© University of Reading 2009 www.reading.ac.uk

Mathematics for the Digital EconomyBuilding Stones

Roland Potthast, Reading, UK

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The Digital Economy

“Novel design or use of information and communication technologies to help transform the lives of individuals, society or business.” (EPSRC)

New

Technology

Behaviour

Lifestyle

Highly multidisciplinary: high impact

Technology + People/Society = Resonance = New Opportunities

+ =New

Products

+ Services

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Math and Digital Economy

People Area/Data Structures

Modelling AlgorithmsTeam

Products Services

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PeopleExample CV

(Potth.) Bridging the Gaps:

- Education Physics and Mathematics

- Project Manager in IT Industry

- Trainer for Siemens ICN

- Partner for Spin-Off Companies in IT/Maths

- Mathematics Reader/Professor

- Team Builder

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Math and Digital Economy

People Area/Data Structures

Modelling AlgorithmsTeam

Products Services

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Becoming data rich…

Data from many sources– Behaviour of people and

groups– Transactions B2C– Communication/networking

P2P– Outreach and information– Participation– Monitoring/surveillance– Measurement– New Imaging Technologies

• In many areas we are data rich but model poor!

• Especially when the “atoms” are people

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Math and Digital Economy

People Area/Data Structures

Modelling AlgorithmsTeam

Products Services

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… the information revolution

The information age just started!

• More information and data on various levels than we could ever imagine: economy, society, science

• Sincere demand of models, order, understanding, monitoring, control

• Scaling, Micro vs. Macro Analysis,

• Hierarchy of Models,

• New Mathematics, continuous or discrete!

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Math and Digital Economy

People Area/Data Structures

Modelling AlgorithmsTeam

Products Services

Companies

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Online commercePure playsExistingFinance

Social interactionNetworkingCommerce

ServicesCommunicationsUtilities…

Some Digital PlayersNow part of the fabric of our lives

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Math and Digital Economy

People Area/Data Structures

Modelling AlgorithmsTeam

Products Services

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Customer Relationship Management

• Explosive growth though IT “reach” … 105-106 customers

• Using behaviour to discover/define addressable groups

• Highly responsive : near real time

• Finding markets that are predictive

• Predicting behaviour and churn

Maths Inside

Unsupervised discrimination over data bases: EM algorithm and its variants

Hidden Markov models for rates of transition between behavioural states

Supervised discrimination: Bayes factors and probability theory

Discrete searches model optimisation: genetic algorithms

Simulation : Agent Based Modelling e.g. possible spin out from Maths@UoR

SORTING OUT THE CROWD

EXAM

PLE

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• Exploding number of new imaging technologies

• Aging society with new needs of diagnostics

• Societies growing strongly in developing countries

• Time-resolved multi-source data, need for model hierarchy and evaluation

Cognitive Neuroscience / Healthcare

Maths Inside

Discrete Theories and Field Theories, Integro-Differential Equations

Medical Imaging, Monitoring, Data Analysis, Remote Analysis

Automated algorithms, Remote Health Care

Inverse Problems, Data Assimilation, Stochastic Estimation Theory

New Multi-Level Structures, Models, Analysis and Numerics

EXAM

PLE

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Monitoring and security

• Searching for aberrant or low probability events

• Classifying behaviour

• Prioritising for different types of intervention

• Supply Chain Management and Monitoring

(SiroTechnologies, EADS, VW, RLS, KMW etc)

• e.g. Health-check data in the home and online (www.brainpanrel.co.uk bid to LLHW prog),

• e.g. fraudulent behaviour detection for online poker companies (Valeo Associates Ltd)

Maths Inside

Bayesian multiple hypotheses testing

Forecasting trends/uncertainties : application of MCMC in adaptive forecasts

Supervised discrimination: Log Bayes factors / probability theory

EXAM

PLE

SiroTechnologies

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Graphs and Networks

• The growth and evolution of dynamical networks

• Small world and range dependent graphs– e.g.

• Inverse problems: calibrating graph parameters from data

• Dealing with very large networks – sensitivity to data

• Comparison of alternative concepts/models

Maths Inside

New classes of random graphs

Numerical linear algebra & spectral theory: clustering within networks

Generalised clustering methods, e.g. SVD-based for stochastic graphs

Maximum likelihood representations of data within classes of graphs

Stability of results with respect to data, Inverse Problems for Graphs

EXAM

PLE

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Behaviour based profiling segmentation

• Segmenting populations with behavioural metrics for product and service development

• e.g. Smart 24/7 energy metering data in the home – “current insight” mining pilot project

• e.g. Analysing m-banking data in Africa (current 2M customer pilot UoOx start-up ARK MF Ltd)

Maths Inside

Unsupervised discrimination over data bases: EM algorithm and its variants

Markov models for rates of transition between behavioural states

Supervised discrimination: Bayes factors and probability theory

Discrete search algorithms: genetic algorithms

Simulation : Agent Based Modelling

EXAM

PLE

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Fuel Cell Quality Monitoring

• Monitoring of current distributions in fuel cells via magnetic tomography

• Development of fuel cells and fuel cell stacks, energy patterns, applications

• Production and quality control

• Maintenance, Diagnostics, Control

Maths Inside

Integral equations and Potential Theory, PDE, Numerical Analysis

Inverse Problems, Imaging, Data Assimilation, Optimization Algorithms

Data analysis, Large ODE systems, FEM/FIT/BEM

Unsupervised discrimination over data bases: EM algorithm and its variants

EXAM

PLE

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Math and Digital Economy

People Area/Data Structures

Modelling AlgorithmsTeam

Products Services

KT

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KT Opportunities

• Many companies have these topics

• Need for new concepts and new practical applications

• Data is very often confidential which is a barrier

• The maths community would benefit from anonymous problem banks

• Algorithms and methods are difficult to protect– Secrecy rather than

publication

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The Horizon Hub• University of Nottingham

and “spokes” at Reading, Cambridge, Exeter

• Highly multidisciplinary:– ICT, – Maths,– Business, – Social science– Art, Performance

• Starts is a few months• Large and growing

number of industrial partners

• Grindrod, UoR, will manage an interface with UK advertising companies on behalf of the national DE community – The IPA

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Math and Digital Economy:Future Trends

Algorithms

People Area/Data Structures

ModellingTeam

Products Services

• Integration of diverse and multilevel technologies

• Simplification and complexity

• Individual empowerment: customers’ perceptions and activities and ideas becoming paramount

• Commerce P2P exchange

• Control, Security, Sustainability

• Ethics and individual/subjective issues

Thank you!