Effec%ve	Data	Science	in	
Aerospace	Applica%ons
Geoffrey	Clark	|	2016-06-25	|	GR-81-RHO	
copyright	Lucidata	2016
Lucidata	Informa%cs?
•  Geoffrey	Clark,	Principal	and	CEO	
– Data	Modeler:	logical	and	ra%onal	DW	
– Solu%on	Architect:	analy%cs	
– Strategy	:	risks,	what-if,	sims,	wargames	
•  Associates	at	Lucidata	
– Senior	Data	Modeler,	requirements	expert	
– Advanced	Analy%cs	experts	
– Senior	GIS	analysts	and	FOSS4G	development	
copyright	Lucidata	2015
Dual	Challenges	of	Analy%cs
Market
TMS
CRM
Analytics Solution
Standard	BI	
Formal,	Trusted,		
Shared,	Public	
Data	Explora0on	
Ad-hoc,	Agile	
Experimental	
indivi
duals	
Power		
Users	
Clean,	Load		
&	Join	Data	
Form	New		
BI	Ques%ons	
Analyze	
Data	
Find	New	Data	
Analytics User Segments
FIN
Your	
Data	
Knowledge,	
Defini%ons,	
Hierarchies,	
Rela%onships		
Ad-Hoc	Data	
Playground	
Standard,	
Industry	
Data	Industry
Reference
Execu%ves,		
Managers	
Analysts,		
Knowledge	
Workers	
Technical	Challenge,	Data	Integra%on
 Cultural	Challenge,	User	Integra%on
How	Unique				is	your	data?*
*	is	that	a	source	of	advantage,	or	confusion?
En%ty-Rela%onship	(ER)	Modeling
This	is	an	example	En.ty-Rela.onship	Diagram	(ERD),	which	explains	how	to	read	the	
nota.on.		It	is	also	an	example	of	a	highly	abstract	model,	which	is	"data	driven",	
meaning	that	new	Thing	Types,	new	Things	and	new	Thing	Rela.onships	may	be	easily	
added	to	a	database	based	on	this	design	without	needing	to	change	the	database	
structures.		This	prac.ce	was	common	in	early	databases,	built	for	on-line	transac.on	
processing	(OLTP).		This	stands	in	contrast	to	the	concrete	business	seman.cs	
implemented	as	part	of	Dimensional	Data	Modeling	efforts,	suppor.ng	on-line	analy.cal	
processing	(OLAP).	

copyright	Lucidata	2016
Simulate	
Op,mize	
Forecast	
Derive	(data	mining)	
Summarize	&	Describe	(sta%s%cs)	
Visualize	
Join	&	Filter	(data	warehousing)	
Measure	&	Store	(source	systems)	
En,,es	&	Rela,onships	(data	modeling)	
BI	EA	STATS	OR	
Analy,cs	
Quality	
AMATEUR	 PROFESSIONAL	
The	Progression	of	Analy%cs
copyright	Lucidata	2015
 5
...	rest	upon	
this	founda%on
These	types	
of	models	...
Big	Data	History,	via	Google	Trends
Source:	heps://www.google.com/trends/
What	research	did	we	do?
	"p1sk"	-	project	#1,	surrogate	keys.		
	airport_id	=	2369		
	
"p2nk"	-	project	#2,	natural	keys.	
	airport_iata_cd	=	'SEA’		
	airport_from_dt	=	'2011-07-01’	
	
"p3uu"	-	project	#3,	universally	unique	iden%fier	(UUID),	
	airport_uuid	=	7cbcc311-18c9-4497-99c9-62c42fd1ef2b	
		
"p4hk"	-	project	#4,	hash	key.			
	airport_key	=	0ed805d25fc96166a5895857a252de4b	
What	has	the	Big	Data	innova.on	cycle	taught	us	about	data	design?
Original	T100	“Green	Book”
Data	Source:	T100	“Green	Book”
Effective Data Science in Aerospace Applications
Importance	of	Reference	Data
copyright	Lucidata	2015
If	you	have	a	flood	of	.mestamps,	beNer	know	what	.me	zone	they	represent.		
And,	don’t	be	like	the	Mars	Climate	Orbiter,	get	your	units	of	measure	right!
Nbr
 Full Number
 ISO 31
 Description
 Comments
10^0
 1
C62
 one (or unit)
 "EA" for each from ANSI
10^1
 10
 ten
10^2
 100
CEN
 one hundred
10^3
 1000
MIL
 one thousand
10^4
 10,000
 ten thousand
10^5
 100,000
 one hundred thousand
10^6
 1,000,000
MIO
 one million
 Somewhat confusing
10^7
 10,000,000
 ten million
10^8
 100,000,000
 one hundred million
10^9
 1,000,000,000
MLD
 one milliard in EU one billion (US)
 Horribly confusing! 
10^12
 1,000,000,000,000
BIL
 one billion in EU (one trillion in US)
 Horribly confusing! 
10^18
 1,000,000,000,000,000,000
TRL
 one trillion (EUR)
 Horribly confusing! 
ISO	31	-	Units	of	Measure
More	work	remains	to	beeer	coordinate	the	measurement	
ac%vi%es	of	humankind,	the	future	will	appreciate	it!
project	#1,	surrogate	key
project	#2,	natural	keys	
project	#3,	universally	unique	id	
project	#4,	hash	key
Addi%onal	Factors	to	Consider
Source:	Dan	Linstedt	on	learndatavault.com.
Addi%onal	Research	Plans
•  How	does	data	design	change	when	using	
distributed	database	technology?	aliases	-	
massive	parallel	processing	(MPP),	“Sharded”	
•  How	does	data	design	change	when	using	
columnar	database	technology?	
•  How	does	data	design	change	when	using	graph	
database	technology?		aliases	–	“RDF”,	“triple	
store”.	
•  How	does	performance	change	with	different	
disk	op%ons	–	HDD,	SSD,	SSD	RAIDS,	etc.
Effective Data Science in Aerospace Applications
Effective Data Science in Aerospace Applications
Effective Data Science in Aerospace Applications
Effective Data Science in Aerospace Applications
Informa%on	Density
Charles	Joseph	Minard’s	Carte	Figura.ve	from	1869,	depic%ng	Napolean’s	1812	invasion	of	
Russia,	and	aqermath	in	seven	dimensions	(la%tude,	longitude,	%me,	temperature,	army	
group,	and	military	phase).		“The	best	sta.s.cal	drawing	ever	made”		--	Edward	Tuqe
Source:	heps://en.wikipedia.org/wiki/File:Minard.png

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Effective Data Science in Aerospace Applications