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2014.11 meetup presentation v1

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Ke Labs presentation for the Data Science Indy Meetup. We describe in detail the benefits of our software for the creation of information base application and use by data scientists.

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2014.11 meetup presentation v1

  1. 1. Addressing the challenges of data scientists through a new modeling technology Ke Labs, Inc. Tom Feigenbaum, CEO 3500 DePauw Boulevard, Suite #1080 Indianapolis, IN 46268 Telephone: (317) 824-1225 www.kelabs.com sales@kelabs.com
  2. 2. Ke Labs – Who We Are A comprehensive technology builds applications that access, prepare, transform, analyze, visualize, monitor, report, share information and more… Big Data Intelligent Computing Artificial Intelligence Information-Based Applications Decision Support Advanced Software Applications
  3. 3. The Information Unit Model •A new knowledge representation schema •Patented – Systems and Methods for Cognition-Based Processing of Knowledge •Powerful, general, intuitive, fun, cool •The Unit Modeler Intelligent Software Development Environment (ISDE)
  4. 4. Information Based Applications Models Prepare Prepare Prepare Translate & Merge Communicate Models Models Import Graphics Reports UI Analysis Model Models Models Models Common Data Repository Data Sources Spreadsheets Databases Documents Web Services Users Model Specific Data Repository Common Data Repository A generalized view of an information based application. Any particular system may include any or all of its components. Systems can work together to form bigger systems. Query Load http DB, csv, domain Tools address all areas •Stand-alone •Components of integrated applications
  5. 5. The Component Nature of Models M1 M2 M4 M3 M5 M1 M2 All models can interact with each other and be incorporated into new models. Site Data Prepare Translate & Merge Common Data Repository Communicate Analyze
  6. 6. Network Architectures Work off of the Cloud or Setup Your Own Private Server Complex architectures can be built supporting high capacity, integration, access and security. Functional processing can be distributed amongst multiple processing units.
  7. 7. Example: Providing Analytic Services Over a Distributed Network of Data Sources m
  8. 8. Example: Near Real-time Dashboard
  9. 9. Extensive Visualizations
  10. 10. Complex User Interfaces •Automatically Generated •Unlimited Complexity •Dynamic
  11. 11. Complex Algorithms
  12. 12. Technology Demonstration •The Unit Modeler ISDE •Workgroups •Data Analytics Package
  13. 13. Questions

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