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

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SEASR Overview

  1. 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. 2. The
SEASR
Picture

  3. 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. 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. 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. 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. 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. 8. Community
Hub

  9. 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. 10. SEASR
@
Work
–
Fedora
 Interac9ve
Web

 Applica9on
 Web
Service

  11. 11. SEASR
@
Work
–

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

 •  Dates
viewed
 on
Simile
 Timeline

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

  14. 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. 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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