Smart Data Webinar: Choosing the Right Data Management Architecture for Cognitive Computing

Choosing the Right Data Management Architecture

for Cognitive Computing

Adrian Bowles, PhD

Founder, STORM Insights, Inc.

Lead Analyst, AI, Aragon Research

info@storminsights.com
Copyright (c) 2017 by STORM Insights Inc. All Rights Reserved.
OCTOBER 12, 2017

AGENDA - CHOOSING THE RIGHT DATA MANAGEMENT ARCHITECTURE FOR COGNITIVE COMPUTING
The Role of Data In AI & CC

What do we need to manage?

Application, Data, and Algorithm Attributes that Inﬂuence Architecture

Database Options

Open Source Infrastructure

Prebuilt Knowledge

Getting Started: Basic Principles

Model
COGNITIVE COMPUTING FUNDAMENTALS: MODELS & ASSUMPTIONS
Model
The Corpus, Assumptions, Algorithms
Used to
Generate & Score Hypotheses
or
Calculate The Strength of a Relationship
Principles that control the
development and representation
of natural intelligence in the
neocortex provide a guide to the
implementation of machine
intelligence.(Numenta
Hierarchical Temporal Memory)
A function applied to a string
representing data or a concept
results in a value or vector
meaningful for comparison.
A Model is an Abstract Representation of Reality
Essential Data for
Cognitive Computing

MODELS WILL MAKE OR BREAK YOUR APPLICATION
Your Model The Real World
“When the map and the terrain disagree, believe the terrain.”
Gause and Weinberg (Exploring Requirements)

Systems
Controls
Learn
Plan Reason
Understand
Model
Data Mgmt
Human
Machine
Input Output
Gestures
Emotions
Language
Narrative Generation
Visualization
Reports
Haptics
Sensors
(IOT)
Systems
Controls
COGNITIVE SYSTEMS: COMMUNICATIONS & CONTROL
Perception

WHERE YOU ARE DICTATES WHAT YOU NEED
Ingest Analyze Maintain/Manage

When everything is connected…

New sources of data emerge

New sources of value emerge

Old assumptions must be challenged
THE IMPACT OF THE IOT

CHOICES HAVE CONSEQUENCES
How You Think About a Domain…
…inﬂuences your choice of maps and models…

rules and representations…and required operations.

HOW YOU ORGANIZE CONSTRAINS HOW YOU WORK - DESIGN WORKFLOW FIRST

START WITH A TAXONOMY
A taxonomy represents the formal structure of classes or types of objects within a domain.

•Generally hierarchical and provide names for each class in the domain.

•May also capture the membership properties of each object in relation to the other objects.

•The rules of a specific taxonomy are used to classify or categorize any object in the domain, so
they must be complete, consistent, and unambiguous. This rigor in specification should ensure that
any newly discovered object must fit into one, and only one, category or object class.

ONTOLOGIES
An ontology formalizes and specifies the names, definitions,
and attributes of entities within a domain. For practical
purposes, an accepted ontology defines the domain.

RDF - Resource Description Framework - A directed, labeled graph.
RDFS - RDF Speciﬁcations Suite Recommendations (Language for representing RDF
vocabularies)
SPARQL - A Semantic Protocol & Query Language for RDF Data
OWL - The Web Ontology Language is a Semantic We
language designed to represent knowledge about things
and relationships between things on the Web.
An OWL Document is an Ontology.
https://www.w3.org/2013/data/
THE SEMANTIC WEB - ALL DATA SHOULD BE ASSOCIATED WITH SEMANTIC ATTRIBUTES (MEANING)
BASICS OF THE W3C SEMANTIC WEB ONTOLOGY STACK

CRITICAL QUESTIONS…
What data do we need?

What data will be produced?

Where does the data get created?

Where does the data get analyzed/reﬁned?

How do we present/output the data?
And for each data category & data lifecycle phase,

What does it look like?

How much is there?
Architectural
Influences

Copyright (c) 2014-2017 by STORM Insights Inc. All Rights Reserved.
DEEP STRUCTURE REQUIRES STRONGER METHODS FOR ANALYSIS
Perception: obvious
structure is easy to
process…
but most of the
interesting stuff isn’t
obvious to a
computer.
Issue:
Do we store or
generate all
intermediate forms?

STATIC
DIVERTED OR
SAMPLED
STREAMINGIN MOTION
STOP AND FRISK
STORED
DATA - SLOTH KILLS
To understand (analyze) data…
Divert the ﬂow?
Pool the data?
Evaluate everything without changing the ﬂow?
Sample? (catch and release?)

COMPLEXITY VS MOBILITY
CCTV SmartPhone
Trafﬁc
Counter Fitbit
Data
Complexity
Stationary Mobile
Low
High
Weather
Station
Telematic
Device

DATA ATTRIBUTES DICTATE ARCHITECTURE CHOICES
Speed
Streaming
Structure/Complexity
Surface_Shallow Dense_Deep
Static

DATA LOCATION INFLUENCES ARCHITECTURE CHOICES
Speed
Streaming
Location
Sensor Gateway Cloud Data Center
Static

ALGORITHM ATTRIBUTES DICTATE ARCHITECTURE CHOICES
Parallelism
Embarrassing
Computational Complexity
n
Sequential
(Parallelism and computational
complexity are not actually
orthogonal…)
p(polynomial)

DATABASE OPTIONS
What Do You Want/Need to Store?

How much? How complex? How fast?
What Do You Want/Need to DO With What You Store?
Options Include…
Files, tables, trees, queues, stacks, lists…

Hierarchical

RDBMS

Object DBMS

NoSQL

Graph
How You Think About a Domain…
…inﬂuences your choice of maps and models…

rules and representations…and required operations.
Data Management
Options

EVOLUTION OF DATA MANAGEMENT SOLUTIONS
Images courtesy of Wikipedia
Today:
Delta Airlines processes 5,000,000 business events per day
Pratt & Whitney jet engine: 5,000 sensors producing 10GB/s/per engine.
Formula 1 car sensors produce about 1.2GB/s
and we need to predict the future…
Perform Operations on Data at Rest

GRAPH DATABASES FOR GRAPH DATA!
Why choose a graph database?
Speed to delivery when the data is naturally modeled as a graph

Simpliﬁes multi-hop queries

Visualization? Baked-in
Do you need an on-premise solution, or to manage your own database?
You Probably Already Think In Graphs if…
You watch detective shows
You remember relationships between people
You took a biology class

Wikipedia contributors. "Taxonomy (biology)." Wikipedia,
The Free Encyclopedia. Wikipedia, The Free Encyclopedia,
11 May. 2016. Web. 12 May. 2016.
GRAPHS 101

Typical crazy wall whiteboard - from Fargo.
A screen from IBM I2 Coplink
GRAPHS 101

GRAPHS 101
Family Tree
LinkedIn Tree

GRAPHS SHOULD BE PART OF YOUR TOOLKIT
A graph is a structure with vertices and edges.
a
e
dc
b
Old Post Road
Cross Highway
Compo
Shinbone Alley
Elk Road
Old Post Road Paved

Old Post Road 11 miles

Elk Road Dirt

Elk Road 2 miles

Cross Highway toll road

Cross Highway 250 miles

Main Street 1 mile

Shinbone Alley .5 miles

a bus stop

b gas station

b Shell

c Elementary school

d House

e Oﬃce building
May be labeled, edges may be directed, all may
be stored/processed by properties
represented as key/value pairs.

GRAPHS HAVE RELEVANT MATHEMATICAL PROPERTIES
e.g. If you represent a graph as a matrix M, then values in Mn
represent the number of paths of length n in the original graph.
a
e
dc
b
a b c d e
a 1
b 1
c 1
d 1
e 1
M =

OVERVIEW OF THE GRAPH DATABASE MARKET
Wikipedia contributors. "Graph database." Wikipedia, The Free Encyclopedia. Wikipedia, The Free Encyclopedia, 11
Property
graph
RDF
RDF - Resource Description Framework, W3C specs for
metadata modeling, now used in knowledge management

OPEN SOURCE FOR GRAPH DATA
Apache TinkerPop, TinkerPop, Apache, Apache feather logo, and Apache TinkerPop project logo are
either registered trademarks or trademarks of The Apache Software Foundation in the United States and other countries.
Apache TinkerPop™ is a graph computing framework for both
graph databases (OLTP) and graph analytic systems (OLAP).
“A graph is a structure composed of vertices and edges. Both vertices and edges
can have an arbitrary number of key/value-pairs called properties. Vertices denote
discrete objects such as a person, a place, or an event. Edges denote relationships
between vertices. For instance, a person may know another person, have been
involved in an event, and/or was recently at a particular place. Properties express
non-relational information about the vertices and edges. Example properties include
a vertex having a name, an age and an edge having a timestamp and/or a weight.
Together, the aforementioned graph is known as a property graph and it is the
foundational data structure of Apache TinkerPop.”
Apache TinkerPop™ is an open source, vendor-agnostic, graph computing
framework distributed under the commercial friendly Apache2 license. When a data
system is TinkerPop-enabled, its users are able to model their domain as a graph
and analyze that graph using the Gremlin graph traversal language.

OPEN SOURCE PROJECTS
Apache Spark
Registered trademarks or trademarks of The Apache Software Foundation
UIMA
Hadoop
Open Source
for Infrastructure

RELEVANT APACHE SOFTWARE FOUNDATION OPEN SOURCE PROJECTS
Apache Storm: “a free and open source distributed realtime
computation system. Storm makes it easy to reliably process
unbounded streams of data, doing for realtime processing what
Hadoop did for batch processing.”
Apache Spark Streaming: “Spark Streaming brings Apache
Spark's language-integrated API to stream processing, letting you
write streaming jobs the same way you write batch jobs.”

RELEVANT APACHE SOFTWARE FOUNDATION OPEN SOURCE PROJECTS
Apache Flink: “open-source stream processing framework for
distributed, high-performing, always-available, and accurate data
streaming applications.”
Apache Samza: “a distributed stream processing framework. It
uses Apache Kafka for messaging, and Apache Hadoop YARN to
provide fault tolerance, processor isolation, security, and resource
management.”
Apache Apex: “Enterprise-grade uniﬁed stream and batch
processing engine.”

USE PRE-BUILT KNOWLEDGE RESOURCES
Off The Shelf
Knowledge

OPENCYC

OFF THE SHELF KNOWLEDGE - NEED TO ASSOCIATE/RECOGNIZE/UNDERSTAND TO
ORGANIZE/REPRESENT
Wordnet(R) Princeton
University "About WordNet."

Princeton University. 2010.
<http://
wordnet.princeton.edu>

Do you have or can you capture streaming data that can increase your value proposition?
Data about your product that can improve performance, reliability, predictability…
Can you create value from new analysis of open data?
Adding your own data/algorithms to open data creates value.
Start by evaluating the emerging open source de facto standards.
Choose an infrastructure that allows you to evaluate live streaming data in the context of
relevant historical data.
It’s All About the Data
GETTING STARTED…
Basic
Principles

Today:
Delta Airlines processes
5,000,000 business events per day
Pratt & Whitney jet engine:
5,000 sensors producing
10GB/s/per engine.
Formula 1 car sensors produce
about 1.2GB/s
and we need to predict the future…
AS THE SCOPE CHANGES, SO MUST THE SOLUTIONS

PRODUCTION ARCHITECTURE VS TRAINING ARCHITECTURE: CHALLENGE YOUR ASSUMPTIONS
In Production,

May Scale UP or DOWN.

The SourceFog Cloud
Data CenterGateway
SHOULD YOU MOVE THE COMPUTATION TO THE DATA, OR DATA TO THE PROCESSOR?

STREAMING
ANALYTICS
DESCRIPTIVE
DATA
PREDICTIVE
STREAMING ANALYTICS: MOVE THE PROCESS TO THE DATA
STREAMING ANALYTICS: STATISTICAL ANALYSIS OF DATA IN MOTION

3-TIER IOT ARCHITECTURE ENABLES DISTRIBUTED INTELLIGENCE & ANALYTICS
Sensors/
Devices
Train the Deep Learning Model
Data Center

Cloud

Cluster

Network
Compress & Run

The DL Model

DataSources
Store
Process/Transform
Observe
Key
Data Flows on the Edges, Queries Everywhere
Sampling vs Monitoring Everything…
STREAMING DATA ARCHITECTURE

PRIMUS INTER PARES
Cloud First!
Mobile First!
AI First!
Data First!!!

6 RECOMMENDATIONS
Deﬁne Your Application Requirements in Terms of Data 
Streaming? Plan for it

Process/Analyze As Close to the Source as Possible 
Move Intelligence To The Edge (Fog)

Parallelism in Algorithms? Exploit it with hardware 
Start With Open Source for Infrastructure

adrian@storminsights.com
Twitter @ajbowles
Skype ajbowles
If you would like to connect on LinkedIn,
please let me know that you that you
registered for the Smart Data webinar series.
NEXT WEEK…
October 18 Enterprise Analytics Online
 
1PM Eastern:

ModernAI From Machine Learning to Cognitive Computing
KEEP IN TOUCH
Upcoming SmartData Webinar Dates & Topics
Nov. 9 See Me Feel Me, Touch Me, Heal Me: 
The Rise of the Cognitive Interface 
Dec. 14 The Road to Autonomous Applications 
Jan. 11 AI At The Edge:

Pushing Intelligence to Fog Computing Nodes

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