e-science data information and knowledge transformation thoughts on education and training for...

e-Science Data Information and Knowledge Transformatione-Science Data Information and Knowledge Transformation

Thoughts on Education and Thoughts on Education and Training for E-ScienceTraining for E-Science

Based on edikt project experience

Dr. Denise EcklundTechnical Architect

www.edikt.orgwww.edikt.org

AgendaAgenda

Background – the edikt project

What’s an “e-science research project”?

Identify e-science engineering activities

Derive educational requirements

– Hypothesise what is covered elsewhere

– Identify what is missing

My wish-list for ‘gap fillers’


The EDIKT ProjectThe EDIKT Project

E-Science Data, Informationand Knowledge Transformation


Edikt and its missionEdikt and its mission

Res

earc

h I

nte

rest

sR

AE

Study newCS theories indata management

Study data mgmt problems in astronomy, physics,

biology and geosciences

Match?


A Model for an e-Science A Model for an e-Science Research ProjectResearch Project


E-Science project staffingE-Science project staffing

The team

PrincipalInvestigator

TheoreticalScientist

ComputationalScientist

ComputingEngineer

The goal is to do new application science. An important,

but supporting role


Computer SWengineering

domain

Applicationsciencedomain

Complimentary knowledgeComplimentary knowledge

Conversationaland shared

understanding

Education andtraining are required

Education and

training are required


Tasks of a Computing EngineerTasks of a Computing Engineerin an e-science research projectin an e-science research project

working with peopleworking with machines


System development processSystem development process

Four phase process

Gatherrequirements

Investigatesolutions

Integratecomponents

Deploy &maintain


Working with peopleWorking with people

Phase 1 activities– Gather requirements

Understand ‘enough’ of the science application

– Answer questions and exchange information Intelligibly

Interact with team membershaving varying levelsof computer expertise

Gatherrequirements


Working with machinesWorking with machines

Phase 2 - activities– Survey, identify and evaluate existing

applicable software technologies Understand the requirements Effective search (where and how) Install and evaluate potential solutions

Gatherrequirements



Working with machinesWorking with machines

Phase 3 - activities– Provide an integrated software solution

Identify technology gaps Develop reliable software to fill the gaps Test and deploy full system

Gatherrequirements


Integratecomponents


Working with machines and peopleWorking with machines and people

Phase 4 - activities– Maintain and evolve the integrated solution

Basic systems support activities

– Instruct team members on software usage

Gatherrequirements


Integratecomponents

Deploy &maintain


Job requirements for anJob requirements for ane-science computing engineere-science computing engineer

Technical capabilities– Can work as the sole computer expert on the team– Understand ‘enough’ vocabulary, concepts,

data use, structure of algorithms, anddomain-specific standards

– Capable of distributed systems analysis and design– Knowledge and practice of rigorous software design

and development practices

Then … the sociological factors– Willingness to re-use existing technology – Willingness to maintain and evolve the system


Structure of the CS curriculumStructure of the CS curriculum

Systems work: operating systems networking compilers embedded systems

Graphics

Computational logicProgramming skillsSW engring methodsDatabase programmingScripting languagesData structuresAlgorithms

Artificial Intelligence work: language processing vision systems human-computer interaction robotics knowledge systems

Algorithmic complexity

HL systems work: middleware service-oriented architectures distributed & parallel systems database systems

Basic Principlesfor everyone

Specialisation

Applicable skillsfor e-science?


Assume a two part approachAssume a two part approach

Leverage good Computer Science curriculum– Identify applicable areas of study– Advise students to study across multiple

specialty areas

Fill the gaps with e-science training


Filling the GapsFilling the Gaps

My wish-list


Technical knowledge gapTechnical knowledge gap

Requirement– The project’s primary expert on most

computer-related topics

CS programme– Teaches “independent work” and

“intra-project teamwork

Teach ‘inter-project teamwork’


Inter-project teamworkInter-project teamwork

Share knowledge of similar tools and practices– E.g., using same software development tools (CVS, JBuilder, Ant)– A local person to talk with for a range of problems

Bio-research Centre

Project BProject A


Application domain gapApplication domain gap

Requirement– Understands ‘enough’ vocabulary, concepts, data use,

structure of algorithms and domain-specific standards

CS Programme– Lab exercises are small examples in easy-to-grasp domains

E-Science applications are not “easy-to-grasp”– Advise students to study introductory domain-specific courses

– Develop lab exercises in e-science application domains

– Recommend a single domain focus


Domain focusDomain focus

Can’t wear all these hats and do it well – focus!


Search, identify and use gapSearch, identify and use gap

Requirement– Identify and re-use existing third party technologies

CS Programme– Traditional emphasis on building all new software– Recently added exercises in “design re-use”

Add lab exercises covering– Re-use of pre-identified tools and components– Find and select candidate technology for re-use


Software methodology gapSoftware methodology gap

Requirement– Rigorous full life cycle software design and

development practices

CS Programme– Typically offer one course on software development

methodologies– Students not required to ‘practice this’ after

software methodologies course

Student interns in an active e-science project– Mandatory use of software development

methodologies


Development Tools

Methods and proven toolsMethods and proven tools

Spiral approach Extreme programming


The making of an e-Science engineerThe making of an e-Science engineer

CS +

+ +

Domain knowledge(just one)

Broadfundamentals

Rigorous SWdevelopment

methods

Internship in areal e-science project

=E-Science Computing Engineer


Thank youThank you

Thoughts and questions?

e-science data information and knowledge transformation thoughts on education and training for...

Documents

escience project

escience training slide

escience data information

escience computing

knowledge transformation

edikt project escience

system slide

role slide