Robert van der Drift NWO

118 

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(1)

Robert van der Drift

(2)

Call for Proposals: Big Software

Software in the Big Data era

(3)

Programme

13:00

Registration

13:30

Introduction Big Software

Robert van der Drift, Head of Computer Science (NWO)

13:45

Michiel van Genuchten (COO Vital Health Software)

‘The Impact of Software’

Jurgen Vinju (Professor Technische Universiteit Eindhoven

and group leader Centrum Wiskunde & Informatica) –

Challenges and Opportunities of Big Software-based

Innovation’

14:15

Start Pitch & Match sessions

(4)

Introduction Big Software

Robert van der Drift

(5)

NWO’s role in research funding

Ministry of Education, Culture and Science Ministry of Economic Affairs, Agriculture and Innovation other ministries

(e.g. Health, Foreign Affairs, Infrastructure & Environment)

private sector and public organisations

direct government funding

indirect government funding

universities

(incl. medical centres)

NWO institutes

other knowledge

institutes

2,3 Bn€ 500 M€ 4

(6)

The Challenges of Big Software

Individual systems have grown to millions of lines of code built from

many different technologies. Such systems have come to be known as

'legacy systems' -- systems that resist change.

Systems have become more and more inter-dependent, relying strongly

on third party components and services, giving rise to systems-of-

systems.

The operational context and actual use of software systems has become

increasingly complex and unpredictable.

In combination, it is increasingly hard to develop software that is

reliable, efficient, secure, and evolvable in a timely and cost-effective

manner.

Big Software welcomes ground-breaking research addressing these

challenges.

(7)

Who can apply

Professors, associate professors and assistant professors as well as

other senior researchers can apply if they:

– are employed at a Dutch University or a research institute

recognised by NWO, and

– have at least a master’s degree in science or engineering or an

equivalent qualification, and

– have an employment contract for at least the duration of the

application procedure and the duration of the research the grant

is applied for.

Role industrial partner(s):

In the project application the industrial and/or public partner(s) must

be listed as a co-applicant.

(8)

Business value for industrial partner(s)

Be involved in innovation projects as your part of innovation strategy

Gain access to state-of-the-art research and excellent research

groups, knowledge and results to be used in product development

Meet potential future employees who are the top talents in their

(9)

Type of project

Project team

Funded by

Contribution

Appointed through

2 researchers

(PhD-students or

postdocs)

1 funded by industrial

and/or public parties

1 funded by NWO

In cash

University

2 researchers

(PhD-students or

postdocs)

1 funded by NWO

1 fte – max 2

employees by

industrial and/or public

parties

In kind

University and

participating partner(s)

1 PhD-student or

postdoc

50% funded by

industrial and/or public

parties

50% funded by NWO

(10)

What can be applied for

a)

Hiring PhD’s and/or 2-year or 3-year postdocs based on fulltime position,

(incl. bench fee of € 5,000).

b)

Project-related equipment/software provided the costs are more than €

5,000.

c)

Other project activities, such as knowledge transfer, valorisation, and

costs to cover non-scientific personnel, travel/accommodation guest

lecturers and organisation of meetings/symposia.

Maximum budget for project-related equipment (b) and other project

activities (c) is no more than 10% of the total project costs, which will be

covered for half of the budget – 50%- by NWO. The other half – 50% -

should be covered by the partner(s)

Not eligible for funding:

Costs for computers, standard software and other costs which are

standard facilities of research institutes

(11)

When can be applied

The closing date for the submission of application to NWO is

Tuesday, 15 September 2015

,

14.00 hours

(CET + 01:00).

Application consists of:

Fact sheet (available at Iris system)

Application form – in English

Letter(s) of Commitment (LoC) – a pledge of financial support

(12)

Criteria

Scientific quality

Quality of the consortium

Knowledge utilisation

More information can be found in Call for Proposals (chapter 4.2).

Timeline procedure

Submission

Full Proposal

Peer

Review

applicant

Rebuttal

Assess-

ment

Decision

15 Sept,

(13)

Contact

Robert van der Drift

Head of Computer Science

Tel. +31 (0)70 – 344 07 75

Email

r.vanderdrift@nwo.nl

Rosemarie van der Veen-Oei

Programme manager

Tel. +31 (0)70 – 344 05 87

Email

bigsoftware@nwo.nl

(14)

More information?

(15)

Programme

13:00

Registration

13:30

Introduction Big Software

Robert van der Drift, Head of Computer Science (NWO)

13:45

Michiel van Genuchten (COO Vital Health Software)

‘The Impact of Software’

Jurgen Vinju (Professor Technische Universiteit Eindhoven

and group leader Centrum Wiskunde & Informatica) –

Challenges and Opportunities of Big Software-based

Innovation’

14:15

Start Pitch & Match sessions

(16)

Michiel van Genuchten

(17)

09/07/2015

30 columns in 2010-15

ASML, Bosch, RealNetworks

Philips, Honeywell

Hitachi, Uni of Queensland

Tomtom, Fujixerox

Microsoft, Shell

CERN, Oracle, Airbus, JPL,

Lint, Bayesian networks,

Vodafone India

(18)

09/07/2015

“To date, no significant

anomalies have revealed

themselves

(19)

LINT

Mobile

apps

Mars

Cabin swairplane kernel

l

lander

09/07/2015

Tokyo

railway

CERN

boson

MM

player

1

100

10k

1M

100M

Volume or

unique users

in #/year

100M

10M

100K

1M

10K

Size of sw

in LOC

ECU

CAR

MR

scanner

Airplane FMS

Workflow

engine

Car

Navigation

ASML

Oil

exploration

Solaris

Bayesian

Tanzania

(20)
(21)

Compound Annual Growth Rate for sw

Software seems to be growing with about 18 % a year

Irrespective of application, technology a.s.o.

Analysed 50 MLOC closed and 500 MLOC open source

No statistical difference in CAGR between the two

Relevant for both theory and practice

Genuchten, Hatton, IEEE Software, 2012, IEEE Computer 2013

Genuchten, Hatton, Spinellis, to appear, 2016

(22)

09/07/2015

sales volume and software size

14000000

12000000

10000000

8000000

6000000

4000000

2000000

0

2004 2005 2006 2007 2008 2009 2010 2011

year

sales volume

lines of code

sa

les

v

o

lu

m

e

14000000

12000000

10000000

8000000

6000000

4000000

2000000

0

li

n

e

s

o

f

c

od

e

(23)

Suggestions for research

To be falsified

Defect free ‘rocket science’ software exists

Software grows with about 18 percent a year

It’s software economics, stupid!

Also to be investigated

'legacy-systemen' - ongevoelig voor veranderingen - No

Quantifying the benefits of next gen sw technology

Explain the 1.18 growth rate (we have some ideas)

(24)

Jurgen Vinju

Center for Mathematics and

Computer Science

(25)

S

oftware Analysis And Transformation

Challenges and Opportunities of

Big Software-based Innovation

Jurgen J. Vinju

Centrum Wiskunde & Informatica

TU Eindhoven

INRIA Lille

Big Software Matchmaking Day

July 1st, 2015

(26)

Go Big Software!

(27)

The Software Medium

Erasmus

(28)

The Software Medium

(29)

The Software Medium

Internet

Tim

Berners-Lee

(30)

The Software Medium

yesterday’s ICT inventions

+

more and better software

=

(31)

Software

The Innovation Engine

from risky products to exploitable services

cost-of-development -> cost-of-ownership

big bang release -> incremental update

from pricy consultants to valuable experts

outsourcing -> core business

from quantity & complexity to quality & fl exibility

constraining people -> supporting people

(32)

Netherlands = Software

Programming

Languages

Formal Methods

Components &

Modules

Agile Processes

Operating Systems

Distributed Computing

Domain Specifi c

Languages

Model Driven

Engineering

Software Architecture

Database technology

Software Analytics

Software Testing

The Netherlands:

a global leader in

software and software

engineering

(33)

Big Software

Big Code

Big Process

Big Logs

Better Code

Better Process

Better Products

Research

Complexity => Opportunity

(34)

[

http://comphacker.org/comp/engl338/2015/01/28/visuals-of-wicked-problems/

]

(35)

Contextual Software Research

Great software and software research is contextual, tailor-made

Expert, local, domain knowledge is key to success

“Premature [generalization] is the root of all evil”

Focus on local urgency and local success factors

[Escher]

collaborate

for the

(36)

Contextual Software

Research

Building up general SE theory & methods as we go

The goal is incremental, but defi nite, improvement in SE

Disruptive innovation is

enabled

by better software engineering

Back to common sense;

stop following the hype

Use yesterday’s and today’s assets and experience

what if?

time-to-market one month sooner?

20% fewer bugs after initial release?

50% of the unused features not even

developed?

developers working on features, not bugs?

legacy code an asset instead of a risk?

how?

research!

(37)

Cross-cutting Contexts

Software Contexts are not silo’ed in industrial or public sectors

Example

: High-end Financial Services and Embedded Systems

High effi ciency

High integration complexity (third-party)

High product/service variability

Example

: Distributed (Big) Data and Meta Programming Systems

Intermediate formats

Marshalling and transformation

(38)

Software for Software

Research methods built as (re)usable software

automated data collection, analysis, reporting

code, process, trace analyses

questionnaires & monitors

Proof-of-concepts built as software

analyzing, transforming, generating, visualizing

integrated into existing environments & processes

There is no fi eld like ours where knowledge transfer {c,sh,w}ould be

organized so directly and faithfully, in either direction

only if research has

access

to the real code, real processes and real logs

only if industry has

access

to full and automated methods and experiments

(39)

CWI SWAT

Preventing and curing software complexity to enable higher quality

software systems, using automated software engineering methods

Know-how

language engineering

source-to-model

model-to-source

source-to-source

mining repositories

continuous delivery

distributed components

Domains

embedded systems

administrative

fi nancial

games

Connected & collaborative

research & education

industry & government

(40)

Roadmap ICT

Roadmap ICT draft has a

fi rst class software theme

reliable & fl

exible software systems”

Needs your voiced support

Stake our claim that software is a leading factor

economically

socially

academically

(41)

Yearly

Inclusive

Excellent speakers

Topical posters

Discussion

Networking

Thursday

December 3rd

Amsterdam

(42)

SWAT - S

oftWare Analysis And Transformation

Big Software

a new start for long term collaboration

(43)

Andy Zaidman

(44)

Software Engineering

Andy Zaidman

Big Software Matchmaking Event

July 1, 2015

(45)
(46)
(47)
(48)

Software

Analytics

Software

Artifacts

People

Running

System

(49)

How to improve reliability, maintainability, …?

Should we do code reviewing, static analysis, …?

How should we test?

What should we do with our technical debt?

Do components with biggest business value change

more, show more bugs, …

(50)

TU Delft coordinating initiative

for research, education and

training in data science and

(51)

Domain-Specific Languages:

enabling software engineers

to systematically design & apply DSLs

Cloud Programming:

composing computations using

mathematically solid foundations

reactive extensions interactive extensions

Software for Data Science

Enabling programmability of

big data analytics

Problem: programming multi-core distributed cloud machines with Von Neumann programming languages Solution: programming languages that abstract from hardware, close to domain experts Problem: data engineers and

scientists not trained as software engineers

(52)

Contact?

a.e.zaidman@tudelft.nl

@azaidman

(53)

Derek Karssenberg

(54)

PCRaster Research Team – Derek Karssenberg

Faculty of Geosciences, Utrecht University

Geocomputation: simulation of land surface processes

Domains:

• Hydrology (e.g. river flows)

• Land use change (e.g. bioenergy expansion)

• Effects of environment on health (e.g. exposure to air pollution)

Objectives:

• Develop concepts and software frameworks

• Distribute software: PCRaster

Team:

• Software engineers (C++) and PhD students in geoinformatics

• Domain specialists (water management, health, human geography)

(55)

Models should be programmable by domain specialists

• Domain specialists (e.g. hydrologists) are the model builders

• Need for software providing the building blocks

(56)

Challenges: (1) modelling heterogeneous systems

Problem:

Lack of concepts and software frameworks integrating fields and

agents

(57)

Challenges: (2) scalability

• Big data (e.g. remote sensing) is input to models

• Requires concurrent execution of models (parallelization, CPU, I/O)

Problem:

Lack of software framework that allows models built on desktop

computers to be run on in a high-performance computing environment

(without modification)

(58)

Our solution

www.pcraster.eu

Model building framework with built-in support for:

Agents

and

fields

(59)

Looking for new project partners (companies, research inst.)

• Our team develops concepts and/or software framework

• Partner provides problem from a particular domain, e.g.

• Health & environment

(possibility to join Global Geo Health Data Centre in Utrecht)

• Water management

• Ecology

• Crop growth, bioenergy

• Sensor networks

• …

Contact:

Derek Karssenberg

d.karssenberg@uu.nl

http://www.pcraster.eu

www.pcraster.eu

(60)

Mark Roest

(61)

1-07-15

• Established in 1996

• Around 25 employees, with academic background in mathematics and IT

• About 2/3 with a PhD

• Located in Delft

(62)

1-07-15

VORtech – Services

Scientific software engineering

Developing scientific software

Accelerating and improving scientific software

Consultancy on scientific software and mathematics

Maintenance of scientific software

Specific expertise

High Performance Computing

(63)

31-01-12

Typical customer code (no relevant proprietary code):

50k to more than 1M lines of code

Fortran, C, C++, Pascal/Delphi

1Mb – 4Gb data files

Interest to learn about techniques for modernization, porting

Possible role as intermediary to customers with case studies

Mark Roest

mark.roest@vortech.nl

06-4478 4413

(64)

Yanja Dajsuren

Centre for Mathematics and

Computer Science

(65)

Modernizing Big Legacy Software

Yanja Dajsuren, CWI

NWO Big Software Matchmaking Event

01-07-2015 Utrecht

(66)

Legacy software

Different modeling language

Different platform

More powerful hardware

(67)

Reo

(68)

Contact for comments and collaboration:

Tel:

+31(0)20 592 4007

Email:

y.dajsuren@cwi.nl

Address:

Centrum Wiskunde&Informatica

Science Park 123

1098 XG Amsterdam

(69)

Patricia Lago

(70)

SIMPLE:

So*ware Innova1on in

coMPLex Eco-‐systems

Research partners

Patricia Lago (VU)

Paul Grefen & Maryam Razavian (TU/e)

Marcel Worring (UvA)

Industrial partners (in kind or poten1al)

Serge Hollander (OMALA)

Sander Klous (KPMG)

Maikel Bouricius (GreenIT Amsterdam)

?

(71)

The Context

9 IS SUE 2: 2015

MORE CONTROL

PLEASE…

CON N ECTED TRAVELERS : S ELF S ERVICE

IN THE ERA OF CONNECTED TRAVELERS, SELF-SERVICE AND MOBILITY ARE KEY UNDERPINNINGS IN THE EVOLVING RELATIONSHIP WITH PASSENGERS.

22 AIR TRANSPORT IT REVIEW 22 AIR TRANSPORT IT REVIEW 22 AIR TRANSPORT IT REVIEW

GATEWAY TO

THE INTERNET

OF THINGS

AS WE STEP TOWARDS THE INTERNET OF THINGS, BEACONS ARE PROVING TO BE A CRUCIAL PART OF THE MIX IN GETTING PROXIMITY AND CONTEXT INFORMATION TO MOBILE DEVICES.

AIR TRANSPORT IT REVIEW 22

CONNECTED TRAVEL: PROXIMITY

(72)

The Context



A Big-‐so*ware environment is [K. Dorst]:

Open

: degrees of visibility and transparency (data,

services)

Dynamic

: Con1nuous change (evolving requirements,

technologies, opportuni1es)

Complex

: Shared benefits and shared responsibili1es

Networked

: Mul1ple stakeholders



A field never explored before

(73)

The Problem

How to create so*ware that realizes sustainable

innova1on

in such a complex environment?

Miss opportuni1es (novel business, iden1fy shared

op1miza1ons, predict emerging markets)

On economic, social, environmental

sustainability

What is the data (so*ware proper1es, influencing

changes, contextual factors like usage) that should be

gathered to support sustainable

change

Miss opportuni1es (iden1fy changing requirements,

perform technological adapta1on)

On technical

sustainability

(74)

The SIMPLE Approach: ingredients

Visual Analy1cs

4 Reasoning & Decision Making

BASE-‐X: iden1fy innova1on opportuni1es

4 complex eco-‐systems

So*ware and Service Engineering

4 sustainability

(75)

Look for co-‐Funding for 2 PhD

candidates

Visual analy1cs 4 SIMPLE so*ware

So*ware

Engineering

4

Big-‐so*ware

environments

Design Decision

Making

4

Sustainable

innova1on

(76)

Contact:

Patricia Lago

p.lago@vu.nl

(77)

Alexandru Iosup

(78)

1

@AIosup

dr. ir. Alexandru Iosup

Parallel and Distributed Systems Group

Won IEEE Scale Challenge 2014!

Scalable + Available +

High Performance

Parallel and Distributed

(79)

2

The Parallel and Distributed Systems group

Fun, International, Visible Team

also, Award-Winning

Join us in 2015!

(80)

3

Scientific Challenges for a Golden Age in ICT

How to massivize ICT?

• Super-scalable, super-flexible, yet efficient ICT infrastructure

• Data-driven feedback loops for end-to-end automation of large-scale processes

• Understanding actual use of dynamic, compute- and data-intensive workloads

• DevOps for evolving, heterogeneous hardware and software

(81)

4

Big Software for clouds and big data

• Data-driven feedback loops for scalable, high-performance, efficient operation

• Meaningful operational logs, including performance and reliability data

• Open-source software stacks for cloud computing and big data processing,

including Hadoop / Spark, Giraph / other graph-processing systems

• Continuous (re-)deployment of systems of systems (deep stacks)

• Analysis and action based of heterogeneous datasets and user requirements

• Analysis of trust and privacy in distributed stacks

• Benchmarking clouds and big data

A.Iosup@tudelft.nl

+31-15-2784433

@AIosup

http://pds.twi.tudelft.nl/~iosup/

https://www.linkedin.com/in/aiosup

PDS Group, Faculty EEMCS, TU Delft

Room HB07.050, Mekelweg 4, 2628CD Delft

(82)

6

Disclaimer: images used in this presentation

obtained via Google Images.

Images used in this lecture courtesy to many anonymous

contributors to Google Images, and to Google Image

Search.

(83)

Tommy van der Vorst

(84)

Research and strategic consultancy

Broadband/telecom ● human capital

(85)

GIS-data and -tools

Dashboards

‘Online reports’

Analysis modules

(Big) data sets

Dialogic Platform

Crawlers & scrapers

Surveys / webforms

Text mining

Customers &

stakeholders

Search

technology

Commercial data

sets & feeds

Partners

Researchers

(86)

Dialogic +

Researchers

who need a platform that provides user-

friendly, real-time and integrated data collection,

linkage, analysis and visualisation

Software suppliers

who can add smart algorithms to

the ‘treasure chest’, or see new applications of the

platform

And (obviously):

Customers

that have a monitoring-, evaluation- or

management question, that can be answered through

real-time analysis

(87)

Q & A

Tommy van der Vorst MSc

Researcher/consultant

dialogic.nl/vandervorst

nl.linkedin.com/in/tommyvdv

vandervorst@dialogic.nl

(88)

Aggregate Formatting Formulas lnteraction Metadata Recode Restructure Select Transform Values Variables http i 'api.dialogicinsight nlldata xml

(89)

Onderzoek en strategisch advies

Breedband/telecom ● onderwijs/arbeidsmarkt

(90)

GIS-data en -tools

Dashboards

‘Online rapport’

Analysemodules

(Big) datasets

Dialogic Platform

Crawlers & scrapers

Surveys / webforms

Tekstmining

Klanten /

stakeholders

Zoektechnologie

Commerciële

datasets/feeds

Partners

Onderzoekers

(91)

Dialogic +

Onderzoekers

die een platform nodig hebben waar

gebruiksvriendelijke en real-time dataverzameling,

koppeling, verwerking en visualisatie bij elkaar komen

Software suppliers

die slimme algoritmes kunnen

toevoegen aan de ‘schatkist’, of andere toepassingen

zien van het Platform

En uiteraard:

Opdrachtgevers

met een monitorings-, evaluatie- of

sturingsvraag hebben die te beantwoorden is met real-

time analyse

(92)

Q & A

ir.

Tommy van der Vorst

Onderzoeker/adviseur

dialogic.nl/vandervorst

vandervorst@dialogic.nl

(93)

Paris Avgeriou

(94)

7/9/2015 | 1

Big Technical Debt

Managing Technical Debt with Big Data

Prof. dr.ir. Paris Avgeriou - paris@cs.rug.nl

Software Engineering and Architecture Group

http://www.cs.rug.nl/~paris/

(95)

The problem

7/9/2015 | 2

› Technical Debt: Quality Trade-offs

› Expedient now, expensive later!

50-75% on evolution

(96)

The solution

7/9/2015 | 3

› Platform for Managing Technical Debt

Source Code Analysis

Maching learning

› Outcome

Valuation of internal qualities

Accurate effort estimation

Actionable figures in dashboard

(97)

Joeri van Leeuwen

(98)

Van Leeuwen A Global Software Telescope for Radio Astronomy NWO Big Software

Joeri van Leeuwen

(99)
(100)

Van Leeuwen Searching for pulsars with LOFAR and fast transients with Apertif NAC Winter Meeting Jan 2015

Further intensification in software, HPC & storage ( Peta -> Exa )

One of the most demanding domains on IT

SW/HW require new solutions for scalability, awareness, performance/W, etc.

Algorithms

Development driven for parallelization / new classes of HPC platforms

Relevant for other big data domains: MRI, seismic imaging, remote sensing, &c.

For 1B Eur SKA telescope; need to deliver a next-gen processing platform

Builds on LOFAR/Westerbork + many opportunities for industry.

Working on PPS with several industrial relationships mainly in the software domain so

that they qualify for the procurement around 2017.

In the heart of the ICT Roadmap of Topsectoren,

extreme streaming data

Business value of PPS is the applicability of the approach to the broad area of big data,

streaming data, etc. – a very good candidate for further valorization

(101)

Van Leeuwen Searching for pulsars with LOFAR and fast transients with Apertif NAC Winter Meeting Jan 2015

Dr. Joeri van Leeuwen

Astronomer, Principal Investigator

leeuwen@astron.nl

Dr. Gert Kruithof

Head of R&D

kruithof@astron.nl

(102)

Jeroen Keiren

(103)

Pitch Big Energy Data

Matchmaking event NWO 1 Juli 2015

Christoph Bockisch, Jeroen Keiren, Rody Kersten, Bernard van Gastel, Marko van Eekelen; Open Universiteit, The Netherlands/RU Nijmegen

The world-wide total energy consumption is steadily increasing, and energy is becoming a scarce resource. This also affects IT systems, to which a growing percentage of this energy drain is attributed. Furthermore, because hardware costs are decreasing, the operating costs of IT solutions are increasingly determined by energy costs. Reducing the energy consumption of IT systems can therefore offer both an immediate and long-term benefit to both the environment, through a lower consumption of scarce resources, and consumers, by lowering their energy bill.

In current practice effort is spent optimizing energy efficiency of hardware. However, the effects of software attract less attention. Still, software plays an important role in the energy consumption of software controlled systems.

Imagine I own an energy-efficient car. The actual energy consumption of the car depends on how heavy my right foot is, if I have a heavy foot, this results in a higher fuel consumption. Compare this to a software controlled system: the hardware (the car) can be energy efficient, but the actual consumption depends on the way it is used by the software (the right foot). Our key question is how to make this control software into a responsible driver.

We propose to investigate energy consumption of software controlled systems from different perspectives: (1) measure energy consumption of relevant devices at a high frequency generating a large amount of data; (2) use machine learning techniques to derive a model of the energy consumption of the hardware; and (3) use these energy models to determine and optimize the energy consumption caused by the software controlling these systems.

There are some existing approaches that consider the separate parts. The research challenge here is to provide an integrated, end-to-end approach and to validate the effectiveness of this process in practical case studies.

Currently, the software improvement group (SIG) has offered their support. IBM has shown an interest, and talks are currently ongoing. If you are interested in reducing energy consumption of software controlled systems, contact us!

Dr.ir. Jeroen J.A. Keiren

(104)

Pitch Big Energy Data

Matchmaking event NWO 1 Juli 2015

Christoph Bockisch, Jeroen Keiren, Rody Kersten, Bernard van Gastel, Marko van Eekelen; Open Universiteit, The Netherlands/RU Nijmegen

The world-wide total energy consumption is steadily increasing, and energy is becoming a scarce resource. This also affects IT systems, to which a growing percentage of this energy drain is attributed. Furthermore, because hardware costs are decreasing, the operating costs of IT solutions are increasingly determined by energy costs. Reducing the energy consumption of IT systems can therefore offer both an immediate and long-term benefit to both the environment, through a lower consumption of scarce resources, and consumers, by lowering their energy bill.

In current practice effort is spent optimizing energy efficiency of hardware. However, the effects of software attract less attention. Still, software plays an important role in the energy consumption of software controlled systems.

Imagine I own an energy-efficient car. The actual energy consumption of the car depends on how heavy my right foot is, if I have a heavy foot, this results in a higher fuel consumption. Compare this to a software controlled system: the hardware (the car) can be energy efficient, but the actual consumption depends on the way it is used by the software (the right foot). Our key question is how to make this control software into a responsible driver.

We propose to investigate energy consumption of software controlled systems from different perspectives: (1) measure energy consumption of relevant devices at a high frequency generating a large amount of data; (2) use machine learning techniques to derive a model of the energy consumption of the hardware; and (3) use these energy models to determine and optimize the energy consumption caused by the software controlling these systems.

There are some existing approaches that consider the separate parts. The research challenge here is to provide an integrated, end-to-end approach and to validate the effectiveness of this process in practical case studies.

Currently, the software improvement group (SIG) has offered their support. IBM has shown an interest, and talks are currently ongoing. If you are interested in reducing energy consumption of software controlled systems, contact us!

Dr.ir. Jeroen J.A. Keiren

Jeroen.Keiren@ou.nl

Twitter: @jkeiren

Prof. dr. Marko van Eekelen

Marko.vanEekelen@ou.nl

(105)

Susan Branchett

(106)
(107)

at

t

h

e

inte

r

face o

f

research a

nd I

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to

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n1

p le

m

en

t

eScie

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ce

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rojec

t

s

a

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echnology

su-

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ab

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f

or a

br

aa

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ange of

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sers

(108)

Business with research question?

www.eScienceCenter.nl

s.branchett@esciencecenter.nl

Susan Branchett

(109)

Joep de Ligt

(110)

Developing & sharing

bioinformatic

pipelines for big

genomics

Joep de Ligt PhD

Dept. Genome Biology

Hubrecht Institute

(111)

U.S. to analyze DNA from 1.000.000

peopl

e”

($215 million)

“UK

to sequence 100.000 patients

($160 million)

Sequencing is the easy part,

analysis is a big challenge

(112)

Arvados as a show-case

Cha

llenge:

Analyse

8 human genomes within 2 days

without buying hardware or writing custom

code

come:

8 genomes in

parallel

in 1 day and 18 hours

in the

cloud

, after 1 week of setup time

(113)
(114)

Key points

Fully Open Source

Docker images for software deployment

Parallel compute & no IO bottleneck

Reproducibility and sharing

More on the future of software development:

(115)

Adriënne Mendrik

(116)

Adriënne Mendrik

Post-doctoral researcher

Image Sciences Institute, UMC Utrecht

I’m a computer scientist specialized in medical

image analysis with 10 years of experience in

the field.

My current interests lie in finding approaches

that bridge the gap between medical image

analysis research and clinical practice

(117)

Why is it necessary to bridge the gap?

For example:

Brain tissue segmentation/quantification in MRI is

one of the oldest medical image analysis tasks.

Many algorithms have been proposed since 1985

Clinical practice today

still qualititative assessment,

clinician looks at MRI scan.

Medical image analysis is challenging:

Requiring highly reliable results

While dealing with large variations in patient

anatomy/pathology, scanner hardware/software and

acquisition protocols.

(118)

Complex problems have a higher chance of getting solved

by joining forces, both in data sharing and combining

algorithms.

I’m currently setting up a collaborative evaluation platform

for medical image analysis, that brings together clinical

researchers, technical researchers and companies.

Collaborators for setting up the platform: Stephen Aylward (Senior

Director of Operations, Kitware, North Carolina, USA), Bram van Ginneken

(Professor of functional image analysis, Radboud University Nijmegen

Medical Centre), and Guido Gerig (Professor of computer science,

University of Utah / New York University, USA)

Collaborate or brainstorm?

Software design is an important consideration for the platform. I’m

always interested in a good brainstorm about this. Who knows

what great ideas might come out. Feel free to contact me:

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