1. SEASR & Meandre for Second Generation Digital Libraries
National Center for Supercomputing Applications University of Illinois at Urbana-Champaign
Automated Learning Group

2. SEASR
This project focuses on developing, integrating, deploying, and sustaining a set of reusable and expandable software components and a supporting framework, SEASR will benefit a broad set of data mining applications for scholars in humanities
The key goals established for this effort are a set of software centric directives:
Support the development of a state-of-the-art software environment for unstructured data management and analysis of digital libraries, repositories and archives, as well as educational platforms that are expected to contribute to many of the humanities breakthroughs of the 21st century.
Support the continued development, expansion, and maintenance of end-to-end software system – user interfaces, workflow engines, data management, analysis and visualization tools, collaborative tools, and other software integrated into a complete environment SEASR – to bring the full power of data analytics to the scholars. 
Support education and training for use of this software environment for analysis through workshops to promote its usage among scholars

3. The SEASR Picture

4. SEASR Enables Scholarly Research
Discovery
What hypothesis or rules can be generated by the “features” of the corpus?
What “features” or language of the corpus best describes the corpus?
What are the “similarities” between elements, documents, or corpuses to each other.

5. SEASR Architecture

6. SEASR: Reach + Relevance + Reuse + Repeatability
SEASR emphasizes flexibility, scalability, modularity, and access to heterogeneous data and computational systems
Semantic driven environment for SOA interoperability
Encourages sharing and participation for building communities
Modular construction allows flows to be modified and configured to encourage reusability within and across domains
Enables a mashup and integration of tools
Data-intensive flows can be executed on a simple desktop or a large cluster(s) without modification
Computation can be created for distributed execution on servers where the content lives

7. SEASR @ Work – Tag Cloud Counts tokens
Several different filtering options supported

8. SEASR @ Work – Dunning Loglikelihood
Feature Comparison of Tokens
Specify an analysis document/collection
Specify a reference document/collection
Perform Statistics comparison using Dunning Loglikelihood
Example showing over-represented
Analysis Set: The Project Gutenberg EBook of A Tale of Two Cities, by Charles Dickens
Reference Set: The Project Gutenberg EBook of Great Expectations, by Charles Dickens

9. SEASR @ Work – Date Entities to Simile Timeline
Entity Extraction with OpenNLP
Dates viewed on Simile Timeline
Locations viewed on Google Map

10. SEASR @ Work – Text Clustering
Clustering of Text by token counts
Filtering options for stop words, Part of Speech
Dendogram Visualization

11. 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: Annotation of Audio via Spectral Analysis

12. SEASR @ Work – Zotero Plugin to Firefox Zotero manages the collection
Launch SEASR Analytics
Citation Analysis uses the JUNG network importance algorithms to rank the authors in the citation network that is exported as RDF data from Zotero to SEASR
Zotero Export to Fedora through SEASR
Saves results from SEASR Analytics to a Collection

13. SEASR @ Work – Fedora Repository Search & Browse
Interactive Web Application
Web Service
Zotero Upload to Repository

14. Meandre: Infrastructure
SEASR/Meandre Infrastructure:
Dataflow execution paradigm
Semantic-web driven
Web Oriented
Supports publishing services
Modular components
Encapsulation and execution mechanism
Promotes reuse, sharing, and collaboration
The SEASR project and its Meandre infrastructure are sponsored by The Andrew W. Mellon Foundation

15. Meandre Data-Intensive Flows
SEASR Apps
SEASR Plugins
SEASR Web Apps
SEASR Services
Meandre Data-Intensive Flows
SEASR Components
Data
Analytics
Visualization
Developer Tools
Gateway Connections
Data Persistence
Data Transformation
Natural Lang Processing
Descriptive Statistics
Predictive Modeling
Discovery
Graphing
Modeling Vis
Info Vis (small multiples)
Component Repository
Component Discovery
Meandre Infrastructure
Shared Stores
File Systems
Metadata Stores
SOA Gateways
Virtualization Infrastructure

16. Meandre: Data Driven Execution
Execution Paradigms
Conventional programs perform computational tasks by executing a sequence of instructions.
Data driven execution revolves around the idea of applying transformation operations to a flow or stream of data when it is available.
Dataflow Approach
May have zero to many inputs
May have zero to many outputs
Performs a logical operation when data is available
The SEASR project and its Meandre infrastructure are sponsored by The Andrew W. Mellon Foundation

17. Meandre: Dataflow Example
Value1
Value2
Sum
Logical Operation
Output
Inputs
Dataflow Addition Example
Logical Operation ‘+’
Requires two inputs
Produces one output
When two inputs are available
Logical operation can be preformed
Sum is output
When output is produced
Reset internal values
Wait for two new input values to become available
The SEASR project and its Meandre infrastructure are sponsored by The Andrew W. Mellon Foundation

18. Meandre: The Dataflow Component
Data dictates component execution semantics
Inputs
Outputs
Component P
Descriptor in RDF of its behavior
The component implementation
The SEASR project and its Meandre infrastructure are sponsored by The Andrew W. Mellon Foundation 18

19. Meandre: Semantic Web Concepts
Relies on the usage of the resource description framework (RDF) which uses simple notation to express graph relations written usually as XML to provide a set of conventions and common means to exchange information
Provides a common framework to share and reuse data across application, enterprise, and community boundaries
Focuses on common formats for integration and combination of data drawn from diverse sources
Pays special attention to the language used for recording how the data relates to real world objects
Allows navigation to sets of data resources that are semantically connected.
The SEASR project and its Meandre infrastructure are sponsored by The Andrew W. Mellon Foundation

20. Meandre: Metadata Ontologies
Meandre's metadata relies on three ontologies:
The RDF ontology serves as a base for defining Meandre descriptors
The Dublin Core Elements ontology provides basic publishing and descriptive capabilities in the description of Meandre descriptors
The SEASR project and its Meandre infrastructure are sponsored by The Andrew W. Mellon Foundation

21. Meandre: Components in RDF
@prefix meandre: < .
@prefix xsd: < .
@prefix dc: < .
@prefix rdfs: < .
@prefix rdf: < .
@prefix : <#> .
<
meandre:name "Limited iterations"^^xsd:string ;
rdf:type meandre:executable_component ;
dc:creator "Xavier Llora"^^xsd:string ;
dc:date " T00:32:35"^^xsd:date ;
dc:description "Allows only a limited number of iterations"^^xsd:string ;
dc:format "java/class"^^xsd:string ;
dc:rights "University of Illinois/NCSA Open Source License"^^xsd:string ;
meandre:execution_context
< ,
< ,
< ,
< ,
< ;
...
Existing Standards
The SEASR project and its Meandre infrastructure are sponsored by The Andrew W. Mellon Foundation

22. Meandre: Components Types
Components are the basic building block of any computational task.
There are two kinds of Meandre components:
Executable components
Perform computational tasks that require no human interactions during runtime
Processes are initialized during flow startup and are fired when in accordance to the policies defined for it.
Control components
Used to pause dataflow during user interaction cycles
WebUI may be a HTML Form, Applet, or Other user interface
The SEASR project and its Meandre infrastructure are sponsored by The Andrew W. Mellon Foundation

23. Meandre: Create, Publish, & Share
“Components” and “Flows” have RDF descriptors
Easily shared, fosters sharing, & reuse
Allow machines to read and interpret
Independent of the implementations
Combine different implementation & platforms
Components: Java, Python, Lisp, Web Services
Execution: On a Laptop or a High Performance Cluster
A “Location” is RDF descriptor of one to many components, one to many flows, and their implementations
The SEASR project and its Meandre infrastructure are sponsored by The Andrew W. Mellon Foundation

24. Meandre: Repository & Locations
Each location represents a set components/flows
Users can
Combine different locations together
Create components
Assemble flows
Share components and flows
Repositories Help
Administrate complex environments
Organize components and flows
The SEASR project and its Meandre infrastructure are sponsored by The Andrew W. Mellon Foundation

25. Meandre: Metadata Properties
Components and Flows share properties such as component name, creator, creation date, description, tags, and rights.
Components specific metadata to describe the components' behavior, it’s location, type of implementation, firing policy, runnable, format, resource location, and execution context
Flow specific metadata describes the directed graph of components, components instances, connectors, connector instance data port source, connector, instance data port target, connector instance source, connector instance target, instance name
The SEASR project and its Meandre infrastructure are sponsored by The Andrew W. Mellon Foundation

26. Meandre: Programming Paradigm
The programming paradigm creates complex tasks by linking together a bunch of specialized components. Meandre's publishing mechanism allows components developed by third parties to be assembled in a new flow.
There are two ways to develop flows :
Meandre’s Workbench visual programming tool
Meandre’s ZigZag scripting language
The SEASR project and its Meandre infrastructure are sponsored by The Andrew W. Mellon Foundation

27. Meandre: Workbench Existing Flow
Web-based UI
Components and flows are retrieved from server
Additional locations of components and flows can be added to server
Create flow using a graphical drag and drop interface
Change property values
Execute the flow
Components
Flows
Locations
The SEASR project and its Meandre infrastructure are sponsored by The Andrew W. Mellon Foundation

28. Meandre: ZigZag Script Language
ZigZag is a simple language for describing data- intensive flows
Modeled on Python for simplicity.
ZigZag is declarative language for expressing the directed graphs that describe flows.
Command-line tools allow ZigZag files to compile and execute.
A compiler is provided to transform a ZigZag program (.zz) into Meandre archive unit (.mau).
Mau(s) can then be executed by a Meandre engine.
The SEASR project and its Meandre infrastructure are sponsored by The Andrew W. Mellon Foundation

29. DEMO
Perform analysis using "Tag Cloud Viewer" on a hard coded web page
Open browser and go to
Click on the "Execute" button to launch the creation of a tag cloud view for "Emma" by Jane Austen retrieved from Project Gutenberg

30. Discussion Questions
What are data repositories that you utilize in your scholarly research?
What tools or applications are being utilized against these repositories?