HathiTrust Research Center Secure Commons

HathiTrust
Research
Center

Secure
Commons

Beth
Plale

Co-‐Director,
HathiTrust
Research
Center

Professor
of
Informa:cs

Director,
Data
To
Insight
Center

Indiana
University

@bplale
University
of
Toronto,
25
June
2015

HathiTrust
is...

•  A
trusted
digital
preserva:on
service
enabling

the
broadest
possible
access
worldwide.

•  An
organiza:on
with
over
100
research

libraries
making
up
its
membership.

•  A
distributed
set
of
services
operated
by

diﬀerent
members
(California
Digital
Library,

Illinois,
Indiana,
Michigan).

•  A
range
of
programs
enabled
by
the
large

scale
collec:on
of
digi:zed
materials.

Mission

To
contribute
to
research,
scholarship,
and
the

common
good
by
collabora:vely
collec:ng,
organizing,

preserving,
communica:ng,
and
sharing
the
record
of

human
knowledge.

…building
comprehensive
collec:ons
and
infrastructure

co-‐owned
and
managed
by
partners.

…infrastructure
for
digital
content
of
value
to
scholars
and

researchers

…enabling
access
by
users
with
print
disabili:es.

…suppor:ng
research
with
the
collec:ons.

…s:mula:ng
shared
collec:on
storage
strategies.

Collec:ons

University
of
Toronto,
25
June
2015

Preserva:on
with
Access

•  Preserva:on

– TRAC-‐cer:ﬁed

•  Discovery

– Bibliographic
and
full-‐text
search
of
all
materials

•  Access
and
Use

– Full
text
search
(all
users)

– Public
domain
and
open
access
works
(all
users)

– Collec:ons
and
APIs
(all
users)

– Lawful
uses
of
in-‐copyright
works
(members)

HathiTrust
in
April
2015

•  13.3
million
total
items

– 6.8
million
book
:tles

– 355,000
serial
:tles

– 612,000
US
federal
government
documents

– 5.03
million
items
open
(public
domain
&
CC-‐
licenses)

The
collec:on
primarily
includes
published

materials
in
bound
form,
digi:zed
from
library

collec:ons.

HathiTrust
Research

Center
Secure
Commons

University
of
Toronto,
25
June
2015

Mission
of
the
HT
Research
Center

•  Research
arm
of
HathiTrust

•  Established:

July,
2011

•  Collabora:ve
center:

Indiana
University
&

University
of
Illinois

•  Mission:

Enable
researchers
world-‐wide
to

accomplish
tera-‐scale
text
data-‐mining
and

analysis

•  Major
eﬀort
to
date:

–  Build
secure
and
trusted
environment
surrounding

the
sensi:ve
text
and
image
data:
Trust
Ring

–  Make
the
data
more
useable
and
accessible
to

researcher

Secure
Commons
“Trust
Ring”

•  Logical
ring
within
which
exist
trusted

services
and
computers
that
protect
and

provide
access
to
the
sensi:ve
(copyright)

data

•  Computa:on
moves
to
the
data
not
vice
versa

•  Computa:on
carried
out
in
the
trust
ring

IU
UIUC

Raw
copyright
data
on
1)

ﬁle
system
and
in
archive
in

pairtree
form,
2)
chunked
form
for
parallel
processing

and
2)
in
full
text
Solr
index

Knowledge
product
services

Data
Capsule
VMs

Services
and
Tools:

data
discovery,
extrac:on,
cleaning,

mining/analysis,
visualiza:on

Knowledge
products
(public):

workset,

ontology,
feature
sets

HTRC
Portal
(for
authen:ca:on)

Knowledge
products

(private):
personal
worksets

External
data

cache

DH
CS
NLP
R
.
.
.

Data
management
services

Secure
Commons
Services
Stack

Trust
Ring
gains
core
of
its
trustworthiness
from

the
highly
secure
and
heavily
managed
storage

and
compute
environment
at
Indiana
University

Researcher
Interac:on

Interac:on
with
HTRC
is
through
one
of
three

op:ons:

1.  Services
and
tools
for
data
extrac:on,
data

cleaning,
data
analysis
and
results

visualiza:on.

Self
service,
browser-‐based.

2.  Check
out
a
Data
Capsule
VM.
Researcher

checks
out
and
conﬁgures
for
their
use

(currently
for
the
technology
savvy)

3.  Direct
engagement
with
HTRC
staﬀ

HTRC
Portal:

h.ps://sharc.hathitrust.org/

Self
service
portal
for
services
and

tools

Data

Extrac:on

Data

Cleaning

Data

Analysis

Visualiza:on

HT

DLib

Result:

stored
to

workset
Input
Parameters
(JSON)

Task
output
(JSON)

Overall
Result

•  Graphs

•  Raw
data

•  Structured
data

•  etc

Tasks
can
be
programs
wrifen
in
any
language

•  Python,
R,
Java,
C#,
…

Current
solu:on
of
SEASR
workﬂows
being
deprecated;

New
solu:on:
four
stage
framework
for
researcher
to
plug

together
desired
tasks.

New
tools
in
each
task
come
from
HTRC
community,
open

source,
etc.

Data
Capsule

Founda:ons
of
HT
Data
Capsule:

K.
Borders,
E.
V.
Weele,
B.
Lau,
and
A.
Prakash.

Protec:ng
conﬁden:al
data
on
personal
computers

with
storage
capsules.

18th
USENIX
Security
Symposium,
pp
367–382.
USENIX
Associa:on,
2009.

HathiTrust
Data
Capsule
concept

•  Researcher
“checks
out”
a
virtual
machine

(VM)

•  VM
runs
in
the
Trust
Ring

•  Researcher
owns
their
VM
through
weeks/
months
of
analysis

•  Geong
stuﬀ
into
VM
is
easy,
but
there
is
a

controlled
and
audited
process
for
geong

results
out
of
the
VM

Data
Capsule
with
i-‐Python
installed

Mode
switch
protec:on:

maintenance
mode

Data
Capsule
Data
Capsule

User
traﬃc
from

desktop
allowed

Arbitrary
network

download
allowed

Arbitrary
network

upload
allowed

during
maintenance
mode,
researcher

installs
new
soqware
and
loads
data

into
capsule

HTRC
raw
data

sources

Mode
switch
protec:on:

secure
mode

Data
Capsule
Data
Capsule

User
traﬃc
from

desktop
allowed

Arbitrary
network

download
not

allowed

Arbitrary

network

upload
not

allowed

Researcher
switches

to
secure
mode
when

ready
to
run
her
tools

HTRC
raw
data

sources

Results
:

researcher
tools

must
write
results

to
special

directory;
these

are
reviewed

before
release

Threat
Model

•  User
is
trustworthy

•  Virtual
machine
(VM)
manager
and
the
host
it

runs
on
are
also
trusted.

•  VM
is
NOT
trusted.
We
assume
the
possibility

of
malware
being
installed
as
well
as
other

remotely
ini:ated
afacks
on
the
VM,
which

are
undetectable
to
the
user.

HTRC
Data
Capsules

See
Data
Capsule
Tutorial
for
step-‐by-‐step
instruc:ons:

Go
to
wiki:

hfps://wiki.htrc.illinois.edu

Navigate
to:

Community
>
HTRC
Data
Capsule
>
HTRC
Data
Capsule
Tutorial

Direct
engagement

with
HTRC
staﬀ

University
of
Toronto,
25
June
2015

HTRC
Advanced
Collabora:ve
Support

Awards
for
HTRC
developer
Hme

1st
round
awards:

•  Detec:ng
Literary
Plagiarisms:
The
Case
of
Oliver
Goldsmith

•  Taxonomizing
the
Texts:
Towards
Cultural-‐Scale
Models
of
Full
Text

•  The
Trace
of
Theory

•  Tracking
technology
diﬀusion
thru
:me
using
HT
Corpus

Coming:
call
for
2nd
round
Proposals.

h?p://hathitrust.org/htrc
for
details

…
or
Dr.
Miao
Chen,
miaochen@indiana.edu

Advanced
CollaboraHve
Support

•  Pairs
HT
ins:tu:on
researchers
with

expert
staff
for
an
extended
period
during

which
they
work
together
to
address
a

par:cularly
vexing
issue
(e.g.,
efficient

paralleliza:on
and
op:miza:on
of
a

machine
learning
algorithm)

•  20
hours/week
available:
example:

at
any

one
:me
4
ac:ve
projects,
each
receiving

5
hours
a
week
for
up
to
2
months.

•  Resourced
at
1.25
FTE

•  Staffed
by
HTRC
Staff
who
have
signed

the
staff
agreement

26

HTRC*Advisory*Board*
7ve*
nt*
earch*
*Students*
D*Students*
ystems*
nistrator*
*FTE)*
Advanced*Collabora7ve*
Support*(coordinated*by*
M.*Chen)*
Research*Programmer*
(.5*FTE)*
Computa7onal*Research*
Liaison*
(.5*FTE)*
Asst*Dir*Outreach*&*
Educa7on*(M.*Chen)*
(1*year*at*.25*FTE)**
Scholarly*Commons*
Dig*Humani7es*Specialist*
(1.0*FTE)*
CLIR*Postdoctoral*
Research*Associate*
(2*years*at*1.0*FTE)*
Digital*Research*
Librarian*support*
(.2*FTE)*
Scholars*Commons*
Support*
(.5*FTE)*
LIS*MS*Students*
UI*Managing*Director*
(.11*FTE)*

Scholarly
Commons

User
Support
Services

•  Develop
training
materials

•  Educa:onal
workshops

•  Tool
and
workset
support

•  Collaborate
with
librarians
and

DH
centers
at
HT
ins:tu:ons

•  Assist
researchers
in
HTRC
text

data
mining
research
projects

•  Collabora:on:
University

Libraries,
Illinois
and
Indiana

Knowledge
Products:

ongoing
projects
to

improve

University
of
Toronto,
25
June
2015

Worksets

•  The
ability
to
slice
through
a
massive
corpus

constructed
from
many
diﬀerent
library

collecHons,
and
out
of
that
to
construct
the

precise
workset
required
for
a
parHcular

scholarly
invesHgaHon,
is
an
example
of
the

“game
changing”
potenHal
of
the
HathiTrust...

Dimensions
of
Workset
Crea:on
(Illustra:ve)

My
workset
should
contain
(inspired
by
2012
UnCamp):

•  Volumes
pertaining
to
Japan
/
in
Japanese

•  All
volumes
relevant
to
the
study
of
Francis
Bacon

•  Music
scores
or
nota:on
extracted
from
HT
volumes

•  Images
of
Victorian
England
extracted
from
HT
vols.

•  Volumes
in
HT
similar
to
TCP-‐ECCO
novels

•  19th
c.
English-‐language
novels
by
female
authors

•  Representa:ve
sample
(by
pub
date
&
genre)
of

French
language
items
in
HT

What
is
Workset?
#1

•  A
workset
is
an
aggrega:on
of
materials

brought
together
for
the
purpose
of
discovery

and
analysis.

What
is
a
Workset?
#2

•  Worksets
are
conceptual
and
must
be

expressible
in
a
variety
of
ways

•  Need
to
facilitate
inclusion
of
resources

beyond
HathiTrust

•  Need
to
facilitate
the
inclusion
of

resources
at
many
diﬀerent
levels
of

granularity
beyond
the
book

What
is
Workset
#3

•  Worksets
encapsulate
the
speciﬁc
materials

that
underwent
analysis

•  Need
to
capture
provenance
informa:on

•  Possible
recording
of
parameters

What
is
a
Workset?
#4

•  Worksets
should
be
able
to
spawn

descendants
but
otherwise
immutable

rdf:type

Draq
Workset
Data
Model
V.
0.2

cnt:content

rdf:type

htrc:isGatheredInto

dcterms:created

dcterms:extent

rdf:type

rdf:type

foaf:accountName

dc:creator

rdf:type

:_workset1

htrc:Collec:on

dc::tle

:_desc1

dcterms:abstract

cnt:ContentAsText
:_curator1

foaf:Agent

“rkfritz”^^xsd:string

9^^xsd:integer

“2013-‐11-‐11T15:55:48-‐5:00Z”^^xsd:dateTime

dul1.ark:/13960/
t77s8cw40

htrc:BibliographicResource

“Agrippa”^^xsd:string

“Agrippa
and
Mexia”^^xsd:string

rdf:about

hfp://catalog.hathitrust.org/
Record/010944168
htrc:BibliographicRecord

Page-‐level
Sta:s:cs

Extrac:on
Over
HathiTrust

Corpus
for
Tech
Terms

Acknowledgements:

collabora:on
with
Michelle

Alexopolous,
University
of

Toronto.

Extrac:on
and

analysis
by
Guangchen
Ruan,
CS

PhD
student
at
Indiana

University

University
of
Toronto,
25
June
2015

Mo:va:on
and
Problem

•  Given
a
list
of
terms
(n-‐grams),
extract
page-‐
level
sta:s:cs
for
each

term

For
instance,
seek
frequency
of
appearance
of
term

“diesel
engine”
at
volume
level
and
page
level:

in

which
volumes,
and
on
which
pages
with
frequency

count
per
page

•  We
undertook
to
compare
the
accuracy
of
two

approaches:

one
that
extracts
terms
from
Solr

index,
and
other
that
extracts
terms
using
a

single-‐pass
processing
framework
we

developed
to
work
directly
on
the
raw
data

•  Sample
output
for
page-‐level
stats
for
“diesel

engine”

{

"tech_name":
"diesel
engine",

"volumes":
[

{

"volumeID":
"uc1.b4125277",

"pageLevelStats":
[

{

"pageSeq":
"146",

"pageLabel":
"136",

"count":
3},

{

"pageSeq":
"649",

"pageLabel":
"639",

"count":
2}]
},
…
]}

Approach
one:

page
level
index
using
Solr

§  Build
page-‐level
index
from
raw
text.
Obtain

page-‐level
stats
through
Solr
query

§  Computa:on
and
:me
cost
high
to
build
page-‐
level
index
for
each
tech
term,
so
build
single

page-‐level
index
for
group
of
words
with

similar
seman:cs

§  e.g.,
“diesel
engine”,
“diesel
motor”,
“diesel

powered
engine”

Approach
two:

single-‐pass
processing

distributed
compu:ng
framework

§  For
each
volume,
directly
scan
tcontent
of
each

page
to
check
match
using
regular
expression

§  Divide
volumes
and
computa:on
across

mul:ple
machines
to
speed
up

§  Not
computa:on
sensi:ve
to
the
#
of
tech

terms
being
searched
so
can
provide
page-‐level

stats
for
each
tech
term
rather
than
one
for
a

group

•  We
compare
results
of
approaches
under
8
tech
term

groups
or
equivalently
57
tech
terms

•  Overall,
results
from
two
approaches
have
over
95%

consistency

•  For
inconsistent
por:on,
we
manually
inspect
the
raw
text

content
to
verify
the
ground
truth

•  Evalua:on
shows
that
single-‐pass
processing
approach
is

more
accurate
(less
false
posi:ves
and
nega:ves)
than
Solr

approach

Quality
evalua:on:
Solr-‐based
vs.
Single-‐pass

processing

Tech
term
groups

Tech
term
group
Tech
terms

diesel
engine

(6
terms)

“diesel
engine”,
“diesel
engines”,
“diesel
motor”,
“diesel

motors”,
“diesel
powered
engine”,
“diesel
powered
engines”

gas
engine

(20
terms)

“gas
engine”,
“gas
engines”,
“gas
motor”,
“gas
motors”,
“gas

powered
engine”,
“gas
powered
engines”,
“gas
powered
motor”,

“gas
powered
motors”,
“gasoline
engine”,
“gasoline
engines”,

“gasoline
motor”,
“gasoline
motors”,
“gasoline
powered
engine”,

“gasoline
powered
engines”,
“gasoline
powered
motor”,

“gasoline
powered
motors”,
“gasoline-‐powered
engine”,

“gasoline-‐powered
engines”,
“gas-‐powered
engines”

“gas-‐powered
motors”

Tech
term
group

Tech
terms

internal-‐combusGon-‐
engine

(4
terms)

“internal
combus:on
engine”,
“internal
combus:on
engines”,

“internal
combus:on
motor”,
“internal
combus:on
motors”

steam
boat

(2
terms)

“steam
boat”,
“steam
boats”

steam
engine

(12
terms)

“Corliss
engine”,
“Corliss
engines”,
“Corliss
steam
engine”,

“Corliss
steam
engines”,
“Newcomen
steam
engine”,

“Newcomen
steam
engines”,
“steam
engine”,
“steam
engines”,

“waf
engine”,
“waf
engines”,
“waf
steam
engine”,
“waf
steam

engines”

steam
locomoGve

(4
terms)

“steam
locomo:ve”,
“steam
locomo:ves”,
“steam
train”,
“steam

trains”

steam
ship

(2
terms)

“steam
ship”,
“steam
ships”

Telegraph

(7
terms)

“cable
gram”,
“cablegram”,
“cable
grams”,
“telegram”,

“telegrams”,
“telegraph”,
“telegraphs”

Tech
term
groups

Volume-‐level
and
page
level
comparison

Tech
term
group

Volume
level
comparison

Page
level
comparison
in
common

volume
set

(vols
appearing

in
s1
but
not

s2)
/
(total
#
of

volumes
in
s1)

(vols
appearing

in
s2
but
not
s1)
/

(total
#
of

volumes
in
s2)

(#
of
page

records
in
s1
but

not
s2
)
/
(total
#

of
page
records

in
s1
)

(#
of
page

records
in
s2
but

not
s1
)
/
(total
#

of
page
records

in
s2
)

diesel
engine

(6
terms)

472/19,869

(2.38
%)

98/19,495

(0.50%)

2,503/84,142

(2.97
%)

1,986/83,625

(2.37
%)

gas
engine

(20
terms)

1,132/45,321

(2.50%)

846/45,035

(1.88%)

7,065/187,735

(3.76%)

5,831/186,501

(3.12%)

internal-‐combusGon-‐
engine

(4
terms)

2,418/21,122

(11.4%)

80/18,784

(0.43%)

8,480/75,567

(11.2%)

3,994/71,081

(5.61%)

steam
boat

(2
terms)

4,209/176,652

(2.38%)

1,166/173,609

(0.64%)

25,808/794,832

(3.25%)

33,194/802,218

(4.14%)

**
s1
refers
to
single
pass
processing,
s2
refers
to
Solr-‐based
approach

Volume-‐level
and
page
level
comparison

Tech
term
group

Volume
level
comparison

Page
level
comparison
in
common

volume
set

(Vols
appearing

in
s1
but
not

s2)
/
(total
#
of

volumes
in
s1)

(Vols
appearing
in

s2
but
not
s1)
/

(total
#
of

volumes
in
s2)

(#
of
page
records

in
s1
but
not
s2
)
/

(total
#
of
page

records
in
s1
)

(#
of
page
records

in
s2
but
not
s1
)
/

(total
#
of
page

records
in
s2
)

steam
engine

(12
terms)

9,259/127,385

(7.27%)

828/118,954

(0.70%)

31,475/476,992

(6.60%)

19,145/464,662

(4.12%)

steam
locomoGve

(4
terms)

707/13,539

(5.22%)

169/13001

(1.30%)

2,880/36,294

(7.93%)

1,752/35,166

(4.98%)

steam
ship

(2
terms)

1,876/134,220

(1.39%)

920/133,264

(0.69%)

12,024/573,453

(2.09%)

15,141/576,570

(2.63%)

Telegraph

(7
terms)

67,293/

361,483

(18.6%)

375/294,565

(0.13%)

787,481/2,140,28
4
(36.7%)

164,601/1,517,40
4
(10.8%)

Analysis
of
Solr
false
posi:ve/nega:ve

•  False
posi:ve

§  Example
one:
false
posi:ve
match
for
“diesel
engine”

“17
Engines
and
Turbines
(Excludes
aircraq
and
rocket
engines;

automo:ve
engines,
except
diesel;
engine
generator
sets;
and

locomo:ves.)”

§  Example
two:
false
posi:ve
match
for
“diesel
motor”

“Fossil
fuel
consump:on
(gasoline,
diesel,
motor
oil)
would
decrease

as
a
result
of
this
alterna:ve.”

§  Cause
analysis:
Solr
builds
page-‐level
index
by
Lucene

tokenizaHon
which
removes
non-‐word
character.
One-‐pass

processing
can
correctly
handle
such
cases
as
it
matches
by

regular
expression
against
raw
text

Cause
analysis
of
Solr’s
false
posi:ve/nega:ve

(Cont.)

•  False
nega:ve

§  Example
one:
false
nega:ve
match
for
“diesel
engine”

“Steam
boilers
and
equipment,
steam
and
gas
turbines,

nuclear
reactors,
steam
engines,
diesel
en-‐

gines,
and
other
prime
movers”

§  Example
two:
false
posi:ve
match
for
“diesel
motor”

“The
introduc:on
of
commercial-‐model
diesel
engines,
in
a

rela:vely
small
quan:ty
of
trucks.”

Lucence
tokeniza:on
splits

en-‐gines
into
“en”
and
“gines”,

Thus
leads
to
false
nega:ve

End
of
line

Single-‐pass
processing
will
handle

word
con:nua:on
case
by

concatena:ng
“en-‐gines”
into

“engines”
ﬁrst
before
matching

Solr
failed
to
detect
this
straigh‚orward
case,
we
do
not

know
the
reason
and
need
further
inves:ga:on

False
nega:ve
caused
by
OCR
errors

•  Example
one:

“Burdick,
R.
H.
Performance
of
diesel.engine
plants
in

Texas.”

•  Example
two:

“from
gasoline-‐powered
to
fuel-‐eﬃcient
diesel-‐
_powered
engines”

•  One-‐pass
processing
approach
failed
to
detect

them
in
such
cases

Tokens
generated
as
result

of
OCR
error

Raw
copyright
data
on
1)

ﬁle
system
and
in
archive
in

pairtree
form,
2)
chunked
form
for
parallel
processing

and
2)
in
full
text
Solr
index

Knowledge
product
services

Data
Capsule
VMs

Services
and
Tools

for
data
extrac:on,
data
cleaning,
data

analysis
and
results
visualiza:on

Knowledge
products
(public):

workset,

ontology,
feature
sets

Portal
(for
authen:ca:on)

Knowledge
products

(private):
personal
worksets

External
data

cache

DH
CS
NLP
R
.
.
.

Data
management
services

Thank
you!

plale@indiana.edu

@bplale

hfps://sharc.hathitrust.org/

HathiTrust Research Center Secure Commons

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