SEASR Overview

Loretta Auvil

National Center for Supercomputing Applications

University of Illinois at Urbana-Champaign

lauvil@illinois.edu

Overview of CourseMonday 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

Meandre Workbench•Overview of Workbench•Overview of Repositories•Designing and Constructing Flows

SEASR Analytics for Zotero•Demonstrations of SEASR Analytic•Use of SEASR services with Zotero and VUE

Mashups and Dashboards•SEASR as a service•Other services, like Tapor•Text Application: JSTOR•Text Application: WEME

Future•Audio Analytics: NEMA •SEASR Central •Future Meandre Features •Future Meandre Workbench Features •Attendee Plan Presentations•Course Wrap-up

AFTERNOON

Text Analytics•Overview of Text Analytics

• Dunning Loglikelihood Comparison

• Text Clustering• Frequent Patterns

Analysis• Entity Extraction

•Text Application: MONK Workbench

More Text Analytics•Emotion Tracking•Concept Tracking•Entity Extraction

Creating Zotero Flows•Configuration Mechanism•Specific Web Service Components•Zotero-enabled Flows•VUE-enabled Flows

Installation, Tools and Deployment of Flows•Installation•Community Collab Tools •Architecture Details •Overview of Development Tools•Overview of ZigZag•Parallelization•Example ZigZag flows with Zotero, VUE and Fedora

Outline

• SEASR Overview and Motivation

• Example SEASR Analytics and Applications

• Attendee Plan

• SEASR Architecture

• Introduction of Meandre

• SEASR Community Hub

• Hands-On

SEASR Overview

SEASR

• This project will focus on developing, integrating, deploying, and sustaining a set of reusable and expandable software components and a supporting framework, SEASR that 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

The SEASR Picture

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.

SEASR + TOOLS + EXEMPLARS + HANDS ON

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

SEASR @ Work – Tag Cloud

• Count tokens• Filter options

supported• Stem words

SEASR @ Work – Ngram Tag Cloud

• Count multiple words

• Filter options• Stem

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-representedAnalysis Set: The Project Gutenberg EBook of A Tale of Two Cities, by Charles DickensReference Set: The Project Gutenberg EBook of Great Expectations, by Charles Dickens

SEASR @ Work – HITS Summarizer

SEASR @ Work – Entity Mash-up

• Entity Extraction with OpenNLP or Stanford NER

• Locations viewed on Google Map

• Dates viewed on Simile Timeline

SEASR @ Work – Entities To Network• Identify entities• Define relationships between entities

within same sentence

SEASR @ Work – Text Clustering• Clustering of Text by token counts

• Filtering options for stop words, Part of Speech

• Dendogram Visualization

• 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

SEASR @ Work – Audio Analysis

SEASR @ Work – MONK

Executes flows for each analysis requested

– Predictive modeling using Naïve Bayes

– Predictive modeling using Support Vector Machines (SVM)

– Feature comparisons

SEASR @ Work – DISCUS• On-demand usage

of analytics while surfing

– While navigating request analytics to be performed on page

– Text extraction and cleaning

• Summarization and key work extraction

– List the important terms on the page being analyzed

– Provide relevant short summaries

• Visual maps– Provide a visual

representation of the key concepts

– Show the graph of relations between concepts

SEASR @ Work – Emotion Tracking

Goal is to have this type of Visualization to track emotions across a text document (Leveraging flare.prefuse.org)

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 Workfl

ow

Repository Search & Browse

Web Service

Interactive Web

Application

Zotero Upload to Repository

SEASR @ Work – Fedora

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

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.

SEASR Architecture

SEASR Architecture

Components

Virtualization Infrastructure

Meandre Infrastructure

Visualization

Component Repository Component Discovery

Meandre Data-Intensive Flows

Apps ServicesPlugins Web Apps

AnalyticsData

Dev

elop

er T

ools

RepositoriesData

AnalysisComponents

Flows

User Interfaces

Cloud Computing

Visualizations

Meandre Workbench

Data Driven Models

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

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?

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.

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

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.

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

@prefix meandre: <http://www.meandre.org/ontology/> .@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .@prefix dc: <http://purl.org/dc/elements/1.1/> .@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .@prefix : <#> .

<http://dita.ncsa.uiuc.edu/meandre/e2k/components/limited-iterations> meandre:name "Limited iterations"^^xsd:string ; rdf:type meandre:executable_component ; dc:creator "Xavier Llora"^^xsd:string ; dc:date "2007-11-17T00:32:35"^^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

<http://norma.ncsa.uiuc.edu/public-dav/Meandre/demos/E2K/V1/resources/colt.jar> , <http://norma.ncsa.uiuc.edu/public-dav/Meandre/demos/E2K/V1/resources/gacore.jar> ,

<http://dita.ncsa.uiuc.edu/meandre/e2k/components/limited-iterations/implementation/> ,

<http://norma.ncsa.uiuc.edu/public-dav/Meandre/demos/E2K/V1/resources/gacore-meandre.jar> ,

<http://norma.ncsa.uiuc.edu/public-dav/Meandre/demos/E2K/V1/resources/formj2.0.jar> ;...

ExistingStandards

Meandre: Components in RDF

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

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

OutputInputs

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

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

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

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

Locations

Components

Flows

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

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.

Community Hub

Community Hub

• Explore existing flows to find others of interest

– Keyword Cloud

– Connections

• Find related flows

• Execute flow

• Comments

Community Hub: Keyword Cloud Design

Keyword Cloud Implementation

Keyword Cloud functionality is currently implemented as a wordpress plugin

Detail View of Application

Detail View with Related Flows

Community Hub: Connections Design

Demonstration

• Community Hub – 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

Learning Exercises: Community Hub1. Explore Community Hub's Keyword Cloud

FunctionalityA. Open browser and go to http://seasr.org B. Click on "View Projects” C. Click on "Keyword Cloud” D. Click on "visualization" to see all the existing

applications that have a tag of "visualization" E. Click on "cluster" to see all the existing

applications that have a tag of "visualization" and "cluster”

F. Click on the delete button to remove "cluster" from the selection

G. Click on the "Tag Cloud Viewer" for more detail information about this application

Learning Exercises: Tag Cloud Viewer2. Perform analysis using "Tag Cloud Viewer" on a

hard coded web page A. Open browser and go to

http://seasr.org/documentation/example-flows/tag-cloud-viewer/

B. Click on the "Execute" button to launch the creation of a tag cloud view for "Emma" by Jane Austen retrieved from Project Gutenberg

3. Perform analysis using Tag Cloud Viewer" on a webpage of your choice A. Open browser and go to

http://seasr.org/documentation/example-flows/tag-cloud-viewer/

B. Find a web url that you are interested in analyzing C. 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

Learning Exercises: Google Search4. Perform analysis using "Google Search to

Tag Cloud Viewer" on a topic of your choice

A. Use Community Hub to open the "Google Search to Tag Cloud Viewer" page or open browser and go to http://seasr.org/documentation/example-flows/google-search-to-tag-cloud-viewer/

B. Click on the "Custom Execute" button to launch the application where you can type your Google query for analysis

Attendee Project Plan

• 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

Discussion Questions

• What are data repositories that you utilize in your scholarly research?

• What tools or applications are being utilized against these repositories?