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

Overview of Course MondayTuesdayWednesdayThursdayFriday MORNINGMORNING 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 Classification Attendee Project Work HTRC Introduction Workset Building Tag Cloud Dunning Loglikelihood Comparison Entity Extraction Attendee Project Work SEASR Analytics for Zotero Demonstrations of SEASR Analytics Use of SEASR services with Zotero Creating Zotero Flows Configuration Mechanism Web Service Components Zotero-enabled Flows Attendee Project Work SEASR Future Work Community Collaboration Tools Future Meandre Features Future Meandre Workbench Features Attendee Plan Presentations AFTERNOONAFTERNOON Meandre Workbench, Installation & NLP Overview Overview of Workbench Overview of Repositories Designing and Constructing Flows Installation NLP Overview & Examples Attendee Project Plan More Text Analytics Emotion Tracking Concept Tracking Topic Modeling Attendee Project Work More HTRC Spell Checking Topic Modeling Attendee Project Work Visualizations, Mashups, and Dashboards Visualization SEASR as a service Text App: Correlation & Ngram Viewer Text App: Topic Model Explorer Text App: ProseVis Text App: Airport Data Attendee Project Work

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

SEASR Overview

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.

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.

The SEASR Picture

Workshop Objective The objective of the workshop is: To provide examples of text analysis 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

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

Work – Tag Cloud Count tokens Filter options supported Stem words

Work – Ngram Tag Cloud Count multiple words Filter options Stem

Work – Dunning Loglikelihood Feature comparison based on 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 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

Work – HITS Summarizer

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

Work – Entities To Network Identify entities Define relationships between entities within same sentence

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

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

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 Work – Audio Analysis

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

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 WorkflowMONK DB Ingestion Workflow

TEI components for SEASR by Brown U

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

Components Java Virtual Machine Meandre Infrastructure Visualization Component Repository Component Discovery Meandre Data-Intensive Flows Apps Services Plugins Web Apps Analytics Data Developer Tools Repositories Data Analysis Components Flows Repositories Data Analysis Components Flows User Interfaces 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 –Modular components –Semantic-web driven –Web Oriented –Supports publishing services –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: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 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

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 of 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 Explore existing flows to find others of interest –Keyword Cloud –Connections Find related flows Execute flow Comments

Community Hub: Keyword Cloud Design

Community Hub

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 Hub 1.Explore Community Hub's Keyword Cloud Functionality A.Open browser and go to B.Click on "Keyword Cloud” (top left side, under Download) C.Click on "visualization" to see all the existing applications that have a tag of "visualization" D.Click on "cluster" to see all the existing applications that have a tag of "visualization" and ”tag cloud” E.Click on the delete button to remove ”tag cloud" from the selection F.Click on the "Tag Cloud Viewer" for more detail information about this application

Learning Exercises: Tag Cloud Viewer 2.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/ 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

Learning Exercises: Tag Cloud Viewer 3.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/ 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

Learning Exercises: Google Search 4.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 flows/google-search-to-tag-cloud-viewer/ 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 Identify Research Question

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?