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Room 806 powerpoint
Room 806 powerpoint
Room 806 powerpoint
Room 806 powerpoint
Room 806 powerpoint
Room 806 powerpoint
Room 806 powerpoint
Room 806 powerpoint
Room 806 powerpoint
Room 806 powerpoint
Room 806 powerpoint
Room 806 powerpoint
Room 806 powerpoint
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Room 806 powerpoint

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  • Sentiment – the most commonly sighted source.. You must have heard a lot about companies trying to leverage this data to provide sentiment trackers, identify influencers etc..
    Clickstream – the trail a user leaves behind as he navigates your website.. Analyze the trail to optimize website design
    Sensor/Machine – these are soon taking over..are everywhere.. Cars, Nike put one in shoes, health equipment, smartphones. God someone also put one in baby diapers.. They call it ‘proactive maintenance’
    Geographic – location based data– the common use you must have heard is location based targeting but this data has much more winder application in supply chain optimization across the manufacturing industry.. Allows them to optimize routes, predict inventory levels etc.
    Server logs – this one is not new to the IT world.. Back when I was a developer, I remember having the configure the log sizes so they could roll over.. The issue there is you often lose precious trails and information.. Today, you should not have to lose this data, you just save the data to Hadoop
    Text – This is everywhere.. We all love to express ourselves,, every blog, article, news site, ecommerce site you go these days, you will find people putting out their thoughts.. And this is on top of the already existing text sources like surveys, content itself.. How do you store, search and analyze all this text data to glean for key insights?
  • Transcript

    • 1. THE FUTURE HAS NEVER BEEN SO OPEN NOVEMBER 6 / THE NEWSEUM / WASHINGTON D.C.
    • 2. Track 2: Red Hat Cloud IaaS Customer Success Stories 11:00 A.M. – 11:45 A.M.
    • 3. Speakers Include: •Wolf Tombe, CTO, U.S. Customs & Border Protection, DHS •Hoot Thompson, System Engineer, NASA Goddard •Dr. Christopher Jacobson, Chief Technologist, U.S. Federal, Systems & Technology Group, IBM •Moderator: Bob Kozdemba, Principal Solutions Architect, Public Sector, Red Hat
    • 4. THE FUTURE HAS NEVER BEEN SO OPEN NOVEMBER 6 / THE NEWSEUM / WASHINGTON D.C.
    • 5. Track 2: Big Data as a Service 11:50 A.M. – 12:35 P.M.
    • 6. Speakers Include: •Chris Layton, HPC Systems Administrator, National Center for Computational Sciences, Oak Ridge National Laboratory •Xavier Hughes, Chief Innovation Officer, Dept. of Labor •Dr. Dave Bauer, Chief Scientist, Data Tactics •John Kreisa, VP of Strategic Marketing, Horton Work •Moderator: Toan Do, Director, Intelligence Programs, Red Hat
    • 7. Daniel Ricciuto (left) and Peter Thornton (right) using the Exploratory Data analysis ENvironment (EDEN) to visually explore multiple Community Land Model (CLM) simulation data sets. In particular, Ricciuto and Thornton are analyzing sensitivities in the Amazonia region using the interactive visual analytics in EDEN on EVEREST's Planar display.
    • 8. Chad Steed using EDEN on EVEREST to explore 1000 CLM4 simulations (81 parameters and 7 output variables) on the previous version of the EVEREST display wall.
    • 9. Big & open data provides an opportunity for external partners to help meet our mission and goals. Triumph through crowd-sourcing. Innovation though collaboration.
    • 10. A Traditional Approach Under Pressure Business Analytics Custom Applications Packaged Applications 2.8 ZB in 2012 85% from New Data Types RDBMS EDW MPP 15x Machine Data by 2020 REPOSITORIES 40 ZB by 2020 Source: IDC Existing Sources (CRM, ERP, Clickstream, Logs) © Hortonworks Inc. 2013 Emerging Sources (Sensor, Sentiment, Geo, Unstructured) Page 10
    • 11. Most Common NEW TYPES OF DATA 1. Sentiment Understand how your customers feel about your brand and products – right now 2. Clickstream Capture and analyze website visitors’ data trails and optimize your website 3. Sensor/Machine Discover patterns in data streaming automatically from remote sensors and machines 4. Geographic Analyze location-based data to manage operations where they occur Value 5. Server Logs Research logs to diagnose process failures and prevent security breaches 6. Unstructured (txt, video, pictures, etc..) Understand patterns in files across millions of web pages, emails, and documents © Hortonworks Inc. 2013 + Keep existing data longer!
    • 12. An Emerging Data Architecture New Custom Applications Business Analytics Packaged Applications BUILD & TEST RDBMS EDW MANAGE & MONITOR MPP REPOSITORIES Existing Sources (CRM, ERP, Clickstream, Logs) © Hortonworks Inc. 2013 Emerging Sources (Sensor, Sentiment, Geo, Unstructured) Page 12
    • 13. Federal Government & Big Data • Law Enforcement/Security – Store and process biometric identification for individuals – Multi-modal ID increases accuracy, but requires more data storage and parallel processing for distinct matching algorithms: – Facial Recognition, Fingerprints, Voice, Gait • Environmental Protection Agency (EPA) – Capture machine generated data to monitor air, water & land quality – Combine sensor data and social media / sentiment analysis • Social Security Administration (SSA) – Finding fraudulent claims for benefits using big data analysis to look for patterns of fraudulent behavior © Hortonworks Inc. 2013

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