SEASR Applications and Future Work National Center for Supercomputing Applications University of Illinois at Urbana-Champaign

Outline Audio Applications Future Hands-On

Defining Music Information Retrieval? Music Information Retrieval (MIR) is the process of searching for, and finding, music objects, or parts of music objects, via a query framed musically and/or in musical terms Music Objects: Scores, Parts, Recordings (WAV, MP3, etc.), etc. Musically framed query: Singing, Humming, Keyboard, Notation-based, MIDI file, Sound file, etc. Musical terms: Genre, Style, Tempo, etc.

NEMA Networked Environment for Music Analysis –UIUC, McGill (CA), Goldsmiths (UK), Queen Mary (UK), Southampton (UK), Waikato (NZ) –Multiple geographically distributed locations with access to different audio collections –Distributed computation to extract a set of features and/or build and apply models

Work – NEMA Executes a SEASR flow for each run –Loads audio data –Extracts features from every 10 second moving window of audio –Loads models –Applies the models –Sends results back to the WebUI

NEMA Flow – Blinkie

NEMA Vision researchers at Lab A to easily build a virtual collection from Library B and Lab C, acquire the necessary ground-truth from Lab D, incorporate a feature extractor from Lab E, combine with the extracted features with those provided by Lab F, build a set of models based on pair of classifiers from Labs G and H validate the results against another virtual collection taken from Lab I and Library J. Once completed, the results and newly created features sets would be, in turn, made available for others to build upon

Do It Yourself (DIY) 1

DIY Options

DIY Job List

DIY Job View

Nester: Cardinal Annotation Audio tagging environment Green boxes indicate a tag by a researcher Given tags, automated approaches to learn the pattern are applied to find untagged patterns

NESTER: Cardinal Audio Analysis

Examining Audio Collection Tagged a set of examples Male and Female

SEASR Central feedback | login | search central Categories Recently Added Top 50 Submit About RSS Featured Component [read more] Word Counter by Jane Doe Description Amazing component that given text stream, counts all the different words that appear on the text Rights: NCSA/UofI open source license Featured Component [read more] Word Counter by Jane Doe Description Amazing component that given text stream, counts all the different words that appear on the text Rights: NCSA/UofI open source license Featured Flow [read more] FPGrowth by Joe Does Browse By Joe Doe Rights: NCSA/UofI Description: Webservices given a Zotero entry tries to retrieve the content and measure its By Joe Doe Rights: NCSA/UofI Description: Webservices given a Zotero entry tries to retrieve the content and measure its Type Component Flows Categories Image JSTOR Zotero Name Author Centrality Readability Upload Fedora

SEASR Central Use Cases register for an account search for components / flows browse components / flows / categories upload component / flow share component / flow with: everyone or group unshare component / flow create group / delete group join group / leave group create collection generate location URL (permalink) for components, flows, collection (the location URL can be used inside the Workbench to gain access to that component or flows) view latest activity in public space / my groups

Hot topics on 1.4.X Complex concurrency model based on traditional semaphores written in Java Server performance bounded by JENA’s persistent model implementation State caching on individual servers increase complexity of single-image clusters Cloud-deployable, but not cloud-friendly

How 1.5 efforts turned into 2.0? Cloud-friendly infrastructure required rethinking core functionalities Drastic redesign of backend state storage Revisited execution engine to support distributed flow execution Changes on the API that will render returned JSON documents incompatible with 1.4.X

What's New 2.0 Series? Rewritten from scratch in Scala RDBMS backend via Jena/JDBC has been dropped MongoDB for state management and scalability Meandre 2.0 server is stateless Meandre API revised –Revised response documents –Simplified API (reduced the number of services) –New Job API (Submit jobs for execution; Track them (monitor state, kill, etc.); Inspect console and logs in real time

What's New From 1.4.X Series? New HTML interaction interface Off-the-shelf full-fledged single-image cluster Revised flow execution lifecycle: Queued, Preparing, Running, Done, Failed, Killed, Aborted Flow execution as a separate spawned process. Multiple execution engines are available Running flows can be killed on demand Rewritten execution engine (Snowfield) Support for distributed flow fragment execution

Meandre 2.0 Meandre 2.0 requires at least 2 separate services running –A MongoDB for shared state storage and management holds all server state, job related information, and system information –A Meandre server to provide services and facilitate execution (customizable execution engines) A single-image Meandre cluster scales horizontally by adding new Meandre servers and sharding the MongoDB store

Meandre and Cloud Computing Next generation data-intensive applications will: –Use cloud computing technologies and conduits –Require adaptation of programming paradigms –Leverage a flexible architecture and a modular –Promote processing and resources at scale. Meandre –Data-intensive execution engine –Component-based programming architecture –Distributed data flow designs to allow processing to be co-located with data sources and enable transparent scalability –Orchestrate cloud deployments –Leverage cloud conduits

Meandre Workbench Futures Copy and paste (between and within flows) Add custom property editor for types (checkbox, lists, etc) Ability to specify parallel computation like in ZigZag Ability to use flows within flows (for grouping of functionality)

Projects SEASR Follow-On with Mellon Foundation –Collaborators: Stanford, University of Maryland, George Mason University Hathi-Trust Research Center –NCSA as a Computational Site –Collaboration with Indiana University –HTRC reception at Digital Humanities :00pm - 7:30pm (PDT) on Monday, June 20 Bamboo –Deploy a set of analytical services

Demonstration

Discussion Questions How can SEASR benefit my research? What does SEASR need to look like for the future of humanities research? What scholarly questions do I have from my research for what to do with a million books?