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SEASR Overview
SEASR Overview
SEASR Overview
SEASR Overview
SEASR Overview
SEASR Overview
SEASR Overview
SEASR Overview
SEASR Overview
SEASR Overview
SEASR Overview
SEASR Overview
SEASR Overview
SEASR Overview
SEASR Overview
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SEASR Overview

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Presentation given introducting SEASR on Mar 31, 2009 for ICHASS to the faculty from the UIUC Department of African American Studies

Presentation given introducting SEASR on Mar 31, 2009 for ICHASS to the faculty from the UIUC Department of African American Studies

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  • 1. SEASR National Center for Supercomputing Applications! University of Illinois at Urbana-Champaign Loretta Auvil lauvil@illinois.edu The SEASR project and its Meandre infrastructure! are sponsored by The Andrew W. Mellon Foundation
  • 2. The
SEASR
Picture

  • 3. SEASR
Overview
 SEASR
will:
 •  help
scholars
access
exis9ng
large
data
stores
more
readily
 •  provide
scholars
with
enhanced
data
synthesis
and
query
analysis
 –  from
focused
data
retrieval
and
data
integra9on
 –  to
intelligent
human‐computer
interac9ons
for
knowledge
access
 –  to
seman9c
data
enrichment
 –  to
en9ty
and
rela9onship
discovery
 –  to
knowledge
discovery
and
hypothesis
genera9on
 •  empower
collabora9on
among
scholars
by
enhancing
and
innova9ng
 virtual
research
environments

  • 4. A
Quick
Look
at
SEASR
 •  Addresses:
 –  Challenges
of
transforming
informa9on
into
 knowledge
 –  Construc9ng
the
soGware
bridges
to
move
from
 the
unstructured
and
semi‐structured
data
world
 to
the
structured
data
world.

 •  Aims:
 –  Make
digital
collec9ons
more
useful

 –  Provide
access
to
relevant
analy9cs
and
 visualiza9ons
 –  Enable
easy
mashability
via
SOA


  • 5. SEASR:
Reach
+
Relevance
+
Reuse
+
Repeatability

 
SEASR
emphasizes
flexibility,
scalability,
modularity,
provides
 community
hub
and
access
to
heterogeneous
data
and
 computa9onal
systems
 –  Seman9c
driven
environment
for
SOA
interoperability
 –  Encourages
sharing
and
par9cipa9on
for
building
communi9es
 –  Modular
construc9on
allows
flows
to
be
modified
and
configured
to
 encourage
reusability
within
and
across
domains
 –  Enables
a
mashup
and
integra9on
of
tools
 –  Data‐intensive
flows
can
be
executed
on
a
simple
desktop
or
a
large
 cluster(s)
without
modifica9on
 –  Computa9on
can
be
created
for
distributed
execu9on
on
servers
where
 the
content
lives
 –  User
accessibility
to
control
trust
and
compliance
with
required
copyright
 license
of
content
 –  Relies
on
standardized
Resource
Descrip9on
Framework
(RDF)
to
define
 components
and
flow

  • 6. SEASR
Text
Analy9cs
Goals
 Address
the
Scholarly
text
analy9cs
needs
by:
 •  Efficiently
managing
distributed
Literary
and
Historical
textual
assets
 •  Structuring
extracted
informa9on
to
facilitate
knowledge
discovery
 •  Extract
informa9on
from
text
at
a
level
of
seman9c/func9onal
 abstrac9on
that
is
sufficiently
rich
to
support
ques9on‐answering
 •  Devise
a
representa9on
for
the
extracted
informa9on
that
can
be
 efficiently
reasoned
over
to
recover
data
in
the
ques9on‐answer
 process
 •  Devise
algorithms
for
ques9on
answering
and
inference
 •  Develop
UI
for
effec9ve
visual
knowledge
discovery
with
separate
 query
logic
from
applica9on
logic

 •  Leveraging
exis9ng
approaches
and
devise
algorithms
for
clustering,
 inference,
and
Q&A
 •  Developing
an
Interac9on
UI
for
effec9ve
visual
data
explora9on
 •  Enable
the
text
analy9cs
through
SEASR
components

  • 7. Workbench
 •  Web‐based
UI
 •  Components
and
flows
 are
retrieved
from
server
 •  Addi9onal
loca9ons
of
 components
and
flows
 can
be
added
to
server
 •  Create
flow
using
a
 graphical
drag
and
drop
 interface
 •  Change
property
values
 •  Execute
the
flow

  • 8. Community
Hub

  • 9. SEASR
@
Work
–
Zotero
 •  Plugin
to
Firefox

 •  Zotero
manages
the
 collec9on
 •  Launch
SEASR
Analy9cs

 –  Cita9on
Analysis
uses
the
JUNG
 network
importance
algorithms
 to
rank
the
authors
in
the
cita9on
 network
that
is
exported
as
RDF
 data
from
Zotero
to
SEASR
 –  Zotero
Export
to
Fedora
through
 SEASR
 –  Saves
results
from
SEASR
 Analy9cs
to
a
Collec9on
 •  Launch
MONK
Processing
 –  MONK
DB
Inges9on
Workflow

  • 10. SEASR
@
Work
–
Fedora
 Interac9ve
Web

 Applica9on
 Web
Service

  • 11. SEASR
@
Work
–

En9ty
Mash‐up
 •  En9ty
 Extrac9on
with
 OpenNLP
 •  Loca9ons
 viewed
on
 Google
Map

 •  Dates
viewed
 on
Simile
 Timeline

  • 12. SEASR
@
Work
–
Audio
Analysis
 •  NEMA:
Executes
a
SEASR
 flow
for
each
run
 –  Loads
audio
data
 –  Extracts
features
for
every
 10
sec
moving
window
of
 audio
 –  Loads
and
applies
the
 models
 –  Sends
results
back
to
the
 WebUI
 •  NESTER:
Annota9on
of
 Audio
via
Spectral
 Analysis

  • 13. SEASR
@
Work
–
MONK
 Executes
flows
for
 each
analysis
 requested
 –  Predic9ve
 modeling
using
 Naïve
Bayes
 –  Predic9ve
 modeling
using
 Support
Vector
 Machines
(SVM)

  • 14. SEASR
@
Work
–
DISCUS
 •  On‐demand
usage
of
 analy9cs
while
surfing
 –  While
naviga9ng
 request
analy9cs
to
be
 performed
on
page
 –  Text
extrac9on
and
 cleaning
 •  Summariza9on
and
key
 work
extrac9on
 –  List
the
important
 terms
on
the
page
 being
analyzed
 –  Provide
relevant
short
 summaries

 •  Visual
maps
 –  Provide
a
visual
 representa9on
of
the
 key
concepts
 –  Show
the
graph
of
 rela9ons
between
 concepts

  • 15. SEASR
and
UIMA
:
Emo9on
Tracking
 
Goal
is
to
have
this
type
of
Visualiza9on
to
track
emo9ons
across
a
text
 document
(Leveraging
flare.prefuse.org)


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