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

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)