBCO Software Inc.
- 13. © Copyright 2000-2016 TIBCO Software Inc.
- 14. © Copyright 2000-2016 TIBCO Software Inc.
- 15. External Data Event-Driven Rules & Predictive Analytics Trip Optimization Rules Predictive Maintenance Rules Alerts Vehicle Clustering Rules Location Stream TIBCO Live Datamart AMX BPM Billions of events Traffic, Twitter, Weather Connected Vehicle Data Weather Case ManagementEnterprise Data BusinessWorks Real-Time Geo Fencing Rules Predictive Route Optimization Journey Disruption Rules Business Events, TERR, StreamBase Live Datamart Operational Command & Control App LiveView Analytics Hadoop / Spark Spotfire Scheduling, Maintenance, MDM, CRM ALERTS IoT Connected Vehicle Architecture
- 16. 1,000 trains simultaneously transmit location, capacity, “blocking.” Alerts on status are sent to customers. Bad weather delays a trip; streaming analytics continuously re-calculates the impact based on state in real-time Operators analyze the impact in real-time, re- calculates train “blocking,” and take action Operations returns to normal, customers alerted A Moment in the Life of a Connected Vehicle
- 18. All systems go: timing estimates calculated in real-time
- 19. Visualize BE rule results in LiveView Visualizing Events
- 20. CVA simulator creates a delay (e.g., weather, equipment problem)
- 21. CVA simulator creates a delay (e.g., weather, equipment problem) Alerts appear in UI
- 22. Critical Business Moment Trip 2202 will be 8 minutes late, so trip 2211 is now delayed
- 23. Alert can be sent via Kafka, BPM, signage update tweet to the public…
- 24. Train now resumes normal speed - although still delayed - trip 2202 no longer impacts the next trip
- 25. 1. Train Time Table Deviation: 40% Reduction in Spread (Dutch Railways) 2. Intelligent Equipment: Saving $40M / year (Oil & Gas – Many) 3. Algorithmic Loyalty: Finding the Jacket You Didn’t Know You Needed (North Face) 4. Predictive Risk & Compliance: Avoiding $440M in 40 Minutes of Loss (ConvergEx) 5. Live Flight Optimization: Get You Home on Time (United Airlines) 6. IT and Transaction Optimization: Watch 20,000 Systems at Once (Morgan Stanley) 7. Parcel Tracking: From 20% to 100% Real-Time (Royal Mail, UK) 7 Predictive Analytics, Spark, Streaming Use Cases
- 26. © Copyright 2000-2016 TIBCO Software Inc.
- 27. © Copyright 2000-2016 TIBCO Software Inc.
- 28. © Copyright 2000-2016 TIBCO Software Inc.
- 29. © Copyright 2000-2016 TIBCO Software Inc.
- 30. © Copyright 2000-2016 TIBCO Software Inc.
- 31. © Copyright 2000-2016 TIBCO Software Inc.
- 32. Voltage Temperature Vibration “When the temperature of any pump goes up more than 20 degrees for any 10 MINUTE window, SCHEDULE MAINTENANCE” Algorithmic IoT
- 33. Streaming & Batch Analytics Continuous Predictive Maintenance Risk Management TIBCO Live Datamart Integration Geo-aware analytics Facility Management Alert Targeting TERR, PMML, StreamBase, BusinessEvents Digital Operations TIBCO LiveView Analytics Spotfire ALERTS Case Management Mobile Weather BusinessWorks,EMS,TIBCOMashery,eFTL TIBCO BPM Data Scientists Digital Operations (e.g., Drilling Operations) OSI PI Engineering Documents Financial WITSML In Memory Data Grid Open Spirit MDM Cloud Foundry Industrial Equipment Monitoring Industrial Equipment & Spark Spark
- 34. 1. Train Time Table Deviation: 40% Reduction in Spread (Dutch Railways) 2. Intelligent Equipment: Saving $40M / year (Oil & Gas – Many) 3. Algorithmic Loyalty: Finding the Jacket You Didn’t Know You Needed (North Face) 4. Predictive Risk & Compliance: Avoiding $440M in 40 Minutes of Loss (ConvergEx) 5. Live Flight Optimization: Get You Home on Time (United Airlines) 6. IT and Transaction Optimization: Watch 20,000 Systems at Once (Morgan Stanley) 7. Parcel Tracking: From 20% to 100% Real-Time (Royal Mail, UK) 7 Predictive Analytics, Spark, Streaming Use Cases
- 35. © Copyright 2000-2016 TIBCO Software Inc.
- 36. Streaming Data Continuous Digital Loyalty IoT Streaming Analytics Social Analytics Live Datamart Enterprise Data Integration In memory data grid Segment & Target Offers & Points Digital Operations Analytics ALERTS Algorithmic Loyalty Case Management API Management Supply Chain Partners Mobile Vehicles Mobile Loyalty Wearables Data Scientists Digital Operations Call Centers Mobile Rewards Generic EventsReport & Analyze Operations
- 37. 1. Train Time Table Deviation: 40% Reduction in Spread (Dutch Railways) 2. Intelligent Equipment: Saving $40M / year (Oil & Gas – Many) 3. Algorithmic Loyalty: Finding the Jacket You Didn’t Know You Needed (North Face) 4. Predictive Risk & Compliance: Avoiding $440M in 40 Minutes of Loss (ConvergEx) 5. Live Flight Optimization: Get You Home on Time (United Airlines) 6. IT and Transaction Optimization: Watch 20,000 Systems at Once (Morgan Stanley) 7. Parcel Tracking: From 20% to 100% Real-Time (Royal Mail, UK) 7 Predictive Analytics, Spark, Streaming Use Cases
- 38. “In December, 2012, Knight Capital lost $460M in under 40 minutes. That changed everything. Now, it’s no longer acceptable to run our business based on end-of-day reports.” - Head of Risk Management, top 3 bank
- 39. Continuous Compliance Market Data Stream Streaming Analytics Large Orders Marking the tape Layering Ramping on close Alerts Ramping on open Spiking Spoofing (1) Spoofing (2) Spoofing (3) Wash Trades Sensitivity Adjustments Wall Street Continuous Compliance Architecture Audit Trail Logging Compliance Alerting Audit Order Stream Live Datamart In aggregate, peak event rates of 600,000 events a second, or a rate of 51 billion events a day Compliance staff +100M orders a day, 90% cancel rate 500,000 EPS peak Continuous compliance analytics answer every interesting surveillance question, (at the peak rate of) 51 billion times a day Orders Market Data Contextual Case Management
- 40. Continuous Query Continuous Query Processor Alerts Rules FTL EMS ActiveSpaces Application Data Social Media Data Market Data Sensor Data Spark In memory data grid Enterprise data Market Data IoT Mobile Social Command & Control ACTION The Birth of the Live Datamart Live Datamart
- 41. 1. Train Time Table Deviation: 40% Reduction in Spread (Dutch Railways) 2. Intelligent Equipment: Saving $40M / year (Oil & Gas – Many) 3. Algorithmic Loyalty: Finding the Jacket You Didn’t Know You Needed (North Face) 4. Predictive Risk & Compliance: Avoiding $440M in 40 Minutes of Loss (ConvergEx) 5. Live Flight Optimization: Get You Home on Time (United Airlines) 6. IT and Transaction Optimization: Watch 20,000 Systems at Once (Morgan Stanley) 7. Parcel Tracking: From 20% to 100% Real-Time (Royal Mail, UK) 7 Predictive Analytics, Spark, Streaming Use Cases
- 42. © Copyright 2000-2016 TIBCO Software Inc. Live Flight Operations & United Airlines
- 43. 1. Train Time Table Deviation: 40% Reduction in Spread (Dutch Railways) 2. Intelligent Equipment: Saving $40M / year (Oil & Gas – Many) 3. Algorithmic Loyalty: Finding the Jacket You Didn’t Know You Needed (North Face) 4. Predictive Risk & Compliance: Avoiding $440M in 40 Minutes of Loss (ConvergEx) 5. Live Flight Optimization: Get You Home on Time (United Airlines) 6. IT and Transaction Optimization: Watch 20,000 Systems at Once (Morgan Stanley) 7. Parcel Tracking: From 20% to 100% Real-Time (Royal Mail, UK) 7 Predictive Analytics, Spark, Streaming Use Cases
- 44. © Copyright 2000-2016 TIBCO Software Inc.
- 45. 1. Train Time Table Deviation: 40% Reduction in Spread (Dutch Railways) 2. Intelligent Equipment: Saving $40M / year (Oil & Gas – Many) 3. Algorithmic Loyalty: Finding the Jacket You Didn’t Know You Needed (North Face) 4. Predictive Risk & Compliance: Avoiding $440M in 40 Minutes of Loss (ConvergEx) 5. Live Flight Optimization: Get You Home on Time (United Airlines) 6. IT and Transaction Optimization: Watch 20,000 Systems at Once (Morgan Stanley) 7. Parcel Tracking: From 20% to 100% Real-Time (Royal Mail, UK) 7 Predictive Analytics, Spark, Streaming Use Cases
- 46. Location Automation Rules TIBCO BusinessEvents Enterprise Integration BusTIBCO Enterprise Message Bus Analytics Event Aggregator Hadoop TIBCO BusinessWorks Enterprise Application Web In-Memory Operational Data Store TIBCO BusinessWorks, Activespaces SMS Email PDA API Management TIBCO API Exchange Mobile Apps Operational Control TIBCO Live Datamart & LiveView Partners Enterprise Application Enterprise Application Enterprise Apps Sensor Data The Postal Service Internet of Things
- 47. 1. Live Train Time Tables: 40% Reduction in Spread (Dutch Railways) 2. Intelligent Equipment: Saving $40M / year (Oil & Gas – Many) 3. Algorithmic Loyalty: Finding the Jacket You Didn’t Know You Needed (North Face) 4. Predictive Risk & Compliance: Avoiding $440M in 40 Minutes of Loss (ConvergEx) 5. Live Flight Optimization: Get You Home on Time (United Airlines) 6. IT and Transaction Optimization: Watch 20,000 Systems at Once (Morgan Stanley) 7. IoT Parcel Tracking: From 20% to 100% Real-Time (Royal Mail, UK 7 Predictive Analytics, Spark, Streaming Use Cases
- 48. Hadoop + Analytics: Discover, Automate, Act AUTOMATE: Inject predictive model into stream DISCOVER: Data scientists use interactive analytics to discover, score and model based on Hadoop / Spark data lakes Automation Case ManagementOperational Intelligence ACT: Automation, alerting and refinement, BPM
Notas del editor
- 3:30
- LET’S LOOK AT ONE OF OUR FIRST ACCELERATORS VEHICLES TODAY ARE MOBILE DEVICES. CARS HAVE SENSORS. TRAINS HAVE SENSORS. DELIVERY VEHICLES HAVE SENSORS. FEDEX, AIRLINES: CREDIBILITY [CLICK] A LOT OF COMPANIES STILL HAVE A REAR-VIEW –MIRROR APPROACH. [CLICK] AT THE SAME TIME, CUSTOMERS – ESPECIALLY MILLENIALS – WERE PRACTICALLY BORN WITH MOBILE PHONES. THEY EXPECT REAL-TIME INSIGHT INTO EVERYTHING.`
- ACCELERATORS AREN’T JUST DEMOS. THEY ARE ARCHITECTED, DOCUMENTED, AND TESTED INFRASTRUCTURE UPON WHICH YOU CAN GROW AND INNOVATE
- HERE’S THE CVA ARCHITECTURE THAT SHOWS THE TIBCO COMPONENTS IN PLACE. BY PROVIDING AN OUT-OF-THE-BOX ARCHITECTURE, YOUR TECHNICAL TEAMS CAN GET UP AND RUNNING WITH THE RIGHT ARCHITECTURE, MORE QUICKLY.
- HERE’S A DEMONSTRATION OF THE CVA IN ACTION: WE LOOK AT A MOMENT IN THE LIFE OF A CONNECTED VEHICLE: A TRAIN. HERE’S WHAT I’M GOING TO SHOW YOU: THE CVA SIMULATES THOUSANDS OF TRAINS TRANSMITTING THEIR LOCATION, CAPACITY, ETC. BAD WEATHER WILL INTRODUCE A DELAY THE CVA WILL ALERT OPERATORS THAT AN EXTERNAL EVENT HAS CAUSED A SYSTEMIC PROBLEM, AND WILL HELP THEM PINPOINT WHERE THE PROBLEM IS, AND REMEDIATE THE PROBLEM. THE DELAY WILL BE ISOLATED AND ADDRESSED IN REAL-TIME, AND THE SYSTEM WILL RETURN TO NORMAL.
- 1 – The CVA LiveView UI, with queries executing against the Live Datamart, shows trip 2202 operating from Dordrecht to Amsterdam. The slightly off-grey box with the blue stripe and the orange strip is the current trip. The orange stripe means it’s currently operating on time. The same trainset will operate trip 2211 from Amsterdam back to Dordrecht after it completes 2202, which is the next box down vertically, with only the blue stripe. Not yet started, so no orange stripe. The grey box is the scheduled time, the blue stripe is BusinessEvents estimated times, the orange stripe is actual time.
- AND THE CVA ARCHITECTURE SHOWS AN EFFECTIVE EXAMPLE OF HOW TO CONNECT, FOR EXAMPLE, BE AND LIVE DATAMART.
- 2 – In CVA, simulators are build-in to help you create an alert due to technical problems that may cause delays. Here, we simulate a “new technical problem alert” in the system.
- 3 – The technical problem delay creates alerts in BE rules, (last one down) plus all the block delay alerts generated by BE because of our technical alert. Each of these represent a trip that will be delayed because of the technical problem, but not directly… only as a consequence of resource scheduling.
- 4 – Now trip 2202 the blue stripe has turned red because it’s delayed and it extends beyond the schedule box, into the time period when trip 2211 would normally start. In other words, the trainset is going to arrive in Amsterdam at 0845 instead of 0817 so it won’t be able to depart on time for trip 2211 at 0842. Instead it will depart 8 minutes late at 0850.
- CVA has now discovered an alert that could be sent to customers via BW, start a case n AMX BPM, send a message via EMS, Tweet to the public on Twitter…. An here, we show the operator clearing the alert – action has been taken to deal with the problem.
- 8 – Again trip 2202 the trip is still delayed, but much less. This is because it had already entered the speed restricted section, but it is able to speed up for the rest of it once the alert cleared. So now it’ll arrive late at 0828, but this is enough time for the trainset to operate the subsequent trip 2211 on time at 0842. So that block delay has disappeared.
- HERE’S THE CVA ARCHITECTURE THAT SHOWS THE TIBCO COMPONENTS IN PLACE. BY PROVIDING AN OUT-OF-THE-BOX ARCHITECTURE, YOUR TECHNICAL TEAMS CAN GET UP AND RUNNING WITH THE RIGHT ARCHITECTURE, MORE QUICKLY.
- HERE’S THE CVA ARCHITECTURE THAT SHOWS THE TIBCO COMPONENTS IN PLACE. BY PROVIDING AN OUT-OF-THE-BOX ARCHITECTURE, YOUR TECHNICAL TEAMS CAN GET UP AND RUNNING WITH THE RIGHT ARCHITECTURE, MORE QUICKLY.