small big data - tno...introduction 3 tno presents the small big data congress, in cooperation with...
TRANSCRIPT
12 SEPTEMBER 2016AMSTERDAM ARENA
SMALL
BIG DATA CONGRESS
INCLUDING: LAUNCH OF THE FIRST BIG DATA HUB IN THE NETHERLANDS
CONTENTS
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HAL24K – MAKES CITIES SMARTER 4GEMEENTE AMSTERDAM – DATAPOINT 5YNFORMED – PUBLIC VALUE THROUGH DATA SCIENCE 6VORTECH – VOR ANALYTICS 7 TNO – PREDICTIVE AND PRESCRIPTIVE POLICING 8QUANTBASE – SUCCESSFUL BIG DATA STARTS SMALL! 9ZYVO – RESHAPING THE FUTURE OF HR 10RIJSKWATERSTAAT – FLOATING CAR DATA 11TNO – VIDEO ANALYTICS FOR DETECTION OF BEHAVIOURS 12 OF INTEREST ENDEAVOUR MEDIA – DIGITAL PRESENCING 13GEMEENTE ALMERE – DATA DRIVEN CRIME PREVENTION 14TNO – EMERGING NEEDS TO EXPLOIT OPERATIONAL DATA 15 IN THE HIGH-TECH INDUSTRYPHILIPS – MR-EYE – FROM MACHINE DATA TO CUSTOMER 16 CENTRIC SOLUTIONSCBS – DATA FOR SOCIETY – FROM OFFICIAL STATISTICS TO 17 DATA SCIENCEXILION – CROSS-BORDER MARKETS EXPLORERS WITH BIG DATA 18TNO – STREETWISE – CONTINUOUS SAFETY VALIDATION 19SECWATCH – INTERNET OF THINGS AND SECURITY 20MIC – UETP REALTIME OMNIPRESENT INTERACTION 21WE4SEA – EFFICIENCY SOLUTIONS FOR SHIPS 22GUTS – REGULATING HONEST, RELIABLE TICKET SALES 23MYCARDWALLET – SMALL BIG DATA AND MOBILE 24 CUSTOMER CONTACT
PI·LAB – RESPECT4U 25KADASTER – GEO DATA PLATFORM OF THE FUTURE 26TNO – POLICY MAKING IN THE ERA OF BIG DATA 27NETHERLANDS ESCIENCE CENTER – SIM-CITY – AN E-SCIENCE FRAMEWORK FOR ASSISTED DECISION SUPPORT 28UVA / AIR FRANCE KLM – RESEARCHING MULTIDOMAIN BIG DATA SHARING INFRASTRUCTURE MODELS 29AIR FRANCE KLM – THE PASSENGER EXPERIENCE CLOUD 30TNO – UNCERTAINTY & MULTI-STAKEHOLDER COLLABORATION 31 IN BIG DATA SOLUTIONSWUR / NETHERLANDS ESCIENCE CENTER – SUMMER IN THE CITY 32UNIVERSITY OF APPLIED SCIENCES – CREATING MORE 33 VALUE WITH DATA XOMNIA – FULLY CONNECTED TRAVELLING 34TNO – RESPONSIBLE DATA INNOVATION 35OBILYTICS – HOW TO OFFER THE RIGHT PRODUCT TO THE 36 CUSTOMER AT THE RIGHT TIME RIGHTDE FIETS LOCATIE – SHARE BIKES FOR A BETTER FUTURE! 37TNO – TRAVEL TIME PREDICTION FOR INCIDENTS: DEEP 38 LEARNING FROM EXPERIENCE BIG DATA REPUBLIC –DURABLE AND CUSTOMER-ORIENTED: 39 PREDICTING THE SUPPLY OF WIND ENERGY GEMEENTE AMSTERDAM – FIXXX – SNELLE INNOVATIE DIE 40 WERKT! TNO – ICONBID – IDENTIFYING MAIN CONGESTION LOCATIONS 41 USING TRAFFIC BIG DATA
INTRODUCTION
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TNO presents the Small Big Data Congress, in cooperation with the Big Data Value Center and the Amsterdam Innovation ArenA.Are you interested in real added value of Big Data? Would you like to know all that is happening in the area of applied research and development and what has been achieved with Big Data?The Small Big Data Congress aims to stimulate fascinating combinations of unusual suspects originating from business, government and research institutions. Get a Big Data shower and be inspired by the huge variety of applications.
The presentations of the Small Big Data Congress provide a storm of two minute pitches showing all kinds of initiatives with Big Data in the Netherlands. This booklet provides an overview of the pitches of the congress.You are warmly invited to attend the next Small Big Data Congress to be organized in 2018!
Best regards,
Jessica DoornBusiness Consultant Data Science at TNOKeynotes concerning the Big Data ecosystem in the Netherlands
addressed how value can be created from the latest developments in Big Data– Franc Weerwind, mayor of Almere and VNG lead information
policy; The importance of digitization and cooperation in the field of (big) data between municipalities.
– Henk van Raan, Chief Innovation Officer Amsterdam ArenA and Jan Wester, director Big Data Value Center; The collaboration between the Amsterdam ArenA and the Big Data Value Center as first Big Data Hub in the Netherlands.
– Henk Jan Vink, director Networked Information TNO; Commit2Data and how valorization can be achieved with big data hubs.
– Sander Klous, professor Big Data ecosystems of UvA and global lead KPMG; the importance of big data and how data governance is set up for the Big Data Hub.
TNO brings together expertise in structured and unstructured data, data infrastructure and data governance, with a particular emphasis on data analytics and semantic technologies. In a world of connected people and devices, data, both complex data and Big Data, is becoming of ever greater importance. Our core mission is to make sense of this data, to help companies and authorities to manage their data and to enable them to make better decisions from both a societal and a business perspective.
HAL24K MAKES CITIES SMARTER
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CONTACTJan Willem van HolstM +31 6 208 918 74E [email protected] hal24k.com
See today, shape tomorrow
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DATAPOINTHow a data and service oriented architecture of government information systems will enable us to create user centered solutions and improve data integration and collaboration in the city
CONTACTBerent DaanDirector of research information and statisticsCity of AmsterdamE [email protected]
YNFORMEDPUBLIC VALUE THROUGH DATA SCIENCE
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Through our years of consulting experience in the public sector we understand what needs to change and what the challenges are. We know the world of professionals, managers and administrators. Making better use of data is not a ‘nice view’ or a ‘pretty picture’ to us. Our data solutions deliver public value. We are not just a consulting firm: our aim is to develop together with our clients practical and sustainable solutions, resulting in real change. To this end we use the latest skills and capabilities in the field of information technology and data science.
CONTACTYnformedRamstraat 273581 HD UtrechtThe Netherlands
T +31 30 341 01 10E [email protected]
VOR ANALYTICSANALYZING THE WORLD AROUND US
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CONTACTJok Tang Sr. mathematical consultant at VORtechE [email protected] www.vortech.nl scientific software engineers
Electricity market
Water management
Financial models
Trafc management
Time-series AnalysisData Assimilation
Monte Carlo Simulations
Machine Learning
PREDICTIVE AND PRESCRIPTIVE POLICING
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Predictive Policing (a term from the US) is nothing more than police operations based on predictions of crime. Predicting crime is difficult, just like predicting the stock market. However, the police have an abundant source of big data on past crimes, and big data never runs out. On the contrary, it grows exponentially. By crunching this data with sophisticated algorithms, the police will be able to predict crimes. In other words, Predictive Policing might enable the police to act before the crime has happened. However, imagine you’re in such a red box and nothing has happened. What now? Should you wait a bit longer? How much longer? Five minutes? Ten minutes? And what must you do? If you could answer these kinds of questions you would probably get even more efficiency out of the predictions. While Predictive Policing tells you what could happen if you do nothing (an incident will probably occur in this box), it would be much more effective to tell you what you should do to prevent the incident. What is the effect of driving around in this box for five minutes? And what if you stay there longer? Prescriptive Policing provides such context-aware and evidence based action strategies and has the promise of better and targeted interventions. CONTACT
Selmar SmitM +31 6 468 474 35E [email protected]
SUCCESSFUL BIG DATA STARTS SMALL!
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Quant Base makes Big Data analytics work for you, with the right pace, the right investments and with tangible results for you!
Thanks to our steady approach one of our clients is now able to process and analyse over 6 million transactions a day. By rearranging their way of working with data they still do not need to invest in extra hard- and software.
HOW DO WE DO THIS?We start with a pilot. You have goals that can be concrete or still be ambiguous. Based upon these wishes we start with a quick but thorough look at your data and the data-infrastructure in your organisation. Within a couple of days we deliver the first results. This gives you the input to make your needs more concrete and get you an idea of the possibilities. Based upon these results we help you to define the route to get there. This means no big upfront investments in big data hardware or tooling! With our way of working: You keep in control, you start small, but you get big results optimizing the return on investment.
We from Quant Base think this is very interesting for the SME market and also the bigger corporates. We can help you out, so contact us!
CONTACTBob van KootenT +31 88 058 87 00E [email protected] www.quantbase.eu
FLOATING CAR DATATOWARDS APPLICATION IN TRAFFIC MANAGEMENT
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The expectations for the use of floating car data (FCD) in traffic management are big. These are based on the cost-effectiveness of FCD collection compared to currently used data sources (loops, radars and the like) and on origin-destination information that FCD extra offers. Nevertheless, the number of applications of FCD still is limited. This has a number of causes. Firstly, FCD as input source is of completely different nature than the sources used now. The main difference is that the current data collection refers to all passing vehicles, while FCD consist of a sample. Furthermore, current data are recorded on a fixed location while FCD consists of observations about routes (‘trajectories’). Both dates forms require processing before they can be used as input for applications. The challenge is to apply appropriate adaption on FCD taking account of the differences with current data sources. A second challenge is to base traffic management applications on the characteristics of FCD (speed instead of flowy). Because the samples of FCD are still relatively small, data fusion with other resources is necessary. Enter for ‘big data’ analysis! This plea is to be transparent in all analyses and calculations. Practical Trial Amsterdam aims to respond to this plea.
CONTACTHarry van OoststroomT +31 6 341 474 22E [email protected]
Floating Car Data (FCD)
VIDEO ANALYTICS FOR DETECTION OF BEHAVIOURS OF INTEREST
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CONTACTDr. Gertjan BurghoutsE [email protected]
In large crowds of people, it is very hard for humans to see everything that is happening. Imagine that you would have to spot the one person that leaves a bag behind, or the two people that are fighting, or the person that collapses after not feeling well. Video analytics is software that runs on a computer or server in the control room and analyses video streams from many cameras. It looks for particular behaviours that are of interest to the operators. The video analytics software provides suggestions to the operators in the control room where interesting behaviours may be happening.
DIGITAL PRESENCINGTHE ALL-IN-ONE MOBILE MARKETING PLATFORM DEDICATED TO MOBILE TAGGING
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CONTACTBabak MonfaredE [email protected]
The Cee platform is a complete package for businesses to reach and engage with the smartphone users. Although there are many alternatives none is as complete on the front-end & on the back-end.
We have gathered all the newest technologies for scanning and creating content for the sole purpose of reaching smartphone users and turning them into customers.
EMERGING NEEDS TO EXPLOIT OPERATIONAL DATA IN THE HIGH-TECH INDUSTRY
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CONTACTBas HuijbrechtsE [email protected]
MR EYEFROM MACHINE DATA TO CUSTOMER CENTRIC SOLUTIONS
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MR Eye provides insight in product utilization and product behavior by leveraging machine data which in turn can be used to provide Customer Centric Solutions:– Product: enabling reliability improvements and technical
optimizations– Service: enabling uptime improvements by remote monitoring,
corrective and prognostic service– Customer: utilization information enables optimization of the
customer workflow by providing consultancy, options or new features
– Daily updates– Over 6000 sites connected– 2 Years historical machine data– Machine Data of 300 M scans stored– Raw data insertion of 100 TB/year – Info extracted in 5 seconds
CONTACTKrelis BlomE [email protected]
DATA FOR SOCIETYFROM OFFICIAL STATISTICS TO DATA SCIENCE
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CBS, the Dutch office for official statistics, is actively exploring opportunities for incorporating big data in official statistics production. This may lead to improved, cheaper and/or completely new statistical information. Official statistics are used by government institutions, enterprises, researchers and the public at large for decision making, scenario planning, academic research and more. CBS therefore pays a lot of attention to make sure that statistical products depending on big data meet its usual high quality standards. Quite a number of different sources have been investigated: road sensors, mobile phone data, social media, scanner data, satellite data, data crawled from the internet, ... We found that big data comes with a lot of new challenges in methodology, IT, data acquisition, and conceptualisation. In addition, new ethical and legal questions emerge as well as questions on skills and resources. All these challenges call for collaboration and open innovation. CBS looks for partners that contribute data, skills, resources and/or who may benefit from big data-based statistical information.
CONTACTBarteld BraaksmaE [email protected]
CONTACTAd Popper
Xilion webservices B.V.De Vlei 241852 KE HeilooThe Netherlands
T +31 6 423 015 88F +31 72 301 05 42E [email protected] www.xilion.eu
CROSS-BORDER MARKETS EXPLORERS WITH BIG DATA
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Money is confidence, knowledge is power
How can Big Data, supply added value to many SME’s with easy accessible customized knowledge about foreign markets? Till now, only multinationals and big enterprises have the resources to acquire and manage customized market knowledge. There is much to gain for SME’s regarding cross-border trade and much easier than you might think.The approach is offering filtered and translated trade related knowledge from Big Data of foreign Governments and Research institutions. It is published in the cloud and made accessible via an easy to use market app. This way, all the benefits from Big Data marketing management are available for everyone.The first delivery of our approach are market apps to disclose France. The unperceived revenue opportunities with France are € 3.5 billion per year! Our market apps to disclose Germany will become available.In summary, the unique added value of Big Data solutions in the field of cross-border trading is the scalability to the SME segment. Providing revenue opportunities who lead to turnover, profit and finally economic prosperity.
Explore economic areas Enterprises explorer Explore retail markets customized
STREETWISECONTINUOUS SAFETY VALIDATION
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Cooperative and automated driving are coming fast. Conventional functional safety tests do not suffice for these systems because we cannot drive enough testing kilometers. In addition, it is difficult to determine whether our test cases are representative or complete. Nobody knows what the cars will meet on the street, how often critical scenarios occur and what the underlying statistics are. This requires a different approach to safety: continuous on-road monitoring of car and user behaviour. We create a feedback loop from system use to development to monitor road performance and safety. By analysing raw car sensor data to classify and characterise manoeuvres and scenarios, we can identify and correct risks before they cause incidents. To process the large amount of data we use a combination of deterministic and machine learning algorithms in the following architecture:
Streetwise provides the tooling to start with continuous safety validation, from data acquisition to machine learning algorithms for safety evaluation. The resulting scenario will provide an invaluable tool for functional safety testing.
CONTACTJan-Pieter PaardekooperE [email protected]
INTERNET OF THINGS AND SECURITYBIG DATA LEAKAGE
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CONTACTHenk-Jan AngermanT +31 36 536 75 73E [email protected] secwatch.nl
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CONTACTDan VeenI We4sea.com
WHAT’S GUTS? REGULATING HONEST, RELIABLE TICKET SALES
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CONTACTJeffrey van der EijkE [email protected]
RESPECT4UA PRIVACY FRAMEWORK FOR DATA DRIVEN INNOVATIONS
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– RESPECT4U offers a privacy framework that enables an encompassing perspective on privacy
– RESPECT4U addresses legal, organisational, technological and societal aspects of privacy
– RESPECT4U offers tools, instruments and approaches that help fulfilling functional requirements for privacy respecting services, products and processes
CONTACTMarc van LieshoutE [email protected] www.pilab.nl
WHAT CAN BE EXPECTED IN THE NEARBY FUTURE
– Connecting Data semantic and Geografical data will be interlinked starting with the the Key Register Addresses and the Dutch Kadaster Map.
– Richer User Interface; more visualisations like 3D, Improved development Portal, More Meta Data, improved findability via Google and other search engines and more to come.
– Self Service enabling data providers to load their data sets automatically including an Semantic mapping functionality.
– Back Report Service enabling reporting back any irregulatiries on the map.
– Quality dashboard offering improvement processes to optimize data quality.
CONTACTTim NelissenE [email protected] www.kadaster.nl/dataplatform
GEO DATA PLATFORM OF THE FUTURE
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DEVELOPMENT OF A SERVICE CENTER
– Manage and unlock information for anyone – Facilitate the capability of answering social Geo questions
DEVELOPMENT OF A DIGITAL GEO DATA PLATFORM
– Guarantuee the quality of the information – Capable of creating relatinos between different datasets – Provide high service levels – Capable of handling different input and output formats for different target audiences – Provide relevant meta information
CONTACTPetra VermeulenScientist Innovator TNO Strategy & PolicyT +31 88 866 36M +31 6 216 187 31E [email protected]
POLICY MAKING IN THE ERA OF BIG DATA
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Big Data promises better, tailored and real-time insights. It provides policy makers with opportunities to address societal challenges with better tailored solutions and policies. Governments want to make use of Big Data and develop innovative solutions but these solutions need to be validated and serve all citizens and businesses. Therefore, they need to grasp the opportunities as well as the limitations of the use of Big Data. For example, the Crime Anticipation System (CAS) set up in Amsterdam helps the Amsterdam Police with predictive policing. However, the data gathered through CAS has a difficult relationship with reality: how to use this data in court; and what about the privacy and ethical consequences of the system?
TNO’s Policy Lab offers policy makers opportunities for policy making in the era of Big Data, and address its challenges. It develops new and innovative methodologies for policy making using Big Data and experiments with these methodologies on relevant policy issues together with policy makers. It presents the opportunity to predict the consequences of societal trends, to experiment with new ways of policy making, while ongoing monitoring and evaluation allows continuous tailoring of policies to a specific problem.
CONTACTBerend Weel E [email protected]
SIM-CITY AN E-SCIENCE FRAMEWORK FOR ASSISTED DECISION SUPPORT
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As cities grow, urban planning is essential to keep them habitable. This is especially true for rapidly urbanizing countries such as India, where the urban population has increased fourfold in the last 50 years. With such wildly growing urban areas, disasters such as fires require additional attention. For urban planners to make informed decisions, they need the tools to analyze the current, highly complex, situation and evaluate likely and significant scenarios.
Simulation approaches are highly data-centric and have demonstrated their ability to capture and model social dynamics complex situations.
In the SIM-CITY project, together with TNO, we combine an interactive geospatial website, the Common Sense framework, with data-driven simulation-based science. With this e-Science infrastructure, urban planners and scientists evaluate, analyze, and predict the complex dynamics in rapidly urbanizing countries. The infrastructure in totality is dubbed an assisted decision support system.
We demonstrate this web framework with a fire response model for Bangalore, which includes a heterogeneous traffic model and dynamic fire station placement.
CONTACTLeon Gommans, PhDScience OfficerIT Technology Office – R&D (ITSTRD)Guest researcher UvA/SNZ
RESEARCHING MULTIDOMAIN BIG DATA SHARING INFRASTRUCTURE MODELS
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Future Internet capabilities: researching big data sharing models
Organizations increasingly considering collected Big Data as an asset. Next to offering open data, organisations may only want to share certain information, representing value, carry risk, imply legal liability and responsibilities, etc. for a particular intend that supports a mutually identified and agreed benefit.
Considering scalability, hub-spoke models are typical approaches to implement data analyses. Hub functions can also ensure data sharing is performed in a trusted and fair way. Considering the fact that network link speeds is are orders of magnitude faster then local disc transfer speeds, the research question asked is “What does unlimited bandwidth mean for hub/spoke based data analyses models?”. “What additional roles can a hub play to ensure data sharing is performed in a trusted and fair way?” is an important subquestion.
THE PASSENGER EXPERIENCE CLOUD
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A customer experience nearly always transcends the sphere of influence of individual enterprises. For KLM a happy customer has, next to a great flight experience, an enjoyable experience at the airport.
Enjoyable experiences often require seamlessness. To create memorable home-to-home experiences we need to link data that is in enterprise-bound silos.
Data sharing between enterprises is notoriously hard, as there’s often implicit value in data that enterprises don’t want to share. We created the Passenger Experience Cloud to break down data silos. The concept relies on sharing actionable insights (experiences) rather than raw data and always in a context that benefits the passenger (opt-in model).
We, KLM & Schiphol, are currently developing an alpha version. In the future we foresee other enterprises to connect to the PEC as well.
CONTACTKoen Bonenkamp – KLMM +31 6 174 376 90
EU regulation regarding handling personal data are a barrier to big data innovations that depend on the combination of data collections from different stakeholders. The ambition of this technology line is to develop governance principles and technology supporting a multi-stakeholder big data innovation, with an architecture for data combination that enforces data protection compliant handling of personal data and providing safeguards to prevent misuse of the data.Information Centric Networking is an alternative networking paradigm for the internet. The goal of ICN is to transform the current IP-based internet from a network based on addressing devices to a network based on addressing data itself, independent of the location of this data. With ICN the fundamental nature of the internet will change radically. The center of gravity of the ICN research is currently the US and Asia. Our ambition is to position TNO as the ICN research center in Europe, and to collaborate with international partners on the future of the internet.
CONTACTJudith Dijk E [email protected]
Wessel KraaijE [email protected]
UNCERTAINTY & MULTI-STAKEHOLDER COLLABORATION IN BIG DATA SOLUTIONS
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The term Big Data is used for collections of data so large and complex that it becomes too difficult to process using on-hand data management tools or traditional data processing applications. The goal of the Early research program Making Sense of Big Data is to create top capabilities (tools, models, methods) that enable a stakeholder to design and implement a data-driven-innovation in a multi-stakeholder setting. In 2017 the program will focus on three topics: uncertainty, multi-stakeholder collaboration and Information Centric Networking. The technology line Uncertainty has as focus to tackle real-world challenges in big data where multiple, unstructured, relatively ‘ambiguous’ (belonging to different domains or contexts) datasets are used. This means identifying and quantifying sources of uncertainty and dealing with uncertain results, for example by an interactive visualization. The ambition of this technology line is to achieve excellence at applying academic state-of-the-art tools and methods for descriptive annotation and predictive modeling of large scale heterogeneous real world (noisy, incomplete, ambiguous) data – tools that are generally developed on constrained, controlled and clean datasets.The technology line Multi-stakeholder collaboration has as focus on multi-stakeholder aspects in big data innovations. Lack of technical safeguards, fear of disclosing sensitive data and increasingly tight
CONTACTJisk AttemaE [email protected]
Gert-Jan Steeneveld E [email protected]
SUMMER IN THE CITY
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In the summer in the city project, a NLeSC project in collaboration with the meteorology and air quality group of Wageningen University, the main goal was the development of a high resolution (100x100m) weather forecasting system, with the ability to do intra-urban, or neighborhood scale, weather forecasting.
The main scientific challenge was to forecast the ‘Urban Heat Island’ effect: due to a local conditions, urban temperatures can be significantly higher than rural temperatures, leading to unhealthy or even dangerous urban temperatures in the summer.
We performed daily 48-hour forecasts during the summer of 2015 for the metropolitan area of Amsterdam, which were available real-time on the projects website. Key success of the project were the creation of an urban morphological data set for the Netherlands, for use in WRF, and proof of the viability and use of very high resolution weather forecasts for heat waves. The work was made accessible to the general public via a map showing the average UHI per neighborhood, and an App. The App provides a thermal comfort forecast based on your precise GPS location, for either sunlit or shadowed areas, and windy or sheltered conditions.
MAIN QUESTIONSIn what way can (interactive) digital media companies get better insights in the value of their user-data and how can this be used for the creation of new business models?
THE PROJECT– Analyze the data from the apps of our partners (framework
Cambridge service alliance)– Research into the needs of the end user (surveys and interviews)– Test new insights by creating prototypes– Results: an implementation roadmap for Data Driven Business
Models, platform for analyzing data-logic cross sector, improved curriculum on data knowledge for media professionals
CONTACTWouter GrootM +31 6 211 569 64E [email protected]
CREATING MORE VALUE WITH DATA
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‘At the moment we are the middleman for our clients when it comes to userdata of our services’
‘We are not really in the position to determine the value of these data for our own company’
Two year research project conducted by Universities of Applied Sciences Amsterdam and Utrecht (sept 2016 - sept 2018)
FULLY CONNECTED TRAVELLING
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Imagine: It is the year 2020 and 13 different cities participate in hosting the European Championships. You and your friends are enjoying a weekend in Rome topped off with the final between Germany and the Netherlands in the Amsterdam Arena
Now we know 2 things: 1. You do not want to be late for a football match especially when it’s the final match with your home country; 2. The first may be a challenge considering we will need both plane, train and (self-driving) car to get there.
Transport service providers do not yet have aligned timetable platforms. Together with our partners: KLM, Schiphol, NS and Provincie Gelderland (initiator of the self-driving WEpod car) we are hoping to bring a change. By organizing a Datathon through which we challenge talented data scientists to compete for the best model combining all data, we will introduce truly connected traveling – Getting you and your friends there in time to witness the legendary game from the very first second.
CONTACTMaartje Wilton, Pieter BoonE [email protected]
government bodies or other organizations in Responsible Data Innovation. We can do that both from technology and business perspectives, and from social and ethical perspectives. Regarding the latter, we can guide you through a process, e.g., in an interactive workshop, using ‘Value Sensitive Design Cards’ to elicit discussions and focus on key values like autonomy/freedom, fairness/equality, and transparency/accountability, in order to make informed and sensible decisions. Our shared goal is to make sure that your Big Data application becomes a success; that it indeed serves people.
CONTACTMarc SteenE [email protected]
Jenny de BoerE [email protected]
Source: responsibility-rri.eu
RESPONSIBLE DATA INNOVATION
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Big Data is not only an enabler of radical changes in technology and in business, but also of radical changes in society and in people’s daily lives. Big Data is both a potential source of benefits, and a source of risks. In general, we assume that businesses, government bodies and other organizations aim to serve people, and that they want to be portrayed positively in the press. This, however, can be challenging, when they work on the development or deployment of innovative Big Data applications, which often give rise to difficult questions, e.g., regarding, people’s privacy or autonomy, or regarding fairness and transparency. TNO can support businesses,
In this specific use case OBILytics identified social media accounts of the current customer base. We analyzed the social activity and classified a customer’s current life stage. Using this information, including historical purchase data, we recommended product offerings for customers that were send to them in personalized advertisement leaflets. We were able to identify a customer’s Twitter, Instagram and Pinterest accounts in about 30% of all cases. Messages were classified according to sixteen different life stages. For approximately 25% of all customers we were able to identify the current life stage of that customer.
CONTACTFrank SmitM +31 6 468 822 09E [email protected]
HOW TO OFFER THE RIGHT PRODUCT TO THE RIGHT CUSTOMER AT THE RIGHT TIME
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Most retailers offer a lot of products, but not every product is right for every customer. Current initiatives use historical purchases to predict which product would be right for a given customer at a certain time. These initiatives do not cope with changes in people’s lives. For example, they could get married, have children, graduate, etcetera. Social Media provides real time, accurate, unbiased insights about the current life stage a customer finds itself in. These insights are very useful to personalize offerings and advertising, which creates higher conversion.
We need to translate the world of big data into relevant information, and relevant information into nice business cases
My inspiration is the world of the telecommunication. This business understood that people want to be connected with their loved ones. And so they introduced the mobile phone step by step in all the countries around the world. And in the same way as people want to be connected with their loved ones, people want to live a healthy life with them in a healthy environment.
So let’s invest in a world of shared bikes, where all people have the possibility to use a bike, for the first mile and for the last mile, to go to work, to have fun, to visit other people.
For more information visit the following websites:www.fietscoalitiesmartcity.nl, www.happycyclista.com, www.mandelaopdefiets.nl.
CONTACTHelma SchenkeveldE [email protected]
SHARE BIKES FOR A BETTER FUTURE!
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Hello, my name is Helma Schenkeveld, I am a partner of the Fietscoalitie Smart city, and I like to tell you about bikes, big dreams and big data.
How can we make the connection with bikes, to the world of billions of individuals, who want– a healthy life– healthy communities– a healthy environment!
At TNO we are currently working towards a solution that adds real-time predictions of travel times in case of traffic incidents. We aim at predicting how the impact of a traffic incident will evolve in the short term in order to inform and advise drivers and planners more realistically without overloading them with non-relevant information. Our data driven approach involves the latest deep learning neural network technology to learn from traffic density and velocity data that has been collected for several years at high spatial and temporal resolution for all major roads in the Netherlands. We expect deep learning to be the most suitable technology able to identify in real-time traffic patterns that are indicative for the impact of traffic jams, ultimately providing more useful and accurate predictions to drivers on the road.
CONTACTMatthias FaethE [email protected]
TRAVEL TIME PREDICTION FOR INCIDENTS: DEEP LEARNING FROM EXPERIENCE
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By Matthias Faeth, Maarten Kruithof, Taoufik Bakri (TNO) While computer assisted route planning is fairly common place, car drivers are often left to making their own guesses about the impact on their travel time when a traffic incident occurs. Current services typically take average statistics of local historic data, and add information on the present state of traffic jams and other data that may have some impact on traffic (weather, soccer games, music concert etc.) and treat the present situation primarily as a static one without changes over time. The impact of traffic incidents, which are unexpected, uncommon, dynamically changing, and often caused by accidents is very hard to predict using just this data.
If you are able to better estimate how much green energy will be available, you can produce less ‘dirty’ grey energy and you don’t have to waste that much green energy of which you didn’t know it would have been produced.
By implementing machine learning and by combining different data sources, BigData Republic has been able to increase the accuracy of the prediction from 75% to 91%. Variables include the expected wind speed, wind direction and air pressure, but also historical data from wind farms.
Besides the fact that green energy is a sustainable solution, we create sustainable solutions for our clients as they can use and maintain it independently. The result: a satisfied client with a sustainable and customer-oriented solution.
CONTACTBigData RepublicE [email protected]
DURABLE AND CUSTOMER-ORIENTED: PREDICTING THE SUPPLY OF WIND ENERGY
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DELTA Energy is a provider of (green) energy, located in Zeeland. Unlike other sectors, the demand for energy is stable and the supply is variable. In this particular case the energy production of wind farms is depending on the wind that blows on a particular day. DELTA has asked BigData Republic to predict the daily supply of wind energy, enabling them to better align supply and demand.
Uncertain supply of green energy is offset by an overproduction of grey energy.
Overproduction of green energy is discarded.
The idea is original. The data that are available now has not been available before and has not used to test this idea. So the combination of the idea with the big data is unique!
CONTACTDr Yusen ChenM +31 6 514 906 62E [email protected]
ICONBID IDENTIFYING MAIN CONGESTION LOCATIONS USING TRAFFIC BIG DATA
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Congestions locations are known and easily identifiable via conventional approaches, such as loop data and FCD (Floating Car Data). Such locations are then used for traffic control and guidance. In PPA project (Amsterdam), A10 and S101 are coordinated for control, but only for the joint and nearby junctions.However the results are not convincing. Two of the main reasons are that (1) removing a congested junction is too late when cars are already waiting at junction; and (2) the congested junction may not be the real problem location, and is probably the secondary location caused by a nearby or downstream junction. Thus identifying the main congestion locations is the most important task before carrying out the actions for a coordinated control.To be able to do this, we need the traffic monitoring data in the adjacent roads next to the motorway. But we do not have the data, or sufficient data for other roads (rather than motorway), in The Netherlands. So how do we continue?In Beijing, there is a big data in traffic, which includes FCD (98,000 cars), GSM (16 million mobile phones), loop data and camera data. And I am supervising my PhD student to start investigating the use of such data for the aforementioned goal. For RWS, investing in right place for control and guidance, avoiding wrong locations of congestion and using data and research from others are the most relevant in promoting PPA Next.
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