gpu-accelerated analytics: case studies and use cases

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  • GPU-Accelerated Analytics Enable New Enterprise Solutions

    11/30/16

    Case Studies and Use Cases

  • Enabling New Enterprise Solutions

    2

    Retail: Customer 360/customer sentiment, supply chain optimizationCorrelating data from point of sales (POS) systems, social media streams, weather forecasts, and even wearable devices. Better able to track inventory in real time, enabling efficient replenishment and avoiding out-of-stock situations.

    Powering High Performance Analytics as a Service SolutionDelivering customer-focused services by leveraging all available transactional data. Currently no ability for business user to do customized analytics; IT has to. Query response times taking 10s of minutes, some over 2 hours, thus limiting ability to analyze and use data.

    Fin servicesLarge scale risk aggregations and billion+ row joins in sub-second time (5TB+ tables choke on RDBMS joins and Hadoop is too slow).

    Manufacturing IoTLive streaming analytics on component functionality to ensure safety (avoid failures) and validate warranty claims .

    Ridesharing/Connected CarsView all passengers and drivers to monitor behavioral analytics. Sensor enabled predictive maintenance on connected cars.

    PresenterPresentation NotesUse cases in play include:Real-time sentiment analysisReal-time anomaly detection to prevent fraudReallocate resources on the fly based upon personnel, environmental and seasonal dataTrack terrorist and other national security threats in real timeOptimize energy generation and uptime based on fluctuating usage patterns and unpredictable natural disastersImprove customer engagements by correlating data from point of sales (POS) systems, social media streams, weather forecasts, and other data. Track inventory in real-time, enabling efficient replenishment and avoiding out-of-stock situationsConnected home, connected car, connected citiesMore rapid query response time and data discovery capabilities for end usersFaster iterations to risk modeling and modeling optimization

  • US Army INSCOM

    US Armys in-memory computational engine for any data with a geospatial or temporal attribute for a major joint cloud initiative within the Intelligence Community (IC ITE).

    Intel analysts are able to conduct near real-time analytics and fuse SIGINT, ISR, and GEOINT streaming big data feeds and visualize in a web browser.

    First time in history military analysts are able to query and visualize billions to trillions of near real-time objects in a production environment.

    Major executive military and congressional visibility.

    Oracle Spatial (92 Minutes)

    42x Lower Space28x Lower Cost38x Lower Power Cost

    U.S Army INSCOM Shift from Oracle to Kinetica

    Kinetica(20ms)

    1 Kinetica server vs 42 servers with Oracle 10gR2 (2011)

    CASE STUDY

    PresenterPresentation NotesGame changer for military; first time in history they can fuse UAV live track data with SIGINT data to target on the fly with high accuracy 1 server with GPUdb can replace 42 server with Oracle 10gR2. 42 servers doing a geospatial query took 92 minutes; same data, same query 1 GPUdb server took less than 1 second

  • Cybersecurity

    4

    CASE STUDY

    Real-time anomaly detectionFusing multiple data feeds.Protecting its financial clients against current and emerging cyber threats.

    PresenterPresentation Notes

    US ARMY again mention

    USPS is the single largest logistic entity in the country, moving more individual items in four hours than the combination of UPS, FedEx, and DHL move all year.Distributed analysis - USPS parallel cluster is able to serve up to 15,000 simultaneous sessions, providing the services managers and analysts with the capability to instantly analyze their areas of responsibility via dashboards and to query it as if it were a relational database. At scale With 200,000 USPS devices emitting location once every minute, that amounts to more than a quarter billion events captured and analyzed daily tracked on 10 nodes.

    Ironnet several major banks, and Sis

    PG&E - Data silos between gas, electric, distribution/transportation, fiber vs land-based.We can consolidate feeds, fuse analytics, proactive on utility data, prevent crisis, full real time visibility on workforce, meters, grids.

  • Real-time fleet optimization

    DISTRIBUTED ANALYSIS

    USPS parallel cluster is able to serve up to 15,000 simultaneous sessions, providing managers and analysts with real-time dashboards

    AT SCALEWith 200,000 USPS devices emitting location once every minute, that amounts to more than a quarter billion events captured and analyzed daily.

    USPSthe largest logistic entity in the UStracks carrier movements in real time.

    Reallocates resources on the fly based upon personnel, environmental and seasonal data.

    CASE STUDY

    In production since 2014 with 5 9s uptime

    PresenterPresentation NotesBeen running since Nov 2014 at 5 9s

  • Real-time fleet optimization

    Analyzing Breadcrumb Data to Improve Services 200,000 USPS devices emit location once every minute, amounting to more than a quarter billion events daily.

    By analyzing this data, the USPS is able to 1) understand where spending would

    achieve the best results2) make faster and more efficient strategic

    decisions 3) provide customers with a reliable service4) reduce costs by streamlining deliveries

    CASE STUDY

    Processing Geospatial Data for Real-Time Decision-makingDispatchers can graphically view employee territory assignments (routes, delivery points, and collection points).

    The USPS makes better use of routes by: 1) finding inefficiencies such as overlapping

    coverage of assigned areas, uncovered areas, and distribution bottlenecks

    2) improving contingency planning if a carrier was unable to deliver to assigned routes

    3) using their workforce more efficiency by aggregating point-to-point carrier performance data

    Wins IDC HPC Innovation Excellence Award, 2016

    PresenterPresentation NotesBeen running since Nov 2014 at 5 9s

  • Redesign Retail without Redesigning your Data

    Multidimensional query on product category x sales on map.

    Drill down to any geographic area for enhanced detail.

    CASE STUDY

  • Smart Grid Visualization and Optimization

    8

    Bringing real-time visibility and analytics to legacy systemGeospatially visualizing smart grid while ingesting real-time vector data streaming from smart meters, workforce and grid.

    Decreasing current SLA for geospatial analytics. Leveraging open standards. Consolidating feeds and layering multiple datasets on the same visualization, fusing analytics.

    Striving for optimized energy generation and uptime based on fluctuating usage patterns and unpredictable natural disasters and crisis prevention.

    CASE STUDY

    PresenterPresentation Notesperform geo-spatial queries (freehand-polygon for example) that current system cannot do, have on the fly rendering of heat maps, flows, etc with real time drill down to an individual component (meter, transformer, pipe, etc)

    PG&E - Data silos between gas, electric, distribution/transportation, fiber vs land-based.We

    visualize billions of points,

  • Retail Solutions

    Customer 360/Customer SentimentOut think the status quo. Correlate data from point of sales (POS) systems, social media streams, weather forecasts, and even wearable devices. Convert billions of impressions to millions of dollars.

    Be able to better understand your customers and your business by querying massive datasets in seconds vs. hours to help you become a real-time retail enterprise.

    Supply Chain OptimizationMeet demand spikes in real time. Better track inventory in real time from a variety of sources, enabling efficient replenishment and avoiding out-of-stock situations.

    PresenterPresentation NotesUse cases in play include:Real-time sentiment analysisReal-time anomaly detection to prevent fraudReallocate resources on the fly based upon personnel, environmental and seasonal dataTrack terrorist and other national security threats in real timeOptimize energy generation and uptime based on fluctuating usage patterns and unpredictable natural disastersImprove customer engagements by correlating data from point of sales (POS) systems, social media streams, weather forecasts, and other data. Track inventory in real-time, enabling efficient replenishment and avoiding out-of-stock situationsConnected home, connected car, connected citiesMore rapid query response time and data discovery capabilities for end usersFaster iterations to risk modeling and modeling optimization

  • Automotive Solutions

    RidesharingView all passengers and drivers to monitor behavioral analysis. Watch for fast acceleration, sudden braking, too many U turns, etc. to avoid risk/lawsuits of faulty drivers.

    Connected carsSystems monitoring, live streaming ingest and analytics for sensor enabled predictive maintenance. Automated emergency management, smart driving assistance, and real-time fleet managment

    PresenterPresentation NotesUse cases in play include:Real-time sentiment analysisReal-time anomaly detection to prevent fraudReallocate resources on the fly based upon personnel, environmental and seasonal dataTrack terrorist and other national security threats in real timeOptimize energy generation and uptime based on fluctuating usage patterns and unpredictable natural disastersImprove customer engagements by correlating data from point of sales (POS) systems, social media streams, weather forecasts, and other data. Track inventory in real-time, enabling efficient replenishment and avoiding out-of-stock situationsConnected home, connected car

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