Schedule

November 15, 2017

Full Day of intimate Workshops with breaks to network and have one–on–one conversations with attendees, sponsors and workshop leaders.

What it is: A careful look at the many ways in which AI and customers interact, run by Agnieszka Walorska.

Key Skills and Abilities to Discover: Learning how Data, machine learning and AI impact the way we interact with technology. This includes translating customer needs into possible AI-applications and dealing with the possible pitfalls of AI and machine learning (including privacy, algorithmic bias, etc).

Who gains the most from it: Everyone responsible for digital product innovation and strategy. Including, but not limited to, Chief Product Officers, Chief Innovation Officers, Chief Digital Officers, Product Owners, UX Designers and Innovation Managers.

As one of the very first employees at one of the biggest exit-success stories on the German market (studiVZ), Agnieszka Walorska has played a crucial role in the explosive growth of this social network. She is the author of publications and sought-after expert and keynote speaker on the influence of Artificial Intelligence on User Experience and Design, Agile Management and Digital Innovation. She led successful Innovation and Customer Experience projects across several industries, including utilities, banking and media.Agnieszka studied social sciences and politics at Warsaw University and Humboldt-University of Berlin and received a scholarship from Studienstiftung and Hertie Foundation. When she is not busy solving innovation problems, she is training for her next thriathlon.

Interested in machine learning but don't know where to begin? This workshop is for those with a basic to advanced understanding of Python who would like to expand their repertoire into supervised machine learning. In this workshop, we will discuss uses of basic machine learning tools in Python including scikit-learn. To prepare for machine learning, we will review common data cleaning tips for normalization as well as how to explore useful features. Finally, we will utilize kaggle to test our solutions.

Preparation: A full repository with exploratory notebooks and requirements will be provided no less than two weeks before the workshop and you will be expected to come prepared with everything installed.

About the instructor
Katharine Jarmul is a Python data scientist, teacher, author and speaker. She is an adjunct lecturer for Introduction to Programming with Data and Data Mining and Analysis at the University of Florida. Katharine has worked at large and small companies as a developer, data scientist and technical manager since first using Python in 2008. Katharine co-authored O’Reilly’s Data Wrangling with Python and has several other video courses and books on Python and data. She helped found the first PyLadies chapter in Los Angeles, California in 2011 and lives in Berlin, Germany where she runs a data consulting company, kjamistan UG.

Some Atypical Applications of Typical Machine Learning Algorithms
A workshop highlighting how to resourcefully use algorithms, run by the world-renowned data scientist, Kirk Borne. Kirk will present a variety of atypical use cases and applications (in science and in business) of some typical textbook machine learning algorithms, including regression, clustering analysis, association mining, time series analysis, and network analysis.
Who it’s for: Anyone interested in machine learning, analytics and data science; Chief Digital Officers, Intrapreneurs, Chief Innovation Officers, Data Scientists, Developers. The audience does not need advanced knowledge or experience, but my use cases will hopefully be interesting and useful even for those people too. Also, the attendees do not need to download or to prepare anything in advance. The applications that I discuss will cover different industries (healthcare, science, marketing, business, cyber), and therefore the applications are relevant to any industry, since the applications cover the basic types of discovery from data science: Class Discovery, Trend/Correlation Discovery, Novelty (Anomaly) Discovery, and Link (Association) Discovery --- those are applicable everywhere!
Key skills and abilities to discover: Design thinking, critical thinking, and finding data-driven ways to find solutions. Key question: “how does data drive the design?”
“It’s really for anyone keen on translating data into action” –Kirk Borne
Dr. Kirk Borne is a data scientist and an astrophysicist. He is Principal Data Scientist in the Strategic Innovation Group at Booz-Allen Hamilton since 2015. He was Professor of Astrophysics and Computational Science in the George Mason University (GMU) School of Physics, Astronomy, and Computational Sciences during 2003-2015. He served as undergraduate advisor for the GMU Data Science program and graduate advisor to students in the Computational Science and Informatics PhD program. Prior to that, he spent nearly 20 years supporting NASA projects, including NASA's Hubble Space Telescope as Data Archive Project Scientist, NASA's Astronomy Data Center, and NASA's Space Science Data Operations Office. He has extensive experience in large scientific databases and information systems, including expertise in scientific data mining. He was a contributor to the design and development of the new Large Synoptic Survey Telescope (LSST), for which he contributed in the areas of science data management, informatics and statistical science research, galaxies research, and education and public outreach.

What it is: A workshop expanding your data science skills by exploring the impact of AI in your field, run by Chris Armbruster: Director at Data Science Retreat and Mustapha Al Helwi, Innovations Manager at HELLA Aglaia.

Key Skills and Abilities to Discover: You will learn how to “Move up in Machine Learning in order to move up in your field.”
Who gains the most from it: Big-time beneficiaries are attendees with experience in data warehousing, data analytics and business intelligence. S.T.E.M graduates (Science, Technology, Engineering, Medicine) seeking further specialization also have a lot to gain.
Areas for career advancement: Potential career paths: Offers a pathway from a data role at your company to a more specific data science and Machine-Learning role.
Number one Takeaway: A 6-9 month roadmap for achieving your AI goals that you can literally take home with you.

November 16, 2017

08:30

Registration : Coffee and Networking

09:00

Welcome to Data Natives 2017!

Opening Remarks by Elena Poughia, Founder at Data Natives and Data Natives Team

9:10

Keynote: Kirk Borne - Principal Data Scientist at Booz Allen Hamilton

The Many Faces of Smart Data

Smart data are essential when faced with massive-scale data collections. "Smart" refers to data that are tagged or indexed with meaning-filled metadata that carry information about the semantic meaning of the data, its applications, use cases, content, context, and more. Such meta-tags enable efficient and effective discovery, description, and delivery of the right data at the right time, both to humans and to automatic processes.

9:40

PANEL

GDPR (Data Privacy & Security)

Alexandra Deschamps-Sonsino, Founder at Designswarm & Good Night Lamp, Katharine Jarmul, Founder and Data Scientist at kjam, Sebastian Weyer, Co-founder and CEO of Statice, Gordon Grill, Data Privacy Expert at Deloitte Analytics Institute and Johannes Klausch, Lawyer & Senior Associate at Luther Rechtsanwaltsgesellschaft mbH

10:20

Jana Kludas, Data Scientist at um*

Artificial Intelligence as a Service – AI of the shelf

Artificial Intelligence (AI) comprises a wide range of algorithms that allow computers to solve specific tasks by generalizing over data. The two main areas are classic Machine Learning algorithms such as classification, regression etc and Deep Learning, which are modern learning algorithms based on Artificial Deep Neural Nets.
In the last years, many big IT corporations like Amazon (Web Services), Google (Cloud Platform), Microsoft (Azure) and IBM (Developer Cloud) as well as startups like Dataiku, BigML, ForecastThis and more have started to offer Artificial Intelligence as a Service (AIaaS). These services are supposed to lower the entry costs for other companies to use Artificial Intelligence. While previously, companies required a lot of time and money to build up the technical know how and infrastructure to develop AI applications, AIaaS can reduce the development time and improve the Time-to-Value significantly - AI of the shelf so to say.
I’ll talk about the current development status of these services and about their pros and cons.

10:40

Networking & Coffee

11:00

PANEL

Diagnosis on Primary Care using Data Science

Eden Duthie, Head of Data Science at ADA Health, Dr. Prateek Mahalwar Manager @EY I Board member @Max Planck Alumni Assoc. I Indo-German Young Leader 2017 and Bart de Witte, Director of Digital Health at IBM DACH. Moderated by Maren Lesche, Ecosystem Manager at etventure Startup Hub & Ambassador at FTR4H

11:40

Bart de Witte, Director of Digital Health DACH at IBM Germany

The Future of Digital Health, why we are entering a different game

In this Talk, Bart de Witte will give an overview on what is happening on the intersection between AI and Healthcare. He will dive into specific use cases and projects he has been involved in, and then into the the future direction of healthcare focusing on business potential, ethical challenges and present future healthcare developments.

12:00

PANEL

The future of Mobility

Holger Weiss, CEO of German Autolabs, Silvan Rath CEO of Predict.io, moderated by Elena Petrova, Data Scientist at Auto1

12:40

John Calian, VP, The Blockchain Group & Co-lead, Head of T-Labs at Deutsche Telekom Innovation Labs

Blockchain in the Enterprise

Blockchain is an emerging technology spoken about in the halls and meeting rooms of companies around the globe. How will it be used in the Enterprise?

At Deutsche Telekom, we will be looking at two (2) ways to explore the topic

CORE SYSTEM DESIGN
Develop a computing stack design and service system utilizing decentralized technologies to power an infrastructure that will expose interfaces for deployment of full system decentralized applications (dApps).

dAPPS DEPLOYMENT
The core system will empower dApps for multiple industries. The dApps will address core telecommunication systems, as devices management, communication and network, as well as various other industries, as logistic, health, pharmaceutical, automotive and a wide variety of internal processes for enterprises.

13:00

Lunch

14:00

PANEL

HealthTech: health insurance

Dr. Torsten Hecke, Head Analytics & Insights at Techniker Krankenkasse, Vadym Vorobiov, Chief Technology Officer at Solve.Care Foundation, Christoph Wülfing, Business Development Manager at Qunomedical and Maren Lesche, Ecosystem Manager at etventure Startup Hub & Ambassador at FTR4H


14:20

PANEL

HealthTech: health insurance, Continued...

14:40

Tjasa Zajc, Strategic Healthcare Partnerships & Healthcare Communications at IRYO.IO

Digital health progress and unsolved puzzles

A decade ago, a sick student would quickly fall behind with school work when hospitalised. Today, he or she could follow classes through live streaming by his classmates. Instead of physically going to the doctor, we can today use telemedical services for health consultations. IoT, sensors, AI and machine learning are bringing new discoveries, accelerating medical progress and development of precision medicine. Medical data value and access is being redefined with blockchain. Many things are improving dramatically.

There are around 325,000 mHealth apps currently on the market, with 3,4 billion people predicted to own a smartphone by the end of 2017. Thanks to digitilisation, healthcare is becoming more and more user friendly. But what about the human aspects technology developers are forgetting about: inaccurate data gathering because patients quickly get tired of using apps? Burned out doctors because of unfriendly IT systems and rising bureaucratic demands? Danger of technical errors?

Adoption of technology in medicine and healthcare is slow because human lives are at stake. Unlike in many other industries mistakes cannot be called innovation or progress. So what is the current state of digital health innovation and what challenges are innovators facing due to human characteristics?

14:50

Suzy Moat, Associate Professor of Behavioural Science at the University of Warwick

Understanding beautiful places with deep learning

Are beautiful places good for us? In this talk, Suzy Moat will describe how data from an online game called ‘Scenic-or-Not’ has begun to offer new answers to this age-old question. She will explain how deep learning can help us understand whether beautiful places are simply natural places - or whether humans might be able to build beautiful places too.

15:00

PANEL

AI and Aesthetics

Robyn Farah, CEO of KATO, Suzy Moat, Associate Professor of Behavioural Science at the University of Warwick, Klaas Bollhoefer, Founder & CxO at Birds on Mars, Claudia Becker, MD and Digital strategist at EDGIZE and Elena Poughia, MD at Dataconomy Media GmbH

15:40

Christoph Tempich, Chief Data Economist at inovex GmbH

Data Product Discovery

The world´s most valuable resource“ titles „The Economist“ in May, referring to data as the resource. With computing resources in the cloud, cheap sensors in physical products, and advanced machine learning algorithms to make use of the collected data, the relevance of data will only increase.
We believe that these developments open up new opportunities for companies to develop and profit form data products. They can include feedback loops in their existing products in order to improve them or create new products based on the usage data of an existing one. The usage data itself can even become the USP of the physical product. Machine learning can help to automate customer jobs that before required tedious input of data by the customer. They can realize more complex business models as users of a service and buyers of data might be very different.
In the talk, we will illustrate our definition of data products based on project examples. We share our insights on how to implement a data product successfully. We have adapted the Lean start-up principle in order to get data products to the market quickly while maintaining the core factors of a valuable data product.

15:50

Christoph Tempich, Chief Data Economist at inovex GmbH

Continued...

16:00

Networking & Coffee

16:20

PANEL:

AdTech/ Adblock?

Dirk Freytag MD at Content Pass | Dr. Steffen Wachenfeld, Chief Product Officer at Ocono | Ionut Ciobortaru, CEO at Pub Native | Martin Karlsch, CTO & Co-Founder at Remerge

16:40

PANEL:

AdTech/ Adblock?

Dirk Freytag MD at Content Pass | Dr. Steffen Wachenfeld, Chief Product Officer at Ocono | Ionut Cioboratu, CEO at Pub Native | Eugen Martin, VP of Product at remerge

17:00

Closing Keynote: Toby Walsh, Professor of Artificial Intelligence at the University of New South Wales

Killer robots: the third revolution in warfare

The age of autonomous killing machines is closer than you think, and scientists and computer experts are deeply worried. Already autonomous weapons systems are under development, and some have already been deployed. But there are no international agreements governing their use, nor any way to ensure they can tell friend from foe. The speaker was a driving force behind an open letter calling for a ban on so-called killer robots – signed by UK physicist Stephen Hawking, Apple co-founder Steve Wozniak and 20,000 others. He has spoken at the United Nations now three times on the issue, adding his voice to an international movement to ban or regulate what has been called the Third Revolution in Warfare. He will describe the technologies currently being developed, and why they are of such concern to leading AI researchers. He will outline the growing global push to ban their introduction, and why a number of nations are resisting the call.

November 17, 2017

8:30

Registration : Coffee and Networking
Registration : Coffee and Networking

9:00

Data Natives Day 2:

May the data be with you! By Elena Poughia, Founder at Data Natives and Data Natives Team

Opening Remarks in the Cube

9:10

Luisella Giani, Digital Director EMEA at Oracle

Will your team mate be a robot? Artificial Intelligence: Separating the real from the hype

Opening Keynote in the Cube

9:40

PANEL

Education and learning: how to become a data scientist and where to look for a job

Sebastien Foucaud, MD of Certace, Chris Armbruster Director of Data Science Retreat, Antje Bustamante-Mena, Head of Insights & Analytics at Scout24, Dr Daniela Drechsel, Data Scientist at mobile.de/eBay
Mari Hermanns, Head of BI at Solaris Bank

Finding suitable BI set-up and making it work in your organisation

Over the past few years BI has emerged as a new central function in many organisations. Frequently we hear about new tools and methodologies and recommendations for best practices in the field. The head of BI in solarisBank will share her experiences and will provide advice on how to set-up and/or re-evaluate Business Intelligence in your organisation. She will also recommend how to find the right tools and how BI could have a lasting impact on your business.​

10:00

PANEL..continued

Education and learning: how to become a data scientist and where to look for a job

Sebastien Foucaud, MD of Certace, Chris Armbruster Director of Data Science Retreat, Antje Bustamante-Mena, Head of Insights & Analytics at Scout24, Dr Daniela Drechsel, Data Scientist at mobile.de/eBay
Jan Wiegelmann, Director Data Analytics at ValTech

Machine Learning for Self-Driving Cars

TBA

10:20

Jamie Conlon, VP Bus Dev at Kx Systems

Kx: From Wall Street to IoT

The worlds fastest time series database, Kx has been solving big data problems within mission critical environments in capital markets for over 20 years and is now being utilised by companies such as Airbus and across multiple industry sectors to solve challenges posed by IoT to traditional environments.
Danilo Poccia, Tech Evangelist at AWS

Machines are Learning: Bringing Powerful AI to All Developers

Have you always wanted to add predictive capabilities, image or voice recognition to your application, but haven’t been able to find the time or the right technology to get started? Everybody wants to build smart apps, but only a few are Data Scientists. This session will help you understand machine learning terminology & challenges, what deep learning is and its possible use cases, how to build a machine learning model that works, and how to use developer-ready APIs for high-quality, high-accuracy AI capabilities that are scalable and cost-effective.

10:40

Networking & Coffee
Networking & Coffee

11:00

Startup Battle!

Judges: Nasir Zubairi, CEO of LHoFT, Jag Singh, MD Techstars, Retail Partnership with METRO, Chiara Sommer, Investment Manager at HighTech Grunderfonds, and Videesha Bocke, MD at Signals.vc.

Winners will be announced at the closing remarks!
PANEL:

Reinforcement Learning and Autonomous Vehicles

Thomas Staufenbiel, Managing Director at GESTALT Robotics GmbH, Gaurav Mehendiratta, Analytics & Data Insights Lead at Ericsson, Ludmila Morozova-Buss, Vice President, Public Relations & Media Communications at Global Institute for IT Management (GIIM) and Fernando Rosa, Head of Data Science at Auto 1 Group

11:40

George Anadiotis, Linked Data Orchestration - ZDNet

Big Data, Crystal Balls and Looking Glasses: Reviewing 2017, predicting 2018

What does working with data since the 90s, having a weekly column on all things data related in one of the world's top tech publications, and a big data crystal ball add up to?
That all adds up to having a -hopefully- equally opinionated and well-informed view on what has happened in 2017 and what will happen in 2018 on planet big data. It's a bargain - around a year in 20 minutes.
Francisco Webber, GM of cortical.io

AI Beyond the Reptilian Brain

12:00

PANEL

WTF is GovTech and what the Future Holds?

George Johnston, Founder of Nitrous and Tech City Ventures, Lisa Witter, Founder and CEO of Apolitical, Benjamin Nanz Snow, CoFounder & CEO at Civocracy.org, and Videesha (Böckle) Kunkulagunta, Partner at a fund/VC
Ralf Klinkenberg, Founder & Head of Data Science Research at RapidMiner

Fraud Detection and Prevention: Leveraging Machine Learning to Detect Fraud Patterns, Anomalies, and Unusual Behaviors

12:20

Continued.. PANEL

WTF is GovTech and what the Future Holds?

George Johnston, Founder of Nitrous and Tech City Ventures, Lisa Witter, Founder and CEO of Apolitical, Benjamin Nanz Snow, CoFounder & CEO at Civocracy.org, and Videesha (Böckle) Kunkulagunta, Partner at a fund/VC
Marija Vlajic Wheeler, Data Scientist at Clue

How data will revolutionarize female health

Women have historically been left out of medical research which failed to take into account gender differences. Menstrual cycle is a vital sign of a person’s health — similar to blood pressure or heart rate — yet, in large parts of the world, menstruation is still considered a taboo. The rise of period tracking apps is set to change this. Personal tracking allows people to understand their cycle and its symptoms, at the same time generating datasets orders of magnitude larger than those previously used in female and reproductive health research. At Clue we use this data to help move the female health research forwards, as well as to provide our users with personal, contextual, and scientifically accurate knowledge about their bodies.

12:40

Carmen Sutter, Sr Manager, Developer Relations at Adobe

Adobe Sensei

Kai Gansel, Key Account Manager, Consultant & Instructor at ADDITIVE Soft

Multi-Paradigm Data Science

Gaining insight from data is not as straightforward as we often wish it would be – as diverse as the questions we’re asking are the quality and the quantity of the data we may have at hand. Any attempt to turn data into knowledge thus strongly depends on it dealing with big or not-so-big data, high- or low-dimensional data, exact or fuzzy data, exact or fuzzy questions, and the goal being accurate prediction or understanding. The talk emphasizes the need for a multi-paradigm data science to tackle all the challenges we are facing today and may be facing in the future. Luckily, solutions are starting to emerge...

13:00

Lunch
Lunch

14:00

PANEL

WTF is Blockchain and what the Future Holds?

Andreas Osowski, IOTA Project Developer, Nasir Zubairi, CEO at LHoFT and Trent McConaghy, CTO at BigchainDB
Tim Ward, CEO at CluedIn

Building a Decision Engine with Machine Learning Techniques and Neo4j

There is no doubt that machine learning and graphs go hand in hand — and Neo4j makes it possible to take machine learning to the next level.
In this presentation, we'll share how CluedIn built Neo4j into a decision engine that turns company data into actionable insights.
By using machine learning techniques and the graph as a decision tree, we were able to achieve amazing precision in merging and identifying insights in the enterprise. With practical demos and techniques, viewers will leave this presentation with new and effective ways of working with Neo4j.

14:20

Continued.. PANEL

WTF is Blockchain and what the Future Holds?

Andreas Osowski, IOTA Project Developer, Nasir Zubairi, CEO at LHoFT and Trent McConaghy, CTO at BigchainDB
Michelle Lee, Manager, Data Science, Risk Analytics at Deloitte UK

Governing AI Risks

TBA

14:40

Trent McConaghy, Founder and CTO of BigchainDB

A decentralized data marketplace, for applications in artificial intelligence and beyond

Society is becoming increasingly reliant on data, especially with the advent of AI. However, a small handful of organizations with both massive datasets and AI capabilities have become powerful with control that is a danger to a free and open society.
Ocean Protocol aims to unlock data, for more equitable outcomes for users of data. It is a decentralized data exchange protocol that makes data available for everyone with privacy, control, and compliance. Its innovative token design merges an efficient proof of work with curation markets on datasets.

Nicolas Woloszko, Data Scientist and Economist at OECD

Machine learning for economic forecasting

The present research introduces a novel algorithm specifically tailored for the needs of macroeconomic forecasting. The economy is an ever-evolving complex system, characterised by high-dimensionality and complex interactions. Our forecast methodology addresses these problems, thus yielding better performance. We produced simulations in pseudo real-time for six major economies (USA, UK, Germany, France, Japan, Italy) and outperform both model- and expertise-based benchmark forecasts.

14:50

Nan Zhao, Researcher at MIT Media Labs

Mediated Atmospheres - A Workspace That Responds To Your Needs

Worker satisfaction is paramount to retention and productivity. The sensorial qualities of the workspace — the atmosphere — shaped by the composition of light, sound, objects, and people have a remarkable influence on our wellbeing and performance. Manipulating it has been shown to be powerful affecting cognitive performance, mood, and even physiology. Emerging technologies for spatial augmentation provide new opportunities to enrich everyday environments and potentially new kinds of architectural services. This talk presents how these technologies could be used to improve the workspace for wellbeing, and performance.
Nicolas Woloszko, Data Scientist and Economist at OECD

Continued...

TBA

15:00

PANEL

Data Journalism

Peter Friess, Senior Programme Officer at European Commission, Pawel Zoneff, Unit Director B2C at PIABO PR and Ulf Schöneberg, Senior Data Scientist at um*, moderated by Elena Poughia, Managing Director at Dataconomy
Ran Taig, Senior Data Scientist at Dell

Hardware Failure Prediction at Dell-EMC

Today’s exponential growth of data along with the amount of hardware required for storing and processing it creates many non-trivial challenges in different areas.
At Dell-EMC, we recognized the importance of using predictive analytics for monitoring our enormous install base and increase, through it, the customer experience, using a proactive support approach.
In this talk Ran Taig from the Data Science as a Service team at Dell IT will review some of the predictive capabilities developed by the team in the HW monitoring domain. Specifically, Ran will walk the audience through the use-case of hard-drive failure prediction. Predicting a drive failure well in advance enables the optimization of business processes involved with at-field drive replacements, increasing significantly the reliability of the storage system in addition to a significant cost saving on logistics related to drive replacements.
The talk will also touch in some lessons learned and challenges faced in the way to deploying such a model as well as other innovative, data science, solutions in an enterprise environment.

15:20

Continued.. PANEL

Data Journalism

Peter Friess, Senior Programme Officer at European Commission, Tilo Bonow, Founder and CEO of Berlin based PIABO PR and Ulf Schöneberg, Senior Data Scientist at um*, moderated by Elena Poughia, Managing Director at Dataconomy
Uri Goren, Data Scientist at Yahoo

Aspect oriented programming for Data science

Aspect oriented programming is a programming paradigm for dealing with cross-cutting concerns. Python enables AOP by using the built-in decorator feature. We will demonstrate how to use these concepts within a data science settings. We will cover: timing, caching, ssh tunneling, and interactive jupyter plotting.

15:40

Janina Mütze, Co-Founder and COO of civey

Defining Opinion Tech

Defining Opinion Tech
Do you know what your target group thinks and wants? For a long time you would ask them directly to get the answer. But since polling industry failed digital transformation, a professional use of behavioral data has increased instead. But using that data doesn't fit all the needs. Civey rethinks polling and achieved to build Germany's largest and most active panel for opinion research. Every day, the company gathers 1m votes and brings back opinion data into professional use.
Boyan Angelov, Data Scientist at MindMatch

Optimal Tooling for Machine Learning and AI

In recent years there has been an explosion of tools and technologies in the ML/AI space. While this is understandable in such a fast moving field, it also presents a challenge to newcomers who have to decide which ones to try first, and where the right mix between cutting edge and stability is. As a data scientist there is always more theory to learn, so you should maximize your productivity. This talk presents a complete and free/open-source tooling solution that you can start using right away, based on many hours of research and comparisons.

15:50

Peter Friess, Senior Programme Officer at European Commission

STARTS and Hyper-Connected Sociality

Science, Technology and Arts (STARTS) and Hyper-Connected Sociality (Social Media & Networks) are two major new digital initiatives of the European Commission. Both target to act as a catalyst by broadening the scope and fostering transversal cooperation around data, Internet of Things, decentralised platforms and between separated communities.
Thomas Poetter, Founder & CEO of Compris Technologies AG

Big Data / Microservices: Versioning for Protocols and Data

Similar to the CAP trade-off (Consistency – Availability – Partition-Tolerance), this presentation will demonstrate the C(A/P)S tradeoff for protocol/data versioning which stands for the trade-off between the goals of Code amount (low), Availability/Performance and Storage (low) which can be achieved through lazy/eager migration or by creating multiple converters. This presentation then shows best practices for versioning and how the trade-off can be solved. A switchboard pattern is presented which permits the use of different versions in parallel. As time permits, the presentation will include Apache Marmotta, simplified & optimized bi-temporal logic and Avro/HBase versioning aspects.

16:00

Networking & Coffee
Networking & Coffee

16:20

Helena Sternkopf, Digital Product Designer at Disruptive Elements

Rethinking Literacy Skills in the Age of Data

Data literacy describes a set of skills that is becoming more and more important for organizations and citizens to engage in a reflected and constructive way in the workplace and in society through and with data. However, can its complexity really be broken down to only this? Large parts of our society do not have much clarity about what data literacy means and what can be expected from it. Overall, there is a lack of consistent and appropriate approaches for helping to ‘speak data’ for an audience of different backgrounds and competencies. This talk will explore necessary data literacy skills and stimulate discussion on finding a way to describe and translate data-handling and -conversion skills for a more data-informed society.
Thomas Richter, COO at Swarm64

The king is dead, long live the king – teaching established relational databases rapid data collection and real-time analysis

16:30

Aron Jost, CEO & Founder of Ideafox

The Innovation Engine: Collective Intelligence vs. Artificial Intelligence?

Thomas Richter, COO at Swarm64

Continued...

TBA

16:40

Chris Zioutas Founder & CEO of DataCircle

Introduction into Data Asset Sale and the Data Sharing Economy

We are in a state of an ever increasing data hording culture.
Although common to many but not to all, the worth of data acquired by enterprises is enormus. However we have not yet reached a fully mature Data Sharing Economy, in which data asset sales are used to increase revenue, remove the need for Investment or Exit and even marketing.
For this reason, DataCircle is creating a data exchange network, that will allow companies to understand and make use of their data.
Amadeus Magrabi, Data Scientist at commerce tools gmbH

Boosting Product Categorization with Machine Learning

Product categories are the structural backbone of every online shop. To attract customers and facilitate navigation, categories need to be easily understandable, logical and consistent. With the explosive growth of data, it is becoming more and more difficult for retailers to match products to appropriate categories, and large product catalogs as well as the need to quickly adapt to changes often lead to costly misclassifications. In this talk, I will present our approach at commercetools to build a category recommendation system using methods from machine learning. I will talk about our use of deep neural nets and transfer learning to build an image classifier, word2vec and tf-idf to build a text classifier, and how we integrated these models in a REST API.

17:00

Closing Keynote: Danko Nikolić, Senior Data Scientist at Teradata

Why is big data fundamentally a replacement for the lack of a good algorithm? And why is this a good thing?

There is no such thing as an algorithm that is both generally applicable and optimally effective in the same time. It is always either – or. The more general the algorithm, the more data and computation it requires for learning. Bit data allow us to apply such general algorithms and thus, address problems we know little about. Effectively, we are replacing human work of coming up with an optimally effective algorithm with machine work that can achieve similar results with less effective algorithms. That way, we save a lot of time and resources.
Closing Keynote in the Cube

17:30

Closing Remarks
TBA
Closing Remarks in the Cube
TBA

18:00

Data Natives After Party!
Data Natives After Party!
Open to conference attendees and the general public and you are more than welcome to invite colleagues and anyone from your network.
Come celebrate the end of a great conference, mingle with other speakers and attendees and enjoy a selection of alcoholic beverages!