Welcome to #innovate11!

het collectief centrum van de Belgische technologische industrie

Innovate with Data

• Introduction• Dr. Elena Tsiporkova, Senior Technology Advisor, Sirris

• Data Driving Innovation• Gabriel Reid, Senior Software Engineer, TomTom

• Traffic data and mobility content• Dr. Steven Logghe, Chief Traffic, BE-Mobile

• Lily, Smart Data at Scale made Easy• Steven Noels, CEO, Outerthought

het collectief centrum van de Belgische technologische industrie

Elena Tsiporkova & Tom TourwéICT & Software Engineering

Data is fueling the knowledge- based economy and society

Data is omnipresent …

• Web of Data

• Social Media: LinkedIn, Xing, Twitter, Facebook, …

• Media and Publishing: news & events sites, blogs, discussion forums, …

• Data (Research) Repositories: Wikipedia, GoogleScholar, DBLP, PubMed, CiteSeer, …

• Enterprise Data Web: enterprise-related, business, financial and regulatory data published by commercial organisations

• Government Data for Citizens: institutional, infrastructure, economical, political, legal and social information provided by governments

… and more data

• Internet of things / Web of Events

• Dynamic Traffic Monitoring: traffic management, smart taxi’s

• Video Surveillance Systems: security, ambient assisted living, …

• Smart Factories: monitoring and control of manufacturing processes, e-maintenance, energy-aware & agile manufacturing

• Smart buildings: energy & water management, smart sensors & smart grid

• Crisis Management: emergency dispatching, flooding monitors, …

… and more data

• Health• Medical imaging• Real-time video feeds created during surgery• Permanent (mobile) telemonitoring• Patient records

• Life sciences• DNA sequencing • High-throughput screening

• R&D• LHC@CERN generates 15 petabytes/year

… and even more data is becoming available

• Every device is or will be connected• Mobile phones, computers• Cars, bikes• Fridges, energy meters

• Creation of data as a by- product• Usage data, logs

• Growing user- generated content• Social media, blogs, discussion forums, ...

• Wide adoption of the open data initiative by governments• Linked Open Data

• Technology evolution• (Elastic) cloud computing• Parallelism on commodity hardware• Cheap hardware and network connectivity cost

Data creates opportunities …

• Improved Decision Making

• Uncover hidden insights and infer additional knowledge from data

• Enable advanced visualization of trends and patterns

• Reduce information overload and target proactive information delivery

• safety-critical environments / financial domains: decisions need to be made in a matter of seconds, and nobody has a global overview of the exact situation

• e-maintance: based on historical data predict when a machine will need to undergo maintenance due to failure of the hardware

… and more opportunities

• Innovations in business models, products and services

• Free products & services in return of data ownership• e.g. free restaurant inquiry phone service

• Transition from selling a product to selling a service• e.g. renting of machines equipped with sensors

• Emergence of data market places

… and even more opportunities …

… as customers, consumers and citizens are becoming both direct and indirect beneficiaries of data

• Large scale genetic data personalized therapies tailored to the patient’s genetic profile

• Open government data policy higher-level of engagement of citizens with the government

• Real-time traffic information reduced travelling time and fuel consumption

• Commercially valuable user information better match between products and customer needs

Data poses challenges …

• Technological• Scalable data storage and processing• Data format standardization (RDF, linked data)• Data integration from heterogeneous sources e.g. public data with

purchased data with proprietary data• Adaptive software supporting data acquisition• Real Time Information Processing• Event Recognition for Intelligent Resource Management• Manage a large population of devices• Decentralized intelligence• Discovery and mapping of real, digital and virtual entities• Consider information from human behaviours and multi-modal

interactions• Act on behalf of the users’ intentions• …

… and more challenges …

• Organisational• A shortage of talent, which is difficult to create, taking years of training:

• people with deep expertise in statistics and machine learning• multi- and cross-discipline expertise

• Managers and analysts who know how to operate companies by using insights from data

• Gathering all the internally available data in a central place• Adequate infrastructure needs to be put in place

• Policy- related• Privacy• Security• Intellectual Property Rights• Liability• Ethics

Two community- driven Big Data initiatives

Linked Open Data

Linked Data - Connect Distributed Data across the Web

• Web of Data• loosely structured & disconnected data• difficult to integrate & query

• Linked data is a way of publishing data on the Web that:• encourages reuse• reduces redundancy• maximizes its (real and potential) inter-connectedness• enables network effects to add value to data

• Community project with W3C support• Began early 2007

How does it work in practice?

• Consider existing open data sets• Wikipedia, Geonames, WordNet, DBLP bibliography, …

• Make them available on the Web in RDF format • use the concept of triples to describe relationship between data

• subject-predicate-object

• Interlink them by setting RDF links between data items from different data sources

about

written by

written by

about

subject of

author of

Open Linked Data Cloud

DBpedia

• A community effort to extract structured information from Wikipedia and to make this information available on the Web.

• Allows users to ask sophisticated queries against Wikipedia, and to link other data sets on the Web to Wikipedia data.

Initiated by people at the Free University of Berlin and the University of Leipzig, in collaboration with OpenLink Software

1000 Genomes Project

No two humans are genetically identical

• Human genetic variation refers to genetic differences both within and among populations

• The 1000 Genomes Project is the first project to provide a comprehensive resource on human genetic variation

• The study of human genetic variation has• Evolutionary significance

• helps understand ancient human population migrations as well as how different human groups are biologically related to one another

• Large impact in medical genomics research• helps identify genetic causes of diseases which occur at a greater frequency in

people from specific geographic regions

1000 Genomes Project

• Largest data collection project as yet undertaken in biology

• A community resource project • launched in January 2008 with the participation of 75 universities and

companies from around the world• with the aim to

• sequence the genomes (DNA sequencing) of at least one thousand anonymous participants from a number of different ethnic groups

• release data rapidly for the benefit of the scientific community

DNA sequencing

• The main role of DNA is the long-term storage of genetic information used in the development and functioning of all living organisms

• The information in DNA is stored as a code made up of four chemical bases: Adenine (A), Guanine (G), Cytosine (C) and Thymine (T)

• Determing the order of these bases is called DNA sequencing

• The human genome consists of approximately 3 billion DNA base pairs

The results from the pilot phase …

• … have revealed some 15 million gene variants, more than half of which had never been observed

• … and generated datasets of over 50 terabytes, corresponding to almost eight trillion DNA base pairs of sequence data

• The results also highlight the fact that there is still an enormous amount left to learn

• In its next phase, the project will expand its sequencing efforts further to 2500 individuals

Data Research @ Sirris

Optimise the choices available

Pro-actively push relevant information

Present information in a user-specifc and context-aware way

Provide the right dosage of information at the right time

Implement intention-aware adaptive automation (trading of control)

Keep the user in control

Pro- active decision support in data- intensiveenvironments

ASTUTE aims …

• … to develop a platform for building embedded products that capture and act upon user intentions …

• … taking into account the user’s context (i.e. user environment and all factors influencing the user performance) and state (i.e. aspects determining the ability of the user to perform in a given situation) …

• … in order to turn the overwhelming amount of information into targeted, context-sensitive advice …

• … so as to focus and enhance the user's attention and decision making

ASTUTE application domains

• Smart Embedded Emergency Dispatching System

A decentralized solution for emergency management

• Embedded Driver Infotainment System

An intelligent visual system, to be run on personal positioning and navigation devices

• Intelligent Cockpit

An empathic system to support anticipation during flight operations

• Virtual Control Room

Smart control room systems for data & event intensive applications for buildings and manufacturing management

… modern economic activity, innovation, and growth simply couldn’t take place without data …

Conclusions

What follows …

• Data Driving Innovation• Gabriel Reid, Senior Software Engineer, TomTom

• Traffic data and mobility content• Dr. Steven Logghe, Chief Traffic, BE-Mobile

• Lily, Smart Data at Scale made Easy• Steven Noels, CEO, Outerthought