Big Data Challenges, Presented by Wes Caldwell at SolrExchage DC
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Big Data Challenges, Presented by Wes Caldwell at SolrExchage DC

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Big Data Challenges, Presented by Wes Caldwell at SolrExchage DC Presentation Transcript

  • 1. Big Data Challenges in the DoD and IC Wes Caldwell Chief Architect Intelligent Software Solutions
  • 2. Topics • Introduction to ISS • The growth of data • Our customer’s data environment • The need for effective big-data management • Search as the cornerstone of a big-data strategy
  • 3. About ISS • Headquartered in Colorado Springs • Other offices located in Washington DC, Hampton VA, Tampa FL, and Rome NY • Innovative Solutions from “Space to Mud and Everything Between” • Sole prime on multiple Air Force Research Labs programs IDIQ • Currently Executing More Than 100 Software Development Projects • Over 800 employees • Strength in Solutions Development and Deployment • Consistently Recognized as a Leader • Recognized as a Deloitte Fast 50 Colorado company and a Deloitte Fast 500 company over eight consecutive years • Three-time Inc. Magazine 500 winner • 2009 Defense Company of the Year
  • 4. ISS Solution Space/Value Proposition • Reusable and license-free to US Federal Government (GOTS) • Committed to providing best ROI to our customers by integrating leading open-source solutions into our products and services • Scalable from a single desktop solution to large distributed networks with thousands of users • Customizable to each organization’s unique analytical and information technology infrastructure • Operationally proven, secure and accredited for all major classified networks
  • 5. ISS Business Strategy Government Off The Shelf (GOTS) Commercial Off The Shelf (COTS) Subject MatterExperts (SMEs) • Low Barrier to Entry: No license fees to US Government Agencies • Fast: Proven baseline provides immediate capability • Turnkey: Highly customizable solutions can be implemented quickly with no development • Solutions Oriented: Subject Matter Experts support implementation in each domain • Low Cost: Cost of Adding Features is shared across large customer base; all customers benefit Blending the best elements of each industry model to provide low risk, nonproprietary, high payoff solutions—fast! 6
  • 6. The growth of data • Most electronic information is not relational, but unstructured (textual, binary) or semi- structured (spreadsheet, RSS feed, etc.) – In 2007, the estimated information content of all human knowledge was 295 exabytes(295 million terabytes) – Data production will be 44 times greater in 2020 than in 2009 • Approx 35 zetabytes total (35 billion terabytes) • A majority of the data produced in the future will be unstructured – A tremendous amount of information and knowledge is dormant within unstructured data
  • 7. Our customer’s data environment • Literally thousands of data sources/feeds from a variety of strategic, national, and tactical sources – Media (documents, images, etc.) – Human interactions – Geospatial – Open Source (News feeds, RSS) – Imagery/Video – Many more…
  • 8. How our analysts feel
  • 9. The need for effective “big-data” management • Analysts are looking to extract knowledge from the massive heterogeneous data sets, providing “actionable intelligence” • Tactical environments absolutely demand effective management of data – Time to live on the relevance of data collected can be very short – Communications pipes aren’t as optimal as large CONUS-based data centers, so reduction of data based on tactical conditions (i.e. AOR, Problem Domain, etc.) is critical • Search and Analytics are key enablers to allow an analyst to reliably search through large amounts of information, and to focus their efforts around a subset of that information to perform deeper analysis
  • 10. Search IS the cornerstone of an effective big-data strategy Structured Content Semi-Structured Content Un-Structured Content Content Cache (Haystacks) Content Acquisition Tenets • Connector architecture • Data normalization • Data staging • Data Compartmenting (Multiple Haystacks) Tenets • Optimized Index of Content for Search and Discovery of Big Data • Analyst Topics that “Shrink the Haystack” Search Features (Facets, Auto- Complete, Tagging, Comments, etc.) • Semantic (Synonym) Search based on pluggable taxonomies Search/Discovery Content Index NLP Pipeline Semantic Enrichment Categorization Named Entity Recognition Clustering Gazetteers Tenets • “Domain Spaces” that support pluggable entity recognition and categorization • Continuous feedback loop that improves the system over time with analyst input • Lexicon-based analytics that allows for targeted categorization across corpus of data Tenets • Data Reduction into focused “Data Perspectives” • Data perspectives stored in optimized formats (e.g. Graph, Time Series, Geo, etc.) for the questions being asked • Leveraging industry- standard parallel processing frameworks for scalable analytics Data Perspectives Data
  • 11. How can Search help you? Have a great conference!!!