"Smart Data Web: Connecting data and extracting knowledge", Prof. Dr. Hans Uszkoreit, Scientific Director at DFKI

•

4 likes•3,875 views

"Smart Data Web: Connecting data and extracting knowledge", Prof. Dr. Hans Uszkoreit, Scientific Director at DFKI Watch more from Data Natives Berlin 2016 here: http://bit.ly/2fE1sEo Visit the conference website to learn more: www.datanatives.io Follow Data Natives: https://www.facebook.com/DataNatives https://twitter.com/DataNativesConf Stay Connected to Data Natives by Email: Subscribe to our newsletter to get the news first about Data Natives 2016: http://bit.ly/1WMJAqS About the Author: Hans is Scientific Director and Head of the Language Technology Lab at the German Research Center for Artificial Intelligence (DFKI). He also serves as site lead of DFKI’s Berlin branch. Hans studied at TU Berlin and the U of Texas at Austin. After research positions at SRI International in Menlo Park and IBM in Stuttgart, he became full professor for computational linguistics at Saarland U. in Saarbruecken where he taught for more than 20 years. He co-founded one print magazine and several language technology startups. Hans’s main interests in AI are foundations and applications of language and knowledge technologies. He has been leading several European and national projects in knowledge extraction, text analytics and automatic translation. His research is documented by more than 200 publications.

Data & Analytics

Smart Data Web
connecting data and
extracting knowledge
Hans Uszkoreit
German Research Center
for Artificial Intelligence
DATA NATIVES 2016 BERLIN

more than 4 Mio
queries
100 hours new
video material
uploaded
2.5 Mio new posts
571 new websites
generated
more than 204 Mio
emails sent
300,000 tweets
generated
220,000 new
pictures uploaded
7 new Wikipedia
articles
New data per minute (impressive but well-known)

o Web 2.0 – social networks
o semantic web - Web 3.0
o Data & Knowledge Communities:
Wikipedia, linked open data,
DBpedia, Wikidata
o cyber-physical systems
o Internet of Things
o intelligent enterprise
Two Exciting Developments
© 2016 DFKI – Hans Uszkoreit

Smart Data in Industry 4.0
Open Knowledge Social Media
News MediaOpen Data
Smart Enterprise
with business intelligence

Data Value Chains for Industrial Applications

Two Application Areas
Supply Chain Management
Market Research

16
linked open data
harvested web
semi-structured data
unstructured data
Data on
the WWW
Data in the
Enterprise
Corporate Knowledge Graph
(Unternehmenswissensnetz)
community-built knowledge

17
linked open data
harvested web
semi-structured data
unstructured data
Data on
the WWW
Data in the
Enterprise
SMART DATA WEB
Corporate Knowledge Graph
(Unternehmenswissensnetz)
community-built knowledge

18
linked open data
harvested web
semi-structured data
unstructured data
Data on
the WWW
Data in the
Enterprise
SMART DATA WEB
Corporate Knowledge Graph
(Unternehmenswissensnetz)
community-built knowledge

19
linked open data
harvested web
semi-structured data
unstructured data
Data on
the WWW
Data in the
Enterprise
SMART DATA WEB
Corporate Knowledge Graph
(Unternehmenswissensnetz)
community-built knowledge

20
linked open data
harvested web
semi-structured data
unstructured data
Data on
the WWW
Data in the
Enterprise
SMART DATA WEB
Corporate Knowledge Graph
(Unternehmenswissensnetz)
community-built knowledge

21
linked open data
harvested web
semi-structured data
unstructured data
Data on
the WWW
Data in the
Enterprise
SMART DATA WEB
Corporate Knowledge Graph
(Unternehmenswissensnetz)
community-built knowledge
Dynamic Data

Semantic combination of heterogeneous data
© 2016 DFKI – Hans Uszkoreit

Semantic combination of heterogeneous data
weather data
passenger counts
© 2016 DFKI – Hans Uszkoreit

Semantic combination of heterogeneous data
news: Newport Yacht Fair
tweets: Newport Yacht Fair
RSS feeds: traffic congestion
weather data
passenger counts
© 2016 DFKI – Hans Uszkoreit

Other
OpenData
Knowledge
Commu-
nities
ProductionLogistics
Marketing
Sales
CRM
Purchasing
Maintenance
Facility Mgmt.
Telekom-
munication
Science
Meteoro-
logiy
Media,
News
Social
Media
Wholesale
Retail
Commerce
Trans-
portation
Geodata,
Satellite
Data

KNOWLEDGE GRAPH
INFORMATION
EXTRACTION
TEXTUAL DATA

Web-DARE Architecture
Facts
Web
pages
Sentence
Mentions of
Facts
Filtered
Patterns
Patterns
Queries

Text Analytics Embedded in Big Data Technologie

Results at half-time
○ Text Analytics Platform has been
embedded in the Apache Flink
Big-Data Plattform
○ Deep analytics of News, Tweets,
RSS Feeds
○ Demonstrator system (with SD4M)
for German corporate world and mobility
○ Realization of data value chains for
both application areas
○ First version of the Smart Data Web
open knowledge graph

Current volume of basic and discovered knowledge
Entities in Knowledge Graph
○ > 1,2 Mio German companies
○ 27.075 German towns
○ 104.598 street names
○ 25.907 transport routes
Per day
○ 139.000 RSS posts
○ 140.000 tweets
○ 16.000 media news
Media reports since February
o 136.811 bankruptcy
o 19.605 strike
o 26.047 take over
o 21.352 mergers
o 6.619 mass lay-offs
o 165.958 natural desasters
o 697.784 accidents
o 320.061 traffic jams
o 558.531 closures

“Artificial Intelligence” covers a wide range of technologies today, including those that enable machine vision, effective computing, deep learning, and natural language processing. As advances increase, so do expectations. We now see a rush to add “AI inside” for applications and appliances in almost every domain. The reality is that some firms will have mega-hits with AI-enabled applications, and many more will suffer setbacks based on flawed adoption strategies. This webinar will present an assessment of key AI technologies today, and help participants identify promising applications based on matching requirements to mature-enough technologies.

AXA x DSSG Meetup Sharing (Feb 2016)

Eugene Yan Ziyou

Smart Data Webinar: Machine Learning Update

DATAVERSITY

Machine Learning (ML) approaches and their supporting technologies can generally be classified as Supervised vs Unsupervised, and within those categories as General or Deep Learning (with Reinforcement Learning as a special case within Supervised Learning). The approaches may be based on biological models or statistical models, or hybrids. As demand for machine learning functionality in consumer and enterprise applications increases, it becomes important to have a framework for comparing ML products and services. This webinar will present an overview of the machine learning landscape, from platform providers to point solutions in each major ML category, and help participants understand their options for experimentation and deployment of ML-based applications.

Insights Driven Intelligence through Knowledge Graphs

Neo4j

Introduction to Cognitive Computing the science behind and use of IBM Watson

Subhendu Dey

Data Science Application in Business Portfolio & Risk Management

Data Science Thailand

The slide has details on below points: 1. Introduction to Machine Learning 2. What are the challenges in acceptance of Machine Learning in Banks 3. How to overcome the challenges in adoption of Machine Learning in Banks 4. How to find new use cases of Machine Learning 5. Few current interesting use cases of Machine Learning Please contact me (shekup@gmail.com) or connect with me on LinkedIn (https://www.linkedin.com/in/shekup/) for more explanation on ML and how it may help your business. The slides are inspired by: Survey & interviews done by me with Bankers & Technology Professionals Presentation from Google NEXT 2017 Presentation by DATUM on Youtube Royal Society Machine Learning Big Data & Social Analytics Course from MIT & GetSmarter

Leading the Future

AnandSRao1962

Quantamental Investing - Merging Machine Learning, Fundamentals, & Insight

gurrajsangha

Webinar: Building a Business Case for Enterprise Search

Lucidworks

1. The Importance of Graphs in Government

Neo4j

Smart Data Webinar: Transforming Industries with Artificial Intelligence (AI)...

DATAVERSITY

The state of the art and practice for AI and Machine Learning (ML) has matured rapidly in the past few years, making it an ideal time to take a look at what works and what doesn’t. In this webinar, we will present an overview of AI-infused applications in two industries: Manufacturing Retail Participants will learn to look for characteristics of business processes and of data that make them well - or ill - suited to AI-augmentation or automation.

Big data user group big data application - mar 2016

Chulalongkorn University

Analytics in banking preview deck - june 2013

Everest Group

This report provides a comprehensive understanding of the analytics services industry with focus on banking domain. Analytics adoption in the banking industry is covered in depth, exploring various aspects such as market size, key drivers, recent analytics initiatives, and challenges. The report also analyses the trends in analytics deals for various banking subverticals (cards, retail, commercial, and lending) and evaluates analytics capabilities of 20+ service providers in the banking space

Quant university MRM and machine learning

QuantUniversity

QU Summer school 2020 speaker Series - Session 7 A conversation with Quants, Thinkers and Innovators all challenged to innovate in turbulent times! Join QuantUniversity for a complimentary summer speaker series where you will hear from Quants, innovators, startups and Fintech experts on various topics in Quant Investing, Machine Learning, Optimization, Fintech, AI etc. Managing Machine Learning Models in the Financial Industry Lecture 1: Model Risk Management for AI and Machine Learning Artificial intelligence and machine learning are part of today’s modeler’s toolbox for building challenger models and new innovative models that address business needs. However, AI presents new and unique challenges for risk management, particularly for assessing, controlling, and managing model risk for models of limited transparency. Another key consideration is the speed at which these models can be developed, validated, and then deployed into productive use to be competitive adhering to a robust model risk management program. This talk will highlight best practices for integrating AI into model risk practices and showcase examples across the model lifecycle.

Satyam open analytics nycOpen Analytics

Blockchain for Auditors

Andrew Clark

Blockchain is a revolutionary technology for some industries and applications, however it is commonly misunderstand and confused with the cryptocurrency Bitcoin. Blockchain is the underlying technology that makes Bitcoin and other cryptocurrencies possible, but it can be used for much more than cryptocurrencies alone. This presentation explains what Blockchain is, how it works and expounds on the uses of blockchain technology outside of cryptocurrencies in order to equip IT auditors with the knowledge they need to advise their companies on blockchain implementations. Blockchain has several very real uses cases, such as in logistics and financial payments clearing, however there is a lot of misleading or false information out there. The basics between what a public and private blockchain is, how the chain operates, what are miners, what security considerations exist, etc. will be discussed, along with an overview of how to audit this technology. Understand what Blockchain is Describe the differences between public and private blockchains Understand where blockchain can be useful to companies, and where it is unnecessary What security considerations exist Be able to perform a basic Blockchain audit

Creating $100 million from Big Data Analytics in Banking

Guy Pearce

PROPEL . Austrian's Roadmap for Enterprise Linked Data

Semantic Web Company

Big data analytics in banking sector

Anil Rana

How to Prepare for 2025's Intelligence Technology

Arik Johnson

Big data & analytics for banking new york lars hamberg

Lars Hamberg

BIG DATA & ANALYTICS FOR BANKING SUMMIT, New York, 1 Dec 2015. Keynote address: "How Predictive Analytics will change the Financial Services Sector” Speaker : Lars Hamberg http://www.specialistspeakers.com/?p=8367 Overview & Outlook: Why Big Data will over-deliver on its hype and transform Financial Services; Use cases with Advanced Analytics and Big Data Analytics in Financial Services, in Production & Distribution of banking products; new opportunities for incumbents in tomorrow’s ecosystem; big data, bigdata, analytics, smart data, data analytics, digitization, digitalization, predictive analytics, sentiment analysis, financial services, banking, asset management, distribution, retail, trading, technology, innovation, fintech, wealth, asset management, investment industry, robo advisory, social investing, behavior, profiling, client segmentation, alias matching, semantic memory models, unstructured data, machine learning, pattern recognition

Artificial intelligence: PwC Top Issues

PwC

This second machine age has seen the rise of artificial intelligence (AI), or “intelligence” that is not the result of human cogitation. It is now ubiquitous in many commercial products, from search engines to virtual assistants. aI is the result of exponential growth in computing power, memory capacity, cloud computing, distributed and parallel processing, open-source solutions, and global connectivity of both people and machines. The massive amounts and the speed at which structured and unstructured (e.g., text, audio, video, sensor) data is being generated has made a necessity of speedily processing and generating meaningful, actionable insights from it.

Responsible AI

Neo4j

Intelligence Analysis & Deliverables

Elijah Ezendu

In pursuit of augmented intelligence

DataScienceAssociation

By Derek Wang - FOUNDER AND CEO AT STRATIFYD INC Description: Everyone is talking about big data but there are some misunderstanding. What is Augmented Intelligence. 1) Human vs computer 2)Human with computer is more powerful than computer or human 3) Augmented intelligence is human intelligence + machine learning Some case studies to indicate the power of augmented intelligence At Southern California Data Science Conference Sept.25.2016 at USC http://socaldatascience.org/ http://www.datalaus.com/en/

Extending and integrating a hybrid knowledge representation system into the c...

Valentina Rho

Key Expert Systems Concepts

Harmony Kwawu

What's hot

Machine learning in Banks

Abhishek Upadhyay

Leading the Future

AnandSRao1962

Quantamental Investing - Merging Machine Learning, Fundamentals, & Insight

gurrajsangha

Webinar: Building a Business Case for Enterprise Search

Lucidworks

1. The Importance of Graphs in Government

Neo4j

Smart Data Webinar: Transforming Industries with Artificial Intelligence (AI)...

DATAVERSITY

Big data user group big data application - mar 2016

Chulalongkorn University

Analytics in banking preview deck - june 2013

Everest Group

Quant university MRM and machine learning

QuantUniversity

Satyam open analytics nycOpen Analytics

Blockchain for Auditors

Andrew Clark

Creating $100 million from Big Data Analytics in Banking

Guy Pearce

PROPEL . Austrian's Roadmap for Enterprise Linked Data

Semantic Web Company

Big data analytics in banking sector

Anil Rana

How to Prepare for 2025's Intelligence Technology

Arik Johnson

Big data & analytics for banking new york lars hamberg

Lars Hamberg

Artificial intelligence: PwC Top Issues

PwC

Responsible AI

Neo4j

Intelligence Analysis & Deliverables

Elijah Ezendu

In pursuit of augmented intelligence

DataScienceAssociation

What's hot (20)

Machine learning in Banks

Leading the Future

Quantamental Investing - Merging Machine Learning, Fundamentals, & Insight

Webinar: Building a Business Case for Enterprise Search

1. The Importance of Graphs in Government

Smart Data Webinar: Transforming Industries with Artificial Intelligence (AI)...

Big data user group big data application - mar 2016

Analytics in banking preview deck - june 2013

Quant university MRM and machine learning

Satyam open analytics nyc

Blockchain for Auditors

Creating $100 million from Big Data Analytics in Banking

PROPEL . Austrian's Roadmap for Enterprise Linked Data

Big data analytics in banking sector

How to Prepare for 2025's Intelligence Technology

Big data & analytics for banking new york lars hamberg

Artificial intelligence: PwC Top Issues

Responsible AI

Intelligence Analysis & Deliverables

In pursuit of augmented intelligence

Viewers also liked

Extending and integrating a hybrid knowledge representation system into the c...

Valentina Rho

Key Expert Systems Concepts

Harmony Kwawu

Poster-An Expert System for Car Failure DiagnosisViralkumar Jayswal

Prepping the Analytics organization for Artificial Intelligence evolution

Ramkumar Ravichandran

This is a discussion document to be used at the Big Data Spain at Madrid on Nov 18th, 2016. The key takeaway from the deck is that AI is reality and much closer than we realize. It will impact our Analytics Community in a very different way vs. an average Consumer. We can shape and guide the revolution if we start preparing for it now - right from our mindset, design thinking principles and productization of Analytics (API-zation). AI is a need to address the problems of scale, speed, precision in the world that is getting more and more complex around us - it is not humanly possible to answer all the questions ourselves and we will need machines to do it for us. The flow of the story line begins with a reality check on popular misconceptions and some background on AI. It then delves into all the ways it can optimize the current flow and ends with the "Managing Innovation Playbook" a set of three steps that should guide our innovation programs - Strategy, Execution & Transformation, i.e., the principles that tell us what we want to get out of it, how to get it done and finally how much the benefits permanent and consistently improving. Would love to hear your feedback, thoughts and reactions.

Artificial Intelligence

Muhammad Ahad

Expert System - Artificial intelligence

Dr. Abdul Ahad Abro

Introduction to artificial intelligence

School of Tourism and Hospitality Management Sant Ignasi, Ramon Llull University

Deep Learning - The Past, Present and Future of Artificial Intelligence

Lukas Masuch

In the last couple of years, deep learning techniques have transformed the world of artificial intelligence. One by one, the abilities and techniques that humans once imagined were uniquely our own have begun to fall to the onslaught of ever more powerful machines. Deep neural networks are now better than humans at tasks such as face recognition and object recognition. They’ve mastered the ancient game of Go and thrashed the best human players. “The pace of progress in artificial general intelligence is incredible fast” (Elon Musk – CEO Tesla & SpaceX) leading to an AI that “would be either the best or the worst thing ever to happen to humanity” (Stephen Hawking – Physicist). What sparked this new hype? How is Deep Learning different from previous approaches? Let’s look behind the curtain and unravel the reality. This talk will introduce the core concept of deep learning, explore why Sundar Pichai (CEO Google) recently announced that “machine learning is a core transformative way by which Google is rethinking everything they are doing” and explain why “deep learning is probably one of the most exciting things that is happening in the computer industry“ (Jen-Hsun Huang – CEO NVIDIA).

Rules in Artificial Intelligence

Pierre Feillet

Artificial Intelligence in Project Management by Dr. Khaled A. Hamdy

Agile ME

Video recording of the Dr. Khaled's session can be found at https://youtu.be/TFNhvAXNU5E. The presentation explores how Artificial Intelligence (AI) can be used in the Project Management field. The origins and history of AI are discussed followed by a brief simplified explanation of the theories behind its application. The actual utilization of AI tools in the Project Management domain is discussed covering diverse areas such as Engineering Design, Cost Estimating and Bidding, Planning and Scheduling, Risk Management, Performance Prediction as well as Project Monitoring and Control. The presentation concludes by a brief discussion about Data Management and Knowledge Engineering and how they are used today to simplify (or complicate) our lives.

Encyclopedic Intelligence as Artificial Super Intelligence: Are You Ready To ...

Azamat Abdoullaev

At the WGS 2017 in Dubai, Musk warned leaders from 139 nations that AI developments should be closely monitored. What he failed to mention that AI has three levels to pass: Artificial Narrow Intelligence; Artificial General Intelligence; Artificial Super Intelligence Some AI Events https://lnkd.in/g5mQywF Are You Ready for AI Revolution? https://lnkd.in/gxFfkN9 https://lnkd.in/gC7bj_C https://lnkd.in/dYwXQ4V

Artificialintelligenceandexpertsystems 121119234025-phpapp02

Zuhaib Zaroon

Artificial intelligence

Prashant Tripathi

RULE-BASED INFERENCING SYSTEM FOR INFERTILITY DIAGNOSIS IN WOMEN

ijaia

Childlessness among married couples is a rising problem in India. One of the major factors of childlessness is due to being infertile of either one or both of wife or husband. Infertility refers to the failure of a couple to become pregnant after one year of regular unprotected sexual intercourse. Infertility is a life crisis with invisible losses, and its consequences are manifold. This paper is intended to propose a rule-based inferencing of infertility diagnosis of women using Java Expert System Shell (JESS). Such a system is essential because the percentage of childlessness due to infertility is rising very high these days in India. This framework is aimed to enhance the existing tools used to identify and diagnose infertility problems in Women in the state of Manipur. We have implemented here the user interface component using Java, the knowledge base of the system using the Java Expert System Shell (JESS) and the Java IDE of Netbeans 7.0 while the database component is using SQL. The proposed framework can be used as guidelines for infertility diagnosis for women to assists the physicians with their daily practices and women who had infertility problems.

Artificial intelligence bsc - iso 27001 information security

Ufuk Cebeci

Expert Systems

Asli Yazagan

Artificial Intelligence

Muhammad Ahad

Artificial Intelligence for Automated Decision Support Project

Valerii Klymchuk

Artificial Intelligence - Past, Present and Future

Grigory Sapunov

artificial Intelligence

Ramya SK

Viewers also liked (20)

Extending and integrating a hybrid knowledge representation system into the c...

Key Expert Systems Concepts

Poster-An Expert System for Car Failure Diagnosis

Prepping the Analytics organization for Artificial Intelligence evolution

Artificial Intelligence

Expert System - Artificial intelligence

Introduction to artificial intelligence

Deep Learning - The Past, Present and Future of Artificial Intelligence

Rules in Artificial Intelligence

Artificial Intelligence in Project Management by Dr. Khaled A. Hamdy

Encyclopedic Intelligence as Artificial Super Intelligence: Are You Ready To ...

Artificialintelligenceandexpertsystems 121119234025-phpapp02

Artificial intelligence

RULE-BASED INFERENCING SYSTEM FOR INFERTILITY DIAGNOSIS IN WOMEN

Artificial intelligence bsc - iso 27001 information security

Expert Systems

Artificial Intelligence

Artificial Intelligence for Automated Decision Support Project

Artificial Intelligence - Past, Present and Future

artificial Intelligence

Similar to "Smart Data Web: Connecting data and extracting knowledge", Prof. Dr. Hans Uszkoreit, Scientific Director at DFKI

SC4 Workshop 2: Soren Auer BDE project Overview

BigData_Europe

Eco-Systems for Smart Cities based on Open Urban Platforms

Open & Agile Smart Cities

Open Data Hub - Giulia Giussani - A look into the future of data sharing

South Tyrol Free Software Conference

Sustainability, efficiency, and innovation need data, but its availability is limited due to interoperability challenges and fear that the data may be misused. This is why a new approach is being created to enable cross-domain data sharing in a federated and sovereign manner: it is the new paradigm of “data spaces”. This keynote will give an insight on the data spaces approach and will shed light on its current developments and adoption on a European and global scale.

Semantic Data Enrichment: a Human-in-the-Loop Perspective

Università degli Studi di Milano-Bicocca

Data enrichment is vital for leveraging heterogeneous data sources in various business analyses, AI applications, and data-driven services. Knowledge Graphs (KGs) support the enrichment of heterogeneous data sources by making entities first-class citizens: links to entities help interconnect heterogeneous data pieces or even ease access to external data sources to eventually augment the original data. Data annotation algorithms to find and link entities in reference KGs, as well as to identify out-of-KG entities have been proposed and applied to different types of data, such as tables, and texts. However, despite recent progress in annotation algorithms, the output of these algorithms does not always meet the quality requirements that make the enriched data valuable in downstream applications. As a result, semantic data enrichment remains an effort-consuming and error-prone task. In this seminar, we discuss the relationships between annotation algorithms, data enrichment, and KG construction, highlighting challenges and open problems. In addition, we advocate for a native human-in-the-loop perspective that enables users to control the outcome of the enrichment and, eventually, improve the quality of the enriched data. We focus in particular on the annotation and enrichment of tabular data and briefly discuss the application of a similar paradigm to the enrichment of textual data in the legal domain, e.g., on court decisions and criminal investigation documents.

Current state of industrial IoT / Industrie 4.0 markets - IoT Tech Expo

IoTAnalytics

Current state of industrial IoT / Industrie 4.0 markets - IoT Tech Expo

Knud Lasse Lueth

Fraunhofer – SINTEF: towards an initiative on Data Sovereignty in Europe

Thorsten Huelsmann

Fraunhofer and SINTEF jointed Industrial Data Space Association in early 2016. Industrial Data Space stands for safer data exchange between companies where the producer of data remains the owner of the data and maintains sovereignty over the use of that data. IDS Association aims to define the conditions and governance for a reference architecture and interfaces aiming at international standards. This standard is actively developed and updated on the basis of use cases. It forms the basis for a number of certified software solutions and business models, the development of which is fostered by the association. Thorsten Huelsmann and Ernst H. Kristiansen talked on this topic during the German-Norwegian Dialogue on Bilateral and European Cooperation , September 29 2016 at Berlin.

Webinar Industrial Data Space Association: Introduction and Architecture

Thorsten Huelsmann

Industrial Data Space Association is an industry and user driven initiative to develop a global Industrial Data Space standard and reference architecture which provides data sovereignty. The work bases on use cases and supports certifiable software solutions and business models for the data economy. The Webinar by Lars Nagel and Sebastian Steinbuss gives and overview to the Industrial Data Space initiative and explains the Reference Architecture and ist main components.

Open Government Open Innovation and the Cloud

Mark Gayler

Economic Value of Open Data

Toon Vanagt

The economic value behind (Linked) Open Data - Toon Vanagt

Open Knowledge Belgium

Big data presentation, explanations and use cases in industrial sector

Nicolas Sarramagna

Lange - Industrial Data Space – Digital Sovereignty over Data

Vienna Data Science Group

Putting the L in front: from Open Data to Linked Open Data

Martin Kaltenböck

ICARUS @EBDVF 2018 - TransformingTransport Session (November 2018, Vienna)

ICARUS2020.aero

Big Data Analytics @ Munich Re - VIII. International Istanbul Insurance Confe...

SigortaTatbikatcilariDernegi

Idsa, smau, milano, 2019 10-24

Nadia Fabrizio

Hitachi Data Systems Big Data Roadmap

Hitachi Vantara

As more companies grow their business in global markets, they discover the need to capture new opportunities in a matter of days rather than months to have competitive advantage and to capture new market share. Their machines are producing terabytes of various data types — video, audio, Microsoft® SharePoint®, sensor data, Microsoft Excel® files — and leaders are searching for the right technologies to capture this data and help provide a better understanding of their business. The HDS big data product roadmap will help customers build a big data enterprise plan that ingests data faster and correlate meaningful data sets to create intelligence that’s easy to consume and helps leaders make the right business decisions. View this webcast to learn about Hitachi’s product roadmap to big data. For more information on HDS Big Data Solutions please visit: http://www.hds.com/solutions/it-strategies/big-data/?WT.ac=us_mg_sol_bigdat

Industrial Data Space Association - New Members, New Insights, New Future Dir...

Thorsten Huelsmann

Digitalisation is both an enabler and a driving force behind innovative business models. A key ability for innovative business models is to be able to combine data in one “ecosystem”: Services are decoupled from physical platforms/products, The architecture levels are decoupled, Products become platforms and vice versa, “Ecosystems“ develop around platforms, Innovation takes place cooperatively. Data as strategic resource enables smart services, products and our desired lifestyle of the future.

Linked Open Data (LOD) Pilot Austria

Martin Kaltenböck

Similar to "Smart Data Web: Connecting data and extracting knowledge", Prof. Dr. Hans Uszkoreit, Scientific Director at DFKI (20)

SC4 Workshop 2: Soren Auer BDE project Overview

Eco-Systems for Smart Cities based on Open Urban Platforms

Open Data Hub - Giulia Giussani - A look into the future of data sharing

Semantic Data Enrichment: a Human-in-the-Loop Perspective

Current state of industrial IoT / Industrie 4.0 markets - IoT Tech Expo

Fraunhofer – SINTEF: towards an initiative on Data Sovereignty in Europe

Webinar Industrial Data Space Association: Introduction and Architecture

Open Government Open Innovation and the Cloud

Economic Value of Open Data

The economic value behind (Linked) Open Data - Toon Vanagt

Big data presentation, explanations and use cases in industrial sector

Lange - Industrial Data Space – Digital Sovereignty over Data

Putting the L in front: from Open Data to Linked Open Data

ICARUS @EBDVF 2018 - TransformingTransport Session (November 2018, Vienna)

Big Data Analytics @ Munich Re - VIII. International Istanbul Insurance Confe...

Idsa, smau, milano, 2019 10-24

Hitachi Data Systems Big Data Roadmap

Industrial Data Space Association - New Members, New Insights, New Future Dir...

Linked Open Data (LOD) Pilot Austria

More from Dataconomy Media

Data Natives Paris v 10.0 | "Blockchain in Healthcare" - Lea Dias & David An...

Dataconomy Media

Data Natives Frankfurt v 11.0 | "Competitive advantages with knowledge graphs...

Dataconomy Media

The challenges of increasing complexity of organizations, companies and projects are obvious and omnipresent. Everywhere there are connections and dependencies that are often not adequately managed or not considered at all because of a lack of technology or expertise to uncover and leverage the relationships in data and information. In his presentation, Axel Morgner talks about graph technology and knowledge graphs as indispensable building blocks for successful companies.

Data Natives Frankfurt v 11.0 | "Can we be responsible for misuse of data & a...

Dataconomy Media

Data Natives Munich v 12.0 | "How to be more productive with Autonomous Data ...

Dataconomy Media

Data Natives meets DataRobot | "Build and deploy an anti-money laundering mo...

Dataconomy Media

Compliance departments within banks and other financial institutions are turning to machine learning for improving their Anti Money Laundering compliance activities. Today, the systems that aim to detect potentially suspicious activity are commonly rule-based, and suffer from ultra-high false positive rates. DataRobot will discuss how their Automated Machine Learning platform was successfully used for a real use case to reduce their false positives and to enhance their Anti-Money Laundering activities.

Data Natives Munich v 12.0 | "Political Data Science: A tale of Fake News, So...

Dataconomy Media

Trump, Brexit, Cambridge Analytica... In the last few years, we have had to confront the consequences of the use and misuse of data science algorithms in manipulating public opinion through social media. The use of private data to microtarget individuals is a daily practice (and a trillion-dollar industry), which has serious side-effects when the selling product is your political ideology. How can we cope with this new scenario?

Data Natives Vienna v 7.0 | "Building Kubernetes Operators with KUDO for Dat...

Dataconomy Media

Data Natives Vienna v 7.0 | "The Ingredients of Data Innovation" - Robbert de...

Dataconomy Media

Data Natives Cologne v 4.0 | "The Data Lorax: Planting the Seeds of Fairness...

Dataconomy Media

What does it take to build a good data product or service? Data practitioners always think about the technology, user experience and commercial viability. But rarely do they think about the implications of the systems they build. This talk will shed light on the impact of AI systems and the unintended consequences of the use of data in different products. It will also discuss our role, as data practitioners, in planting the seeds of fairness in the systems we build.

Data Natives Cologne v 4.0 | "How People Analytics Can Reveal the Hidden Aspe...

Dataconomy Media

Data Natives Amsterdam v 9.0 | "Ten Little Servers: A Story of no Downtime" -...

Dataconomy Media

Cloud Infrastructure is a hostile environment: a power supply failure or a network outage leads to downtime and big losses. There is nothing we can trust: a single server, a server rack, even a whole datacenter can fail, and if an application is fragile by design, disruption is inevitable. We must distribute our application and diversify cloud data strategy to survive disturbances of any scale. Apache Cassandra is a cloud-native platform-agnostic database that stores data with a distributed redundancy so it easily survives any issue. What to know how Apple and Netflix handle petabytes of data, keeping it highly available? Join us and listen to a story of 10 little servers and no downtime!

Data Natives Amsterdam v 9.0 | "Point in Time Labeling at Scale" - Timothy Th...

Dataconomy Media

Data Natives Hamburg v 6.0 | "Interpersonal behavior: observing Alex to under...

Dataconomy Media

Data Natives Hamburg v 6.0 | "About Surfing, Failing & Scaling" - Florian Sch...

Dataconomy Media

Data NativesBerlin v 20.0 | "Serving A/B experimentation platform end-to-end"...

Dataconomy Media

Data Natives Berlin v 20.0 | "Ten Little Servers: A Story of no Downtime" - A...

Dataconomy Media

Big Data Frankfurt meets Thinkport | "The Cloud as a Driver of Innovation" - ...

Dataconomy Media

Creativity is the mental ability to create new ideas and designs. Innovation, on the other hand, Means developing useful solutions from new ideas. Creativity can be goal-oriented, Whereas innovation is always goal-oriented. This bedeutet, dass innovation aims to achieve defined goals. The use of cloud services and technologies promises enterprise users many benefits in terms of more flexible use of IT resources and faster access to innovative solutions. That’s why we want to examine the question in this talk, of what role cloud computing plays for innovation in companies.

Thinkport meets Frankfurt | "Financial Time Series Analysis using Wavelets" -...

Dataconomy Media

Big Data Helsinki v 3 | "Distributed Machine and Deep Learning at Scale with ...

Dataconomy Media

"With most machine learning (ML) and deep learning (DL) frameworks, it can take hours to move data for ETL, and hours to train models. It's also hard to scale, with data sets increasingly being larger than the capacity of any single server. The amount of the data also makes it hard to incrementally test and retrain models in near real-time. Learn how Apache Ignite and GridGain help to address limitations like ETL costs, scaling issues and Time-To-Market for the new models and help achieve near-real-time, continuous learning. Yuriy Babak, the head of ML/DL framework development at GridGain and Apache Ignite committer, will explain how ML/DL work with Apache Ignite, and how to get started. Topics include: — Overview of distributed ML/DL including architecture, implementation, usage patterns, pros and cons — Overview of Apache Ignite ML/DL, including built-in ML/DL algorithms, and how to implement your own — Model inference with Apache Ignite, including how to train models with other libraries, like Apache Spark, and deploy them in Ignite — How Apache Ignite and TensorFlow can be used together to build distributed DL model training and inference"

Big Data Helsinki v 3 | "Federated Learning and Privacy-preserving AI" - Oguz...

Dataconomy Media

"Machine learning algorithms require significant amounts of training data which has been centralized on one machine or in a datacenter so far. For numerous applications, such need of collecting data can be extremely privacy-invasive. Recent advancements in AI research approach this issue by a new paradigm of training AI models, i.e., Federated Learning. In federated learning, edge devices (phones, computers, cars etc.) collaboratively learn a shared AI model while keeping all the training data on device, decoupling the ability to do machine learning from the need to store the data in the cloud. From personal data perspective, this paradigm enables a way of training a model on the device without directly inspecting users’ data on a server. This talk will pinpoint several examples of AI applications benefiting from federated learning and the likely future of privacy-aware systems."

More from Dataconomy Media (20)