Understandable Artificial Intelligence

Infrastructure Review

Building On Principals, A New Frontier

The EDS Infrastructure -- Big Picture

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Cloud Resources

We seamlessly combine physical and cloud resources which can be dynamically managed

EDS Data Center

Combined Computing Power

Client Interface

Our Technology Stack

©2018 Economic Data Sciences




©2018 Economic Data Sciences

  • Asynchronous

    • Actions occuring at the same time, in any order, without waiting on each other

    • Current solutions are sequence oriented

  • Distributed

    • Connecting many smaller systems to work together

    • Current solutions grow by sequencing faster

  • Modular

    • This principal means components can be easily repurposed

    • As opposed to current monolithic designs

Many underlying principals drive the design of the EDS infrastructure. In this section we will define and compare them with current solutions.

Principals (cont)

©2018 Economic Data Sciences

  • Scalable and Redundant

    • Built expecting failure and can easily 'drop-in' new resources live

    • Current solutions would need to be shut down and migrated

  • Analysis Focused not Storage Focused

    • We store data multiple times, in multiple forms, focused on insight

    • Current solutions minimize storage cost, but make insight costly

  • Flexible Data Consumption

    • Takes all data comers, SQL, NoSQL -- structure, unstructure

    • Current solutions have strict data structure requirements

These principals represent a revolution in solution design.

The EDS Infrastructure - Components

©2018 Economic Data Sciences

Each component of the technology stack leads towards our infrastructure principals and a more effective solution

Cassandra Database Meets All Puts Data of Any Kind Closer To Analysis For Speed and Flexibility
Hadoop Distributed Disk Meets All Quickly Consumes Any Type of Data
Mesos Scheduler Meets All Coordinates Tools for 'Drop-in' Flexibility
Zookeeper Failover Coordinator Meets All Coordinates Redistribution of 'Duties' During Failures Or When Connections Are Dropped
Spark, Scala, and R Analytics Combined -- Meets All When Combined, Tools Provide Analytics In Ways That Align With Principals
Play Front-End Meets All Visualizes to client and collects client information
Component Purpose Principal What It Does

The EDS Infrastructure - Security

©2018 Economic Data Sciences

  • Point to point encryption over the network
    • Data is never exposed without encryption
  • Client to server encryption
    • In line with best practices at banks. Each client session is encrypted regardless of where the client is connecting from
  • Cloud compute not cloud storage
    • We use the cloud for compute power not storage, we control the physical location of each data point
  • Multi-tiered security layers
    • Designed with multiple layers of security so each must be compromised separately

Always at the top of our list

EDS Infrastructure - Summary

©2018 Economic Data Sciences

  • Scale and speed
    • Add nearly an unlimited number of 'factors' - qualitative/quantitative
    • Can run in seconds/minutes and so could be done 'live' for clients
    • Each analysis is applied for all
  • Data communication - goodbye to silos
    • Each part of the business process can share information
  • More in depth client feedback
    • Better understanding of client interest

These features make possible real advances 'on the ground'

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©2018 Economic Data Sciences