# # 11

Getting Ahead of the Avalanche:Getting Ahead of the Avalanche:

Rick Hayes-RothRick Hayes-RothProfessor, Information Sciences, NPS, Monterey, CA.Professor, Information Sciences, NPS, Monterey, CA.

hayes-roth@nps.eduhayes-roth@nps.eduNovember, 2007

How everyone How everyone can benefit from a can benefit from a

near-infinite amount near-infinite amount of technologyof technology

# # 22

The Big, Big TrendsThe Big, Big Trends

Computers, storage, sensors, Computers, storage, sensors, communicationscommunications Performance doubling about every 18 monthsPerformance doubling about every 18 months Data volumes increasing exponentiallyData volumes increasing exponentially

# # 33

# # 44

Storage Capacity & InformationStorage Capacity & Information

How much storage will $200 buy?

# # 55

Networking: Sites & PerformanceNetworking: Sites & Performance

# # 66

Exponentials Destroy Status QuoExponentials Destroy Status Quo

# # 77

The Everyday Experience: The Everyday Experience: InfoglutInfoglut

# # 88

What’s Wrong with this Picture?What’s Wrong with this Picture?

Human capacity is Human capacity is constantconstant We can only learn & absorb a tiny fractionWe can only learn & absorb a tiny fraction

# # 99

In a world of infoglut, for bits to have value,

they must find their consumers

# # 1010

And their Consumers Must be And their Consumers Must be Ready, Willing & Able to ConsumeReady, Willing & Able to Consume

# # 1111

Plausible OutcomesPlausible Outcomes

1.1. A small number of channels for all contentA small number of channels for all content

2.2. A small number of devices for all contentA small number of devices for all content

3.3. A small number of suppliers for all contentA small number of suppliers for all content

4.4. A user-centric value delivery systemA user-centric value delivery system

Me-centric services:

Consumers delegate concerns to their agents, and these agents deliver customized value

# # 1212

Some Early SuccessesSome Early Successes

iTunesiTunes TiVoTiVo Yahoo! AlertsYahoo! Alerts RSS FeedsRSS Feeds NavigationNavigation ONGMAPONGMAP

# # 1313

ONGMAP Personalizes Info for YouONGMAP Personalizes Info for You

# # 1414

Two Emerging ArchitecturesTwo Emerging Architectures(1) The ubiquitous cloud

# # 1515

Two Emerging ArchitecturesTwo Emerging Architectures

(2) The virtual personal portal

IntelligentPersonalized

Filtering

Content On-Ramps Content Off-RampsSensors

Media

Enterprises

Educators

Governments

# # 1616

Personal Portals Specialized by RolePersonal Portals Specialized by Role

# # 1717

Two Basic Approaches: Two Basic Approaches: PullPull v. v. PushPush

Smart Smart PullPull - Each consumer seeks - Each consumer seeks

and acquires whatever and acquires whatever information it needs, information it needs, when and as neededwhen and as needed

Smart Smart PushPush- Each consumer tells Each consumer tells

network agents what network agents what conditions or events it conditions or events it would value detectingwould value detecting

# # 1818

PushPush MUCH BETTER than MUCH BETTER than PullPull

5 orders of magnitude more efficient5 orders of magnitude more efficient

99.999% less data for the operator to consider99.999% less data for the operator to consider

# # 1919

Condition Monitoring is KeyCondition Monitoring is Key Conditions of Interest (COIs) Conditions of Interest (COIs)

““Continuous queries” about important possible Continuous queries” about important possible eventsevents

Low-value data do not flowLow-value data do not flow

High-value events High-value events are detectedare detected Flow with priorityFlow with priority

# # 2020

Semantics and the “Semantics and the “OO” Word” Word Distinctions (meanings) that signal different Distinctions (meanings) that signal different

situations and justify different responsessituations and justify different responses

OntologiesOntologies are standard off-the-shelf sets are standard off-the-shelf sets

Collaborative business requires theseCollaborative business requires these

People are rolling their own (folksonomies)People are rolling their own (folksonomies)

This is what XML is This is what XML is really really aboutabout

# # 2121

Technology Shortfalls Technology Shortfalls (Opportunities)(Opportunities)

3.3. ““Cartridges” or “blades” for the most popular Cartridges” or “blades” for the most popular database productsdatabase products to support these vocabularies to support these vocabularies and expressions and expressions

1.1. VocabulariesVocabularies that match consumer concepts that match consumer concepts

2.2. An An expression languageexpression language for conditions of interest for conditions of interest

4.4. High-performance High-performance event detectorsevent detectors, especially , especially forecast space-time intersectionsforecast space-time intersections

5.5. Tools to Tools to audit information flowsaudit information flows and to determine and to determine specifically “why” particular alerts occurred or specifically “why” particular alerts occurred or “why not” when they didn’t“why not” when they didn’t

6.6. Tools to Tools to improveimprove the the information value chainsinformation value chains by by fixing bugs in the vocabularies, expressions or fixing bugs in the vocabularies, expressions or COIs. COIs.

# # 2222

ConclusionsConclusions

In a world of near-infinite data volumes, people will In a world of near-infinite data volumes, people will need machines to filter data intelligently for themneed machines to filter data intelligently for them This requires “semantic” understanding & dynamic contextThis requires “semantic” understanding & dynamic context

Centering on the user’s context produces enormous Centering on the user’s context produces enormous advantagesadvantages Up to 5 orders of magnitude improvementUp to 5 orders of magnitude improvement

Just as supply chain integration revolutionized the manufacturing and delivery Just as supply chain integration revolutionized the manufacturing and delivery of molecules, of molecules,

valued bit delivery chainsvalued bit delivery chains will revolutionize the information industrywill revolutionize the information industry

# # 2323

When bounded human capacity When bounded human capacity collides with unbounded technology, collides with unbounded technology,

great opportunities arisegreat opportunities arise

# # 2424

Backup Backup SlidesSlides

# # 2525

Bill

ing

Sea

rch

e

ngi

ne

Syn

ch-

roni

satio

n

Prin

ta

nyw

he

re

Pay

me

ntsu

ppo

rt

Usa

ge

a

nal

ysis

Use

r g

rpm

gt.

Lo

catio

np

roce

ssin

g

Te

lep

ho

ny

inte

gra

tion

Not

ifica

tion

ma

na

gem

en

t

Col

lab

ora

tion

Wor

kflo

w

Me

ssa

gin

g

Lo

cal

serv

ice

s

Cer

tific

atio

n

Adv

ert

isin

g

Info

me

dia

tion

Set

tlem

ents

Bro

keri

ng

Con

ten

tp

rese

nta

tion

Use

r co

nte

xtm

an

ag

eme

nt

Pus

hm

an

ag

eme

nt

Cha

rgin

g

Voi

cep

roce

ssin

g

Roa

min

gm

an

ag

eme

nt

Cu

sto

mer

m

anag

emen

t

Hig

h a

vail

abil

ity

pla

tfo

rm

Sys

tem

s an

d a

pp

lica

tio

ns

man

ag

emen

t

Ser

vice

cre

atio

n &

dep

loym

ent

Portal usersAny device (PDA, phone, PC, STB)

Content serviceproviders

Portal contact pointAny network (mobile, fixed) + any gateway (HTTP, WAP, Voice)

Content service feed

Service aggregation

Content service management

Shared services

Other data

Userdata

Sec

uri

ty Pass-throughaccess

PIM

Cus

to-

mis

atio

n

etc

...

UM

Con

fere

ncin

g

Access management

Bud

dy

list

Lo

cal b

ud

die

s

Hom

e p

ag

e

Portal ArchitecturePortal Architecture

# # 2626

Model-based Communication Networks:Model-based Communication Networks:Seeking a “mind meld” (shared situation models) Seeking a “mind meld” (shared situation models) under resource constraints under resource constraintsChallengesChallenges

Distributed entities have different concerns and Distributed entities have different concerns and perspectivesperspectives

Dynamic situations evolve rapidly Dynamic situations evolve rapidly Data updates glut channels and processorsData updates glut channels and processors Backlogs build and processing entities thrashBacklogs build and processing entities thrash

MCN remedy: optimize information flowsMCN remedy: optimize information flows Each node lets others know its concernsEach node lets others know its concerns Every node maintains dynamic modelsEvery node maintains dynamic models

Of itselfOf itself Of othersOf others

A node X informs a node Y when X detects an A node X informs a node Y when X detects an event that affects Yevent that affects Y

# # 2727

Where are the Opportunities?Where are the Opportunities?

DoD is building a Global Information Grid for DoD is building a Global Information Grid for

“Information Superiority”“Information Superiority” They will spend > $10B over the next decadeThey will spend > $10B over the next decade

DHS, FEMA, states and municipalitiesDHS, FEMA, states and municipalities

Content providers seeking new channelsContent providers seeking new channels

Technology providers seeking new devicesTechnology providers seeking new devices

Enterprises hunting for effective servicesEnterprises hunting for effective services

# # 2828

The Basic Ideas for DoD & othersThe Basic Ideas for DoD & others1.1. Optimize info chains (bit flows) for each operatorOptimize info chains (bit flows) for each operator

Get the high-value bits to operators quickly (Get the high-value bits to operators quickly (VIRTVIRT)) Reduce the number of low-value bits they receiveReduce the number of low-value bits they receive

2.2. Measure the productivity of information processesMeasure the productivity of information processes Compare “smart pull” to “smart push”Compare “smart pull” to “smart push” Show 5 orders of magnitude advantage for “smart push”Show 5 orders of magnitude advantage for “smart push”

3.3. Shift efforts in DoD to VIRT and Smart PushShift efforts in DoD to VIRT and Smart Push Value derives from operator plans and contextsValue derives from operator plans and contexts Filters use Filters use COIsCOIs to optimize flow: significant “news” to optimize flow: significant “news” This filtering dictates priorities for semantic mark-upsThis filtering dictates priorities for semantic mark-ups

4.4. Implement information superiority incrementallyImplement information superiority incrementally One operator “thread” at-a-timeOne operator “thread” at-a-time Delivering a few, high-value bits, swiftlyDelivering a few, high-value bits, swiftly Continually improving Continually improving COIs COIs & enabling semantics& enabling semantics

VIRT = Valuable Information at the Right TimeVIRT = Valuable Information at the Right TimeCOI = Condition Of InterestCOI = Condition Of Interest

# # 2929

Condition Monitoring is KeyCondition Monitoring is Key

Conditions of Interest (COIs) Conditions of Interest (COIs) Computable expressions (“continuous queries”)Computable expressions (“continuous queries”) Describe critical assumptions (like CCIRs)Describe critical assumptions (like CCIRs) Depend on operator’s evolving contextDepend on operator’s evolving context

Usually reflect phase of a mission & current statusUsually reflect phase of a mission & current status

High-value events are detectedHigh-value events are detected Data describing the event match the COIData describing the event match the COI The event is “news”The event is “news” The COI assures the event is still “relevant”The COI assures the event is still “relevant” Bits reporting the event flow with priorityBits reporting the event flow with priority

Low-value data do not flowLow-value data do not flow Generally “relevant” data not matching a COIGenerally “relevant” data not matching a COI Repeated and redundant data, not newsworthyRepeated and redundant data, not newsworthy

# # 3030

Numerical Analysis of ExampleNumerical Analysis of Example

Theater & Information SourcesTheater & Information Sources Area of interest is 200 km X 200 kmArea of interest is 200 km X 200 km Lat-long mesh 1 km x 1 km => 40K grid pointsLat-long mesh 1 km x 1 km => 40K grid points Altitude ranges to 6km, 500m mesh => 13 planesAltitude ranges to 6km, 500m mesh => 13 planes Time span = 4.5hr, gridded @ 30min => 10 slicesTime span = 4.5hr, gridded @ 30min => 10 slices 10 variables of interest10 variables of interest

50M 50M apparently relevantapparently relevant data values data values Data refreshed on average every 30 minData refreshed on average every 30 min

Pilot’s strategy: Reexamination every 10 minPilot’s strategy: Reexamination every 10 min 27 reexaminations over the 4.5 hr mission27 reexaminations over the 4.5 hr mission

Conservative assumptionsConservative assumptions 90% automatically dropped as “obviously” not “relevant”90% automatically dropped as “obviously” not “relevant” 90% automatically dropped as “obviously” not “significant”90% automatically dropped as “obviously” not “significant”

Theory 1 gets just 1% of Theory 1 gets just 1% of apparently relevantapparently relevant data data

# # 3131

Comparing Process EfficienciesComparing Process Efficiencies

Theory 1 (Smart Theory 1 (Smart PullPull))Every 10 minutes, 1% of 50M data values receivedEvery 10 minutes, 1% of 50M data values received I.e., 500K relevant & significant data valuesI.e., 500K relevant & significant data valuesEquivalently, 50K items per minute, or 800/secEquivalently, 50K items per minute, or 800/secAs a consequence, the pilot “skims” the glutAs a consequence, the pilot “skims” the glut

Theory 2 (Smart Theory 2 (Smart PushPush))Every 10 minutes, 0 or a small number of Every 10 minutes, 0 or a small number of

significant events will occursignificant events will occurAs a consequence, the pilot has required As a consequence, the pilot has required

cognitive resources to process any eventcognitive resources to process any event

Theory 2 : Theory 1Theory 2 : Theory 1 ((Push Push >>>> PullPull)) 99.999% less data for the operator to 99.999% less data for the operator to

considerconsider 5 orders of magnitude more efficient5 orders of magnitude more efficient

# # 3232

Route Michigan

Route Texas

Route Iow

a

Route V

irginia

PL GREEN

PL RED

PL B

LUE

OBJ

SQUAD RELEASE POINT

Ingress

OPEN FIELD

1

3

2

(+)

?

?

PL O

RA

NG

E

LEGEND

One Story Bldg

Two Story Bldg

Palm Grove

USMC-VIRT Scenario: USMC-VIRT Scenario: High Value Target RaidHigh Value Target Raid

• Platoon Sized Force• Each squad deploys to their positions

Lin

e o

f D

ep

art

ure

(L

OD

)

# # 3333

Conditions of InterestConditions of Interest

1-1. Notify me if my target location is no longer valid.

1-1.a. The distance we are concerned with is a variable. For this instance, we say +/- 100m

1-2-1. Tell me if there are any of friendly organic forces injured to the extent that it impacts mission accomplishment.1-2-1.a. Variable here is the definition of what hinders the mission. Examples include mobility, life threatening injuries, and combat effectiveness issues.

1-2-2. Same as 1-1. Variable here is the distance of the squad from it's expected location; We are concerned with +/- 50m.

1-2-3. Tell me if any organic blue force weapons become inoperable.1-2-3a. By inoperable, we mean incapable of sending a round downrange. Does not take into account multiple weapon systems (203 grenade launcher).

1-3. Notify me if I’m about to lose comms. Approaching my Approaching my communication communication device's thresholddevice's threshold

Still within my Still within my communication's communication's thresholdthreshold

# non-mission- # non-mission- capable weapon capable weapon systems exceeds systems exceeds Go / No-Go thresholdGo / No-Go threshold

Weapons are Weapons are mission capablemission capable

Squads' locations Squads' locations are not as planned / are not as planned / expectedexpected

Squads’ locations Squads’ locations are accurate are accurate

Organic blue force Organic blue force casualties exceed casualties exceed Go-No-Go thresholdGo-No-Go threshold

All organic blue All organic blue forces are forces are mission mission capablecapable

Actual target Actual target location not as location not as planned / expectedplanned / expected

Target location Target location knownknown

NegatedAssumptions

PlanAssumptions

# # 3434

USMC-VIRT Semantic Object Model v.1 USMC-VIRT Semantic Object Model v.1 Concepts of…Concepts of…

# # 3535

In_1.0   //Target Location has changed//

1 Current target location not as planned

2 Current target location not as expected

In_2.0   //Health & status of organic, mission assigned, friendly forces//

1 Blue organic force member is seriously injured

2 # of organic blue forces injured exceeds mission Go-No-Go criteria

In_3.0  //Location of msn essential organic blue force maneuver elements, in this case a 12 man squad.//

1 Current position of Squad(n) is not as planned

[Mission]:Msn_#, Msn_Type-HVT[Phase]:= Ingress, [Target]:Tgt_ID, [Location]: Location_ID, Coordinates ≠ Coordinates Planned

[Mission]:Msn_#, Msn_Type-HVT[Phase]:= Ingress, [Target]:Tgt_ID, [Location]: Location_ID, Coordinates ≠ Coordinates Expected

[Mission]:Msn_#, Msn_Type-HVT[Phase]:= Ingress, [Rifleman]: Rifleman_ID and/or [Squad]: Sqd_Ldr and /or [Fire_Team]: FireTeam_Ldr,

Health_N_Status = Serious Injury

[Mission]:Msn_#, Msn_Type-HVT[Phase]:= Ingress, [Rifleman]: Rifleman_ID and/or [Squad]: Sqd_Ldr and /or [Fire_Team]: FireTeam_Ldr,

Health_N_Status SUM Qty Serious Injury ≥ Go_No_Go_Criteria {abort}

[Mission]:Msn_#, Msn_Type-HVT[Phase]:= Ingress, [Squad]:Squad_ID, [Location]: Location_ID, Coordinates ≠ Coordinates Planned

Example Information Requirements andExample Information Requirements and Conditions of Interest (COIs)Conditions of Interest (COIs)