1. SEASR Overview Loretta Auvil, Boris Capitanu
University of Illinois at Urbana-Champaign

2. Overview of Course Monday Tuesday Wednesday Thursday Friday MORNING
SEASR Overview
Overview of Course
SEASR Overview and Motivation
Example SEASR Analytics and Applications
SEASR Architecture
Introduction of Meandre
SEASR Community Hub
Attendee Project Plan
Text Analytics
Dunning Loglikelihood Comparison
Entity Extraction
Spell Checking
Text Analytics: Monk
Attendee Project Work
SEASR Analytics for Zotero
Demonstrations of SEASR Analytics
Use of SEASR services with Zotero and VUE
Creating Zotero Flows
Configuration Mechanism
Web Service Components
Zotero-enabled Flows
VUE-enabled Flows
Visualizations, Mashups & Dashboards
Visualization
SEASR as a service
Other services
Text App: Correlation & Ngram Viewer
Text App: ProseVis
SEASR Apps & Future Work
Audio Analytics: NEMA
Text Analytics: HTRC
SEASR Central
Future Meandre Features
Future Meandre Workbench Features
Attendee Plan Presentations
AFTERNOON
Meandre Workbench, Installation & NLP Overview
Overview of Workbench
Overview of Repositories
Designing and Constructing Flows
Installation
NLP Overview & Examples
More Text Analytics
Emotion Tracking
Concept Tracking
Topic Modeling
Modify project plan
Identify data set
Identify analysis
Tools & Deployment
Community Collaboration Tools
Architecture Details
Overview of Development Tools
Overview of ZigZag
Parallelization
Example ZigZag flows with Zotero, VUE and Fedora

3. Outline Overview of Course SEASR Overview and Motivation
Example SEASR Analytics and Applications
Attendee Plan
SEASR Architecture
Introduction of Meandre
SEASR Community Hub
Hands-On

4. SEASR Overview

5. SEASR Project This project will focus on developing, integrating,
deploying, and
sustaining
a set of reusable and expandable software components and a supporting framework,
to benefit a broad set of data mining applications for scholars in humanities.

6. Key Goals
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.

7. The SEASR Picture

8. Workshop Objective The objective of the workshop is:
To explain and demonstrate the utility of SEASR for digital humanities, and to bring you to a point where you could deploy, contribute and utilize the SEASR environment.

9. Workshop Goals The goals of the workshop are:
LEARN: Provide a detailed understanding of the SEASR framework
LEARN: Provide a foundation and examples for participant teams to use SEASR in a study or inquiry
ADOPT: Share participant generated research plans to utilize SEASR
INSTALL: Provide detailed instructions on how to install, build components, integrate existing applications, and maintain the SEASR environment
SUPPORT: Develop plans for resolution of issues raised by the user community in utilization of SEASR
SUSTAIN: Develop a plan for community driven future development and dissemination of SEASR

10. SEASR @ Work – Tag Cloud Count tokens Filter options supported
Stem words

11. SEASR @ Work – Ngram Tag Cloud
Count multiple words
Filter options
Stem

12. 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

13. SEASR @ Work – HITS Summarizer

14. SEASR @ Work – Entity Mash-up
Entity Extraction with OpenNLP or Stanford NER
Locations viewed on Google Map
Dates viewed on Simile Timeline

15. SEASR @ Work – Entities To Network
Identify entities
Define relationships between entities within same sentence

16. SEASR @ Work – Topic Modeling
Given: Set of documents
Find: To reveal the semantic content in large collection of documents
Usage: Mallet Topic Modeling tools
Output:
Shows the percentage of relevance for each document in each cluster
Shows the key words and their counts for each topic

17. SEASR @ Work – Concept Mapping
Goal is to have this type of Visualization to track emotions across a text document (Leveraging flare.prefuse.org)

18. 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

19. SEASR @ Work – MONK Executes flows for each analysis requested
Predictive modeling using Naïve Bayes
Predictive modeling using Support Vector Machines (SVM)
Feature comparisons

20. 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
Launch MONK Processing
MONK DB Ingestion Workflow

21. TEI components for SEASR by Brown U

22. Attendee Project Plan Explore tool usage during learning exercises
Participate in discussion
Design a project plan to use SEASR this week for some analysis
Modify and develop the project plan over the week
Present and discuss project plan and results on Friday

23. Attendee Project Plan (2)
Study/Project Title
Team Members and their Affiliation
Procedural Outline of Study/Project
Research Question/Purpose of Study
Data Sources
Analysis Tools
Activity Timeline or Milestones
Report or Project Outcome(s)
Ideas on what your team needs from SEASR staff to help you achieve your goal.

24. SEASR Architecture

25. Meandre Data-Intensive Flows
SEASR Architecture
Visualizations
User Interfaces
Apps
Plugins
Web Apps
Services
Meandre Workbench
Repositories
Data
Analysis
Components
Flows
Meandre Data-Intensive Flows
Components
Developer Tools
Data
Analytics
Visualization
Component Repository
Component Discovery
Meandre Infrastructure
Java Virtual Machine

26. Data Driven Models

27. SEASR: Reach + Relevance + Reuse + Repeatability
SEASR emphasizes flexibility, scalability, modularity, provides community hub 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
User accessibility to control trust and compliance with required copyright license of content
Relies on standardized Resource Description Framework (RDF) to define components and flow

28. Enables Humanist To ask key questions:
What recurrent patterns would be of interest to literary scholars
Which patterns are characteristic of the English language and which are characteristic of a particular author, work, topic, or time?
Patterns based on words can be extracted from literary bodies; however, can patterns be extracted based on grammar or plot constructs?
When are correlated patterns meaningful? Can they be organized based on such criteria?
How can an author’s intentionality be assessed given an extracted pattern?

29. 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.

30. 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

31. 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.

32. 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 Meandre ontology describes a set of relationships that model valid components, as understood by the Meandre execution engine architecture

33. 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

34. 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

35. Meandre: Dataflow Example
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
Value1
Value2
Sum
Logical Operation
Output
Inputs

36. 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

37. 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

38. 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

39. 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

40. 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

41. 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.

42. Community Hub Explore existing flows to find others of interest
Keyword Cloud
Connections
Find related flows
Execute flow
Comments

43. Community Hub: Keyword Cloud Design

44. Community Hub

45. Keyword Cloud Implementation
Keyword Cloud functionality is currently implemented as a wordpress plugin

46. Detail View of Application
Detail View with Related Flows

47. Community Hub: Connections Design

48. Demonstration Community Hub NEMA's Son of Blinkie
Keyword Cloud Functionality
Tag Cloud Viewer
Ngram Tag Cloud Viewer
HITS Summarizer
Date Entity to Simile Timeline
Location Entity to Google Map
Google Search to Tag Cloud Viewer
Entity to Protovis Network Graph
Readability
NEMA's Son of Blinkie

49. Learning Exercises: Community Hub
Explore Community Hub's Keyword Cloud Functionality
Open browser and go to
Click on "Keyword Cloud” (top left side, under Download)
Click on "visualization" to see all the existing applications that have a tag of "visualization"
Click on "cluster" to see all the existing applications that have a tag of "visualization" and ”tag cloud”
Click on the delete button to remove ”tag cloud" from the selection
Click on the "Tag Cloud Viewer" for more detail information about this application

50. Learning Exercises: Tag Cloud Viewer
Perform analysis using "Tag Cloud Viewer" on a hard coded web page
Use Community Hub to open the "Tag Cloud Viewer" page or open browser and go to cloud-viewer/
Click on the "Execute" button to launch the creation of a tag cloud view for "Emma" by Jane Austen retrieved from Project Gutenberg

51. Learning Exercises: Tag Cloud Viewer
Perform analysis using Tag Cloud Viewer" on a webpage of your choice
Use Community Hub to open the "Tag Cloud Viewer" page or open browser and go to cloud-viewer/
Find a web url that you are interested in analyzing
Click on the "Custom Execute" button to launch the application where you can copy and paste a web url that you are interested in analyzing

52. Learning Exercises: Google Search
Perform analysis using "Google Search to Tag Cloud Viewer" on a topic of your choice
Use Community Hub to open the "Google Search to Tag Cloud Viewer" page or open browser and go to flows/google-search-to-tag-cloud-viewer/
Click on the "Custom Execute" button to launch the application where you can type your Google query for analysis

53. Attendee Project Plan Identify Research Question Study/Project Title
Team Members and their Affiliation
Procedural Outline of Study/Project
Research Question/Purpose of Study
Data Sources
Analysis Tools
Activity Timeline or Milestones
Report or Project Outcome(s)
Ideas on what your team needs from SEASR staff to help you achieve your goal.
Identify Research
Question

54. Discussion Questions
Which kinds of data repositories do you utilize in your scholarly research?
What analytical tools or applications do you utilize with these repositories?