The document provides an overview of the Open Data Interoperability Platform (ODIP) and the DCAT Application Profile, which are tools aimed at promoting the reuse of open government data across Europe. ODIP allows data to be uniformly searched and discovered across different data portals. The DCAT Application Profile establishes a common vocabulary for describing datasets, based on the Data Catalog Vocabulary, to increase discoverability and reuse of data. It was developed by an international working group involving data portals and institutions across Europe.
Presentation / Workshop which will teach you the core patterns, concepts and visualisation options of D3.js (v4). Accompanying exercises can be found here: https://github.com/josdirksen/d3exercises
Enabling a Data Mesh Architecture with Data VirtualizationDenodo
Watch full webinar here: https://bit.ly/3rwWhyv
The Data Mesh architectural design was first proposed in 2019 by Zhamak Dehghani, principal technology consultant at Thoughtworks, a technology company that is closely associated with the development of distributed agile methodology. A data mesh is a distributed, de-centralized data infrastructure in which multiple autonomous domains manage and expose their own data, called “data products,” to the rest of the organization.
Organizations leverage data mesh architecture when they experience shortcomings in highly centralized architectures, such as the lack domain-specific expertise in data teams, the inflexibility of centralized data repositories in meeting the specific needs of different departments within large organizations, and the slow nature of centralized data infrastructures in provisioning data and responding to changes.
In this session, Pablo Alvarez, Global Director of Product Management at Denodo, explains how data virtualization is your best bet for implementing an effective data mesh architecture.
You will learn:
- How data mesh architecture not only enables better performance and agility, but also self-service data access
- The requirements for “data products” in the data mesh world, and how data virtualization supports them
- How data virtualization enables domains in a data mesh to be truly autonomous
- Why a data lake is not automatically a data mesh
- How to implement a simple, functional data mesh architecture using data virtualization
Presentation / Workshop which will teach you the core patterns, concepts and visualisation options of D3.js (v4). Accompanying exercises can be found here: https://github.com/josdirksen/d3exercises
Enabling a Data Mesh Architecture with Data VirtualizationDenodo
Watch full webinar here: https://bit.ly/3rwWhyv
The Data Mesh architectural design was first proposed in 2019 by Zhamak Dehghani, principal technology consultant at Thoughtworks, a technology company that is closely associated with the development of distributed agile methodology. A data mesh is a distributed, de-centralized data infrastructure in which multiple autonomous domains manage and expose their own data, called “data products,” to the rest of the organization.
Organizations leverage data mesh architecture when they experience shortcomings in highly centralized architectures, such as the lack domain-specific expertise in data teams, the inflexibility of centralized data repositories in meeting the specific needs of different departments within large organizations, and the slow nature of centralized data infrastructures in provisioning data and responding to changes.
In this session, Pablo Alvarez, Global Director of Product Management at Denodo, explains how data virtualization is your best bet for implementing an effective data mesh architecture.
You will learn:
- How data mesh architecture not only enables better performance and agility, but also self-service data access
- The requirements for “data products” in the data mesh world, and how data virtualization supports them
- How data virtualization enables domains in a data mesh to be truly autonomous
- Why a data lake is not automatically a data mesh
- How to implement a simple, functional data mesh architecture using data virtualization
Building a Logical Data Fabric using Data Virtualization (ASEAN)Denodo
Watch full webinar here: https://bit.ly/3FF1ubd
In the recent Building the Unified Data Warehouse and Data Lake report by leading industry analysts TDWI, we have discovered 64% of organizations stated the objective for a unified Data Warehouse and Data Lakes is to get more business value and 84% of organizations polled felt that a unified approach to Data Warehouses and Data Lakes was either extremely or moderately important.
In this session, you will learn how your organization can apply a logical data fabric and the associated technologies of machine learning, artificial intelligence, and data virtualization can reduce time to value. Hence, increasing the overall business value of your data assets.
KEY TAKEAWAYS:
- How a Logical Data Fabric is the right approach to assist organizations to unify their data.
- The advanced features of a Logical Data Fabric that assist with the democratization of data, providing an agile and governed approach to business analytics and data science.
- How a Logical Data Fabric with Data Virtualization enhances your legacy data integration landscape to simplify data access and encourage self-service.
This is the presentation for the lecture of Dimitar Mitov "Data Analytics with Dremio" (in Bulgarian), part of OpenFest 2022: https://www.openfest.org/2022/bg/full-schedule-bg/
Denodo as the Core Pillar of your API StrategyDenodo
Watch full webinar here: https://buff.ly/2KTz2IB
Most people associate data virtualization with BI and analytics. However, one of the core ideas behind data virtualization is the decoupling of the consumption method from the data model. Why should the need for data requests in JSON over HTTP require extra development? Denodo provides immediate access to its datasets via REST, OData 4, GeoJSON and other protocols, with no coding involved. Easy to scale, cloud friendly and ready to integrate with API management tools, Denodo can be the perfect tool to fulfill your API strategy!
Attend this session to learn:
- What’s the role of Denodo in an API strategy
- Integration between Denodo and other elements of the API stack, like API management tools
- How easy it is to access Denodo as a RESTful endpoint
- Advanced options of Denodo web services: OAuth, OpenAPI, geographical capabilities, etc.
Big Data Fabric: A Recipe for Big Data InitiativesDenodo
Big data fabric combines essential big data capabilities in a single platform to automate the many facets of data discovery, preparation, curation, orchestration, and integration across a multitude of data sources. Attend this session to learn how Big Data Fabric enabled by data virtualization constitutes a recipe for:
• Enabling new actionable insights with minimal effort
• Securing big data end-to-end
• Addressing big data skillset scarcity
• Providing easy access to data without having to decipher various data formats
Agenda:
• Big Data with Data Virtualization
• Product Demonstration
• Summary & Next Steps
• Q&A
Watch webinar on demand here: https://goo.gl/EpmIBx
This webinar is part of the Data Virtualization Packed Lunch Webinar Series: https://goo.gl/W1BeCb
This presenation explains basics of ETL (Extract-Transform-Load) concept in relation to such data solutions as data warehousing, data migration, or data integration. CloverETL is presented closely as an example of enterprise ETL tool. It also covers typical phases of data integration projects.
LDM Slides: Data Modeling for XML and JSONDATAVERSITY
Data modeling has traditionally focused on relational database systems. But in the age of the internet, technologies such as XML and JSON have evolved to provide structure and definition to “data in motion”. Have data modeling technologies evolved to support these technologies? Can we use traditional approaches to model data in XML and JSON? Or are new tools and methodologies required? Join this webinar to discuss:
- XML & JSON vs. Relational Database Modeling
- Techniques & Tools for Data Modeling for XML
- Techniques & Tools for Data Modeling for JSON
- Use Cases & Opportunities for XML and JSON Data Modeling
In this session you will learn how Qlik’s Data Integration platform (formerly Attunity) reduces time to market and time to insights for modern data architectures through real-time automated pipelines for data warehouse and data lake initiatives. Hear how pipeline automation has impacted large financial services organizations ability to rapidly deliver value and see how to build an automated near real-time pipeline to efficiently load and transform data into a Snowflake data warehouse on AWS in under 10 minutes.
Business Intelligence (BI) and Data Management Basics amorshed
A one-day training course on the Concepts of Data Management and Business Intelligence (BI) in the DX age
A Basic Review of BI and DM
How to Implement BI
A review of BI Tools and 2022 Gartner Quadrant Magic
Basics of Data warehouse (DWH)
An introductions to Power BI
Components of Power BI
Steps for BI Implementation
Data Culture
Intro to ETL and ELT
OLAP files and Architecture
Digital transformation or DX review
A glance at DMBOK2.0 framework
BI Challenges
Data Governance
Data Integration
Data Security and Privacy in DMBOK2.0
Data-Driven Organization
Data and BI Maturity Model
Traditional BI
Self-service BI
who is DMP
who is BI developer
what is Metadata
what is Master data
Data Quality
Data Literacy
Benefits of BI
BI features
How does BI Works?
Modern BI
Data Analytics
BI Architecture
Data Types
Data Lake
Data Mart
Data Silo
Data Visualization
Power BI Architecture and components
Qlik integration with Salesforce is unique, powerful and differentiating, this presentation provides insights into the options users of the Qlik platform have
This session will bring you the opportunity to discover how FIWARE will make Data Spaces happen! Contents will give all the details and insights around the path taken in this strategic area. An introduction will provide the overall vision on Data Spaces, the status of the Data Spaces Business Alliance (DSBA) Technical Convergence activities, and initial considerations around the concept of FIWARE Data Space Connector, the first dataspace connector that will comply with the Data Space Business Alliance recommendations.
Different coordination and support actions of the Digital Europe Programme (DEP) in the Data Spaces domain will also be presented, as well as initial outputs from these projects. It will provide insights about the opportunities to influence and drive decisions within this important program of the European Union.
A series of presentations will deep dive into technical details about the minimum viable framework recommended in DSBA: the standards proposed and how they integrate together. Concretely, presentations will focus on the pillars linked to decentralized Trust, Identity & Access Management and the pillar for Data Value creation covering aspects for Monetization and Marketplace services.
Several presentations will tackle elements that open the discussion around the evolution of Data Spaces, as well as components expected to be integrated in the concept of Data Space Connector. They will be followed by use cases that provide insight on what is being developed and testimonies on how technologies based on Data Spaces concepts previously displayed are being used in real life scenarios.
5 Critical Steps to Clean Your Data Swamp When Migrating Off of HadoopDatabricks
In this session, learn how to quickly supplement your on-premises Hadoop environment with a simple, open, and collaborative cloud architecture that enables you to generate greater value with scaled application of analytics and AI on all your data. You will also learn five critical steps for a successful migration to the Databricks Lakehouse Platform along with the resources available to help you begin to re-skill your data teams.
Data Ingestion in Big Data and IoT platformsGuido Schmutz
Many of the Big Data and IoT use cases are based on combining data from multiple data sources and to make them available on a Big Data platform for analysis. The data sources are often very heterogeneous, from simple files, databases to high-volume event streams from sensors (IoT devices). It’s important to retrieve this data in a secure and reliable manner and integrate it with the Big Data platform so that it is available for analysis in real-time (stream processing) as well as in batch (typical big data processing). In past some new tools have emerged, which are especially capable of handling the process of integrating data from outside, often called Data Ingestion. From an outside perspective, they are very similar to a traditional Enterprise Service Bus infrastructures, which in larger organization are often in use to handle message-driven and service-oriented systems. But there are also important differences, they are typically easier to scale in a horizontal fashion, offer a more distributed setup, are capable of handling high-volumes of data/messages, provide a very detailed monitoring on message level and integrate very well with the Hadoop ecosystem. This session will present and compare Apache NiFi, StreamSets and the Kafka Ecosystem and show how they handle the data ingestion in a Big Data solution architecture.
Building a Logical Data Fabric using Data Virtualization (ASEAN)Denodo
Watch full webinar here: https://bit.ly/3FF1ubd
In the recent Building the Unified Data Warehouse and Data Lake report by leading industry analysts TDWI, we have discovered 64% of organizations stated the objective for a unified Data Warehouse and Data Lakes is to get more business value and 84% of organizations polled felt that a unified approach to Data Warehouses and Data Lakes was either extremely or moderately important.
In this session, you will learn how your organization can apply a logical data fabric and the associated technologies of machine learning, artificial intelligence, and data virtualization can reduce time to value. Hence, increasing the overall business value of your data assets.
KEY TAKEAWAYS:
- How a Logical Data Fabric is the right approach to assist organizations to unify their data.
- The advanced features of a Logical Data Fabric that assist with the democratization of data, providing an agile and governed approach to business analytics and data science.
- How a Logical Data Fabric with Data Virtualization enhances your legacy data integration landscape to simplify data access and encourage self-service.
This is the presentation for the lecture of Dimitar Mitov "Data Analytics with Dremio" (in Bulgarian), part of OpenFest 2022: https://www.openfest.org/2022/bg/full-schedule-bg/
Denodo as the Core Pillar of your API StrategyDenodo
Watch full webinar here: https://buff.ly/2KTz2IB
Most people associate data virtualization with BI and analytics. However, one of the core ideas behind data virtualization is the decoupling of the consumption method from the data model. Why should the need for data requests in JSON over HTTP require extra development? Denodo provides immediate access to its datasets via REST, OData 4, GeoJSON and other protocols, with no coding involved. Easy to scale, cloud friendly and ready to integrate with API management tools, Denodo can be the perfect tool to fulfill your API strategy!
Attend this session to learn:
- What’s the role of Denodo in an API strategy
- Integration between Denodo and other elements of the API stack, like API management tools
- How easy it is to access Denodo as a RESTful endpoint
- Advanced options of Denodo web services: OAuth, OpenAPI, geographical capabilities, etc.
Big Data Fabric: A Recipe for Big Data InitiativesDenodo
Big data fabric combines essential big data capabilities in a single platform to automate the many facets of data discovery, preparation, curation, orchestration, and integration across a multitude of data sources. Attend this session to learn how Big Data Fabric enabled by data virtualization constitutes a recipe for:
• Enabling new actionable insights with minimal effort
• Securing big data end-to-end
• Addressing big data skillset scarcity
• Providing easy access to data without having to decipher various data formats
Agenda:
• Big Data with Data Virtualization
• Product Demonstration
• Summary & Next Steps
• Q&A
Watch webinar on demand here: https://goo.gl/EpmIBx
This webinar is part of the Data Virtualization Packed Lunch Webinar Series: https://goo.gl/W1BeCb
This presenation explains basics of ETL (Extract-Transform-Load) concept in relation to such data solutions as data warehousing, data migration, or data integration. CloverETL is presented closely as an example of enterprise ETL tool. It also covers typical phases of data integration projects.
LDM Slides: Data Modeling for XML and JSONDATAVERSITY
Data modeling has traditionally focused on relational database systems. But in the age of the internet, technologies such as XML and JSON have evolved to provide structure and definition to “data in motion”. Have data modeling technologies evolved to support these technologies? Can we use traditional approaches to model data in XML and JSON? Or are new tools and methodologies required? Join this webinar to discuss:
- XML & JSON vs. Relational Database Modeling
- Techniques & Tools for Data Modeling for XML
- Techniques & Tools for Data Modeling for JSON
- Use Cases & Opportunities for XML and JSON Data Modeling
In this session you will learn how Qlik’s Data Integration platform (formerly Attunity) reduces time to market and time to insights for modern data architectures through real-time automated pipelines for data warehouse and data lake initiatives. Hear how pipeline automation has impacted large financial services organizations ability to rapidly deliver value and see how to build an automated near real-time pipeline to efficiently load and transform data into a Snowflake data warehouse on AWS in under 10 minutes.
Business Intelligence (BI) and Data Management Basics amorshed
A one-day training course on the Concepts of Data Management and Business Intelligence (BI) in the DX age
A Basic Review of BI and DM
How to Implement BI
A review of BI Tools and 2022 Gartner Quadrant Magic
Basics of Data warehouse (DWH)
An introductions to Power BI
Components of Power BI
Steps for BI Implementation
Data Culture
Intro to ETL and ELT
OLAP files and Architecture
Digital transformation or DX review
A glance at DMBOK2.0 framework
BI Challenges
Data Governance
Data Integration
Data Security and Privacy in DMBOK2.0
Data-Driven Organization
Data and BI Maturity Model
Traditional BI
Self-service BI
who is DMP
who is BI developer
what is Metadata
what is Master data
Data Quality
Data Literacy
Benefits of BI
BI features
How does BI Works?
Modern BI
Data Analytics
BI Architecture
Data Types
Data Lake
Data Mart
Data Silo
Data Visualization
Power BI Architecture and components
Qlik integration with Salesforce is unique, powerful and differentiating, this presentation provides insights into the options users of the Qlik platform have
This session will bring you the opportunity to discover how FIWARE will make Data Spaces happen! Contents will give all the details and insights around the path taken in this strategic area. An introduction will provide the overall vision on Data Spaces, the status of the Data Spaces Business Alliance (DSBA) Technical Convergence activities, and initial considerations around the concept of FIWARE Data Space Connector, the first dataspace connector that will comply with the Data Space Business Alliance recommendations.
Different coordination and support actions of the Digital Europe Programme (DEP) in the Data Spaces domain will also be presented, as well as initial outputs from these projects. It will provide insights about the opportunities to influence and drive decisions within this important program of the European Union.
A series of presentations will deep dive into technical details about the minimum viable framework recommended in DSBA: the standards proposed and how they integrate together. Concretely, presentations will focus on the pillars linked to decentralized Trust, Identity & Access Management and the pillar for Data Value creation covering aspects for Monetization and Marketplace services.
Several presentations will tackle elements that open the discussion around the evolution of Data Spaces, as well as components expected to be integrated in the concept of Data Space Connector. They will be followed by use cases that provide insight on what is being developed and testimonies on how technologies based on Data Spaces concepts previously displayed are being used in real life scenarios.
5 Critical Steps to Clean Your Data Swamp When Migrating Off of HadoopDatabricks
In this session, learn how to quickly supplement your on-premises Hadoop environment with a simple, open, and collaborative cloud architecture that enables you to generate greater value with scaled application of analytics and AI on all your data. You will also learn five critical steps for a successful migration to the Databricks Lakehouse Platform along with the resources available to help you begin to re-skill your data teams.
Data Ingestion in Big Data and IoT platformsGuido Schmutz
Many of the Big Data and IoT use cases are based on combining data from multiple data sources and to make them available on a Big Data platform for analysis. The data sources are often very heterogeneous, from simple files, databases to high-volume event streams from sensors (IoT devices). It’s important to retrieve this data in a secure and reliable manner and integrate it with the Big Data platform so that it is available for analysis in real-time (stream processing) as well as in batch (typical big data processing). In past some new tools have emerged, which are especially capable of handling the process of integrating data from outside, often called Data Ingestion. From an outside perspective, they are very similar to a traditional Enterprise Service Bus infrastructures, which in larger organization are often in use to handle message-driven and service-oriented systems. But there are also important differences, they are typically easier to scale in a horizontal fashion, offer a more distributed setup, are capable of handling high-volumes of data/messages, provide a very detailed monitoring on message level and integrate very well with the Hadoop ecosystem. This session will present and compare Apache NiFi, StreamSets and the Kafka Ecosystem and show how they handle the data ingestion in a Big Data solution architecture.
Good metadata is critical to helping people find information. Metadata can be used to enhance search tools, drive navigation and relate documents to one another. Unfortunately, manually adding metadata to content is cumbersome for small batches of content and impractical or impossible for large content sets.
Enterprise Knowledge understands the difficulty and importance of maintaining metadata. In this session, we will share 6 different ways to simplify and/or automate metadata management even on extremely large content sets. We will share the tools and techniques we have used with our clients to make metadata management possible and provide real world examples as to how these techniques can be applied to your content.
Talend Metadata Bridge accelerates the design and continuous improvement of integration scenarios by sharing, synchronizing and managing metadata across your enterprise data environment.
Developers save time by focusing on design and improving productivity to apply change, architects gain agility to keep their projects up to date with the latest business needs and technologies, and business users can access to crucial information about their data for reporting, validation, auditing and compliance.
The presentation gives an overview of what metadata is and why it is important. It also addresses the benefits that metadata can bring and offers advice and tips on how to produce good quality metadata and, to close, how EUDAT uses metadata in the B2FIND service.
November 2016
Presentation delivered by Ludo Hendrickx and Joris Beek on 11 December 2013 Dutch at the Ministry of Interior, The Hague, The Netherlands. More information on: https://joinup.ec.europa.eu/community/ods/description
A presentation given on the Horizon 2020 open data pilot as part of a series of OpenAIRE webinars for Open Access week 2014 - http://www.fosteropenscience.eu/event/openaire-webinars-during-oa-week-2014
The Horizon 2020 Open Data Pilot - OpenAIRE webinar (Oct. 21 2014) by Sarah J...OpenAIRE
Sarah Jones (HATII, Digital Curation Center) will provide more information on the Open Research Data Pilot in H2020: who should participate and how to comply (in collaboration with FOSTER)
Date: Tuesday, October 21 2014
Data management plans – EUDAT Best practices and case study | www.eudat.euEUDAT
| www.eudat.eu | Presentation given by Stéphane Coutin during the PRACE 2017 Spring School joint training event with the EU H2020 VI-SEEM project (https://vi-seem.eu/) organised by CaSToRC at The Cyprus Institute. Science and more specifically projects using HPC is facing a digital data explosion. Instruments and simulations are producing more and more volume; data can be shared, mined, cited, preserved… They are a great asset, but they are facing risks: we can miss storage, we can lose them, they can be misused,… To start this session, we will review why it is important to manage research data and how to do this by maintaining a Data Management Plan. This will be based on the best practices from EUDAT H2020 project and European Commission recommendation. During the second part we will interactively draft a DMP for a given use case.
Presentation describing the purpose of the European Data Portal project. The launching of the European Data Portal is one of the key steps the European Commission is taking in supporting the access to public data.
Putting the L in front: from Open Data to Linked Open DataMartin Kaltenböck
Keynote presentation of Martin Kaltenböck (LOD2 project, Semantic Web Company) at the Government Linked Data Workshop in the course of the OGD Camp 2011 in Warsaw, Poland: Putting the L in front: from Open Data to Linked Open Data
OSFair2017 Workshop | Service provisioning for excellent sciencesOpen Science Fair
Daan Broeder presents the EUDAT community
Workshop title: Organising high-quality research data management services
Workshop abstract:
Open science needs high quality data management where researchers can create, use and share data according to well defined standards and practices. this is one of the pillars of Open Science. In the data management landscape we find quite a few organisations that aim at achieving this, however to get it right, a collaboration is called for where all can play a suitable role and present this in a consistent way to the researcher.
The proposed workshop brings together representatives of standard organisation (RDA), eInfrastructures (EUDAT) and Libraries (LIBER) that together can organise the high quality data management for research.
DAY 1 - PARALLEL SESSION 2
http://opensciencefair.eu/workshops/organising-high-quality-research-data-management-services
The Role of Logical Data Fabric in a Unified Platform for Modern Analytics (A...Denodo
Watch full webinar here: https://bit.ly/3BuphcW
Join us for a webinar based on TDWI’s recent Best Practice Report, Unified Platforms for Modern Analytics, where we will discuss the role of the logical data fabric in a unified platform for modern analytics, focusing on several of the key findings outlined in this report.
The Role of the Logical Data Fabric in a Unified Platform for Modern AnalyticsDenodo
Watch full webinar here: https://bit.ly/3FHKalT
Given the growing demand for analytics and the need for organizations to advance beyond dashboards to self-service analytics and more sophisticated algorithms like machine learning (ML), enterprises are moving towards a unified environment for data and analytics. What is the best approach to accomplish this unification?
In TDWI’s recent Best Practice Report, Unified Platforms for Modern Analytics, written by Fern Halper, TDWI VP Research, Senior Research Director for Advanced Analytics, adoption, use, challenges, architectures, and best practices for unified platforms for modern analytics is explored. One of the approaches for unification outlined in the report is a data fabric approach.
Join us for a webinar with our Director of Product Marketing, Robin Tandon, where he will discuss the role of the logical data fabric in a unified platform for modern analytics, focusing on several of the key findings outlined in this report. He will share insights and use case examples that demonstrate how a properly implemented logical data fabric is the most suitable approach for Unified Data Platforms across enterprises and organizations.
Watch on-demand & Learn:
- The benefits of a unified platform and its ability to capture diverse & emerging data types and how to support high performance and scalable solutions.
- The role of an enhanced AI driven data catalog and its implications towards the findings in the best practice report.
- Implications of a logical data fabric as it relates to several of the recommendations outlined in the report.
Similar to Promoting the re use of open data through ODIP (20)
This module supported the training on Linked Open Data delivered to the EU Institutions on 30 November 2015 in Brussels. https://joinup.ec.europa.eu/community/ods/news/ods-onsite-training-european-commission
Linked Open Data Principles, Technologies and ExamplesOpen Data Support
Theoretical and practical introducton to linked data, focusing both on the value proposition, the theory/foundations, and on practical examples. The material is tailored to the context of the EU institutions.
Polish version of training module 1.2 Design and Manage Persistent URIs.
Remark: This slide deck may slightly differ from the original one in English, German and French because it has been specifically used for the training in Poland.
Polish version of training module 1.4 Introduction to Metadata Management.
Remark: This slide deck may slightly differ from the original one in English, German and French because it has been specifically used for the training in Poland.
Polish version of training module 1.2 Introduction to Linked Data.
Remark: This slide deck may slightly differ from the original one in English, German and French because it has been specifically used for the training in Poland.
Open Data Support onsite training in Italy (Italian)Open Data Support
The ODS training was given on 16 November on the Smart City Exhibition 2013 in the city of Bologna.
The original ODS material in this slide deck has been translated to Italian.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
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1. DATA
SUPPORT
OPEN
Training Module 1.5
Promoting the reuse
of Open Government
Data through the
Open Data
Interoperability
Platform (ODIP)
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3. DATASUPPORTOPEN
Learning objectives
By the end of this training module you should have an understanding
of:
• How you can overcome the barriers of reuse for your datasets.
• How Open Data Support can promote the reuse of datasets.
• What the DCAT Application Profile is and how it can be used.
• What Open Data Interoperability Platform (ODIP) is and how it can
be used.
Slide 3
4. DATASUPPORTOPEN
Content
This module contains...
• An outline of the context of Open Government Data in Europe.
• An outline of the Open Data Support project.
• Information about the DCAT Application Profile for Data Portals in
Europe as a homogenised metadata model.
• Information on how to use the Open Data Interoperability Platform.
Slide 4
5. DATASUPPORTOPEN
There are more than 160 portals in Europe
hosting Open Government Data
160+
Slide 5
Provenance?
Licence? Persistence?
Trust? Availability?
Quality?
6. DATASUPPORTOPEN
Open Data has a great potential to create
social and economic value
Slide 6
Developers /
Companies integrate
data into app
(services)
Public
administrations
share data online
Citizens/businesses
benefits from the
app (services)
Developers /
Companies
search for data
Publishing data
Reusing data
7. DATASUPPORTOPEN
Barriers to Open Data publishing and reuse
Data publishers Data reusers
No view on which data is more likely
to be reused / has a higher ROI
potential.
Lack of overview of existing/available
datasets.
Unclear business model for
publishing Open Data.
Unclear business model for reusing
Open Data.
Limited tool support. Data is often of low quality, outdated,
unstructured and/or not machine-
readable.
Competing licenses for datasets. Lack of licensing information or
incompatible licenses.
Competing vocabularies for
describing datasets.
Different vocabularies when searching
for datasets.
Domain-specific metadata needs. Lack of (good quality) metadata.
Effort required for keeping the
metadata up-to-date.
Lack of provenance information.
Slide 7
Metadata
Metadata
8. DATASUPPORTOPEN
No reuse = No social and economic value
Slide 8
Public
administrations
share data online
Developers /
Companies integrate
data into app
(services)
Citizens/businesses
benefits from the
app (services)
Developers /
Companies
search for data
10. DATASUPPORTOPEN
Open Data Support mission...
Slide 10
To Improve the and facilitate the
to datasets published on local and
national Open Data portals in order to
increase their within and across
borders.
See also:
http://www.slideshare.net/OpenDataSupport
13. DATASUPPORTOPEN
A shared initiative of ...
Funded by the ISA Programme under Action 1.1.
“Improving semantic interoperability in European
eGovernment systems” (a.k.a the SEMIC project).
Slide 13
14. DATASUPPORTOPEN
An international Working Group of experts
• Chair: Antonio Carneiro (Publications Office)
• 59 Working Group members representing:
- 15 different European Member States
(UK,IT,ES,DK,DE,SK,BE,AT,SE,FI,FR,IE,NL,GR,SI )
- US
- Several European Institutions and international organisations
- 40 different Data Portals
Slide 14
See also:
https://joinup.ec.europa.eu/asset/dcat_application_profile/description
15. DATASUPPORTOPEN
By using a common metadata schema to describe
datasets and sharing metadata...
• Data publishers increase discoverability and thus reuse
of their data.
• Data reusers can uniformly search across platforms
without facing difficulties caused by the use of separate
models or language differences.
Slide 15
The quality and the availability of the description metadata directly
affects how easily datasets can be found!
19. DATASUPPORTOPEN
Usage of the DCAT Application Profile
Mandatory class: a receiver of data MUST be able to process information about
instances of the class; a sender of data MUST provide information about instances of
the class.
Recommended class: a receiver of data MUST be able to process information
about instances of the class; a sender of data MUST provide information about
instances of the class, if it is available.
Optional class: a receiver MUST be able to process information about instances of
the class; a sender MAY provide the information but is not obliged to do so.
Mandatory property: a receiver MUST be able to process the information for that
property; a sender MUST provide the information for that property.
Recommended property: a receiver MUST be able to process the information for
that property; a sender SHOULD provide the information for that property if it is
available.
Optional property: a receiver MUST be able to process the information for that
property; a sender MAY provide the information for that property but is not obliged
to do so.
Slide 19
20. DATASUPPORTOPEN
Controlled vocabularies
Slide 20
Property URI Used for Class Proposed vocabulary
dcat:mediaType Distribution MDR File types Name Authority List
dcat:theme Dataset EuroVoc domains
dcat:themeTaxonomy Catalog EuroVoc
dct:accrualPeriodicity Dataset
Dublin Core Collection Description Frequency
Vocabulary
dct:format Distribution MDR File Type Named Authority List
dct:language Catalog, Dataset MDR Languages Named Authority List
dct:publisher Catalog, Dataset MDR Corporate bodies Named Authority List
dct:spatial Catalog, Dataset
MDR Countries Named Authority List, MDR Places
Named Authority List
adms:status CatalogRecord ADMS change type vocabulary
dct:type License Document ADMS license type vocabulary
22. DATASUPPORTOPEN
Example description of dataset with the DCAT-AP
<rdf:Description rdf:about=“http://data.gov.uk/data ">
<rdf:type rdf:resource=“http://www.w3.org/ns/dcat#Catalog”/>
<dct:title xml:lang=“en”>data.gov.uk</dct:title>
<dct:description xml:lang=“en”>Description of the data portal</dct:description>
<dct:license rdf:resource=” http://www.nationalarchives.gov.uk/doc/open-government-licence”/>
</rdf:Description>
<rdf:Description rdf:about=“http://data.gov.uk/dataset/east-sussex-county-council-election-results”/>
<rdf:type rdf:resource=“http://www.w3.org/ns/dcat#Dataset”/>
<dct:title xml:lang=”en”>East Sussex County Council election results</dct:title>
<dct:description xml:lang=“en”>A list of elections to East Sussex County Council, which leads to data about candidates,
parties, electoral divisions and votes cast. Uses the Open Election Data RDF vocabulary from http://openelectiondata.org/
</dct:description>
</rdf:Description>
<rdf:Description rdf:adbout=“http://www.eastsussex.gov.uk/yourcouncil/localelections/election2009/default.aspx”/>
<rdf:type rdf:resource=“http://www.w3.org/ns/dcat#Distribution”/>
<dct:title xml:lang=“en”>East Sussex County Council election 4 June 2009, and subsequent bi-elections</dct:title>
<dcat:accessURL rdf:resource=“http://www.eastsussex.gov.uk/yourcouncil/localelections/election2009/default.aspx “/>
<dct:license rdf:resource=“http://www.nationalarchives.gov.uk/doc/open-government-licence”/>
</rdf:Description>
Slide 22
24. DATASUPPORTOPEN
Where can you find it?
Slide 24
https://joinup.ec.europa.eu/asset/dcat_application_profile/description
25. DATASUPPORTOPEN
Share the metadata of
you datasets on ODIP
The Open Data Interoperability Platform (ODIP) enables
you to share metadata of datasets described using the
DCAT-AP, thus improving the discoverability and
visibility of your datasets, eventually leading to wider
reuse.
Slide 25
26. DATASUPPORTOPEN
What can ODIP do?
• Harvest metadata from an Open
Data portal.
• Transform the metadata to RDF.
• Harmonise the RDF metadata
produced in the previous steps with
DCAT-AP.
• Validate the harmonised metadata
against the DCAT-AP.
• Publish the description metadata as
Linked Open Metadata.
• Translate metadata automatically in
English
Slide 26
ODIPP
Pan-European
Data portal
27. DATASUPPORTOPEN
How can ODIP help you improve your metadata?
• ODIP maps your metadata to a standard model, i.e. the DCAT-AP.
• ODIP helps you reuse standardised multilingual controlled
vocabularies in your metadata, replacing error-prone text values or
tailor-made lists.
• By means of its validation services, ODIP allows you to detect
inconsistencies and errors in your metadata.
• ODIP assigns persistent URIs to your metadata.
• ODIP links your metadata with other metadata, thus adding context
to it and enriching its meaning.
• ODIP automatically translates the title and description of the
metadata to English.
Slide 27
29. DATASUPPORTOPEN
An ODIP Job
The ODIP job consists of three possible phases which need to be ran in
order and that are composed of several plug-ins :
1. Extraction
2. Transformation
3. Loading
Slide 29
Furthermore these jobs can be
scheduled to be launched
periodically, in succession or
manually.
31. DATASUPPORTOPEN
1. Extraction
• The extraction phase entails
retrieving (extracting) raw data
from a given source Open Data
portal using the appropriate
plug-in, depending on the
technology of the source.
• Available extractors:
CKAN Extractor
RDF extractor
SPARQL Extractor
Virtuoso Extractor
CSV Extractor
Slide 31
32. DATASUPPORTOPEN
2. Transformation (1/3)
• The goal of the transformation phase
is to harmonise, cleanse and prepare
for storing on ODIP metadata
harvested from Open Data portals.
• Available transformers:
ODS Value Mapper.
SPARQL Update Query
Transformer.
ODS Cleaner.
ODS DCAT Application Profile
Harmoniser.
ODS Modification Detector.
ODS Validator.
Web Translations.
Slide 32
33. DATASUPPORTOPEN
Loading
• In the loading phase, the
harvested and harmonised
metadata is stored on Virtuoso’s
RDF repository using the
Virtuoso Loader.
Slide 33
34. DATASUPPORTOPEN
Example
Harvesting a CKAN-based Open Data portal
1. Create a new job on ODIP
2. Extraction phase
- Add and Configure a CKAN Extractor to harvest data from a CKAN API.
3. Transformation phase
- Add ODS Value mapper
- Add a SPARQL Update Query Transformer with the pertinent queries
- Add ODS Cleaner
- Add and configure DCAT Application Profile Harmoniser
- Add Modification detector
- Add ODS Validator
- Add Web Translations
4. Loading phase
- Load the extracted data in a Virtuoso RDF Store via the Virtuoso Loader
5. Scheduling the job on ODIP
Slide 34
35. DATASUPPORTOPEN
Example – 1. Create Job : Creating a job on ODIP
• To create a new job, click on
“New Job”.
• At the bottom part of the
screen you can configure the
actual tasks within each of
the three phases by selecting
a tab.
• For each phase you can add
and configure modules
accordingly.
Slide 35
Provide a name
for the Job.
Present the job
with a short
description.
Press the “Add” button
to determine the plug-
ins to deploy.
36. DATASUPPORTOPEN
Example – 2.Extraction : Adding and Configuring
a CKAN Extractor to harvest data from a CKAN
API
After adding the CKAN extractor plugin you will be prompted to fill out
the following form:
Slide 36
The Web location of the
CKAN portal you wish to
harvest.
The portal should support API
version 3 and the API must be
enabled.
Publisher, license, title and
description: Used in the
stored catalog for the
dct:publisher, dct:license,
dct:title and dct:description
properties.
Predicate prefix: JSON attributes are
converted to predicates by appending
them to the predicate prefix.
The CKAN API response is in JSON, we
then convert this into RDF.
Subject prefix: The prefix used to
create a URI for each the metadata
of harvested dataset.
The subject is created as
<subjectprefix>/dataset/<datasetid>
Ignored keys: A comma seperated
list of JSON attributes that should
not be converted to RDF triples.
37. DATASUPPORTOPEN
Example – 3. Transformation : Adding and
configuring plug-ins to harmonise data(1/3)
• Start by adding the ODS DCAT Application Profile
Harmonizer.
This plugin will create the harmonized catalog data and a basic
skeleton for each dataset it identifies.
• Use the Modification Detector to compare provenance data
generated by the CKAN extractor between the current and previous
version of the raw data to set the dct:modified field of the catalog
records.
No configuration is required.
Slide 37
Provide a name to identify the catalog
38. DATASUPPORTOPEN
Example – 3. Transformation : Adding and
configuring plug-ins to harmonise data (2/3)
• Mapping the description of dataset to dct:description as required by
the DCAT-AP.
• Use the ODS Cleaner Plugin to remove raw data loaded into the
working set before storing it into a harmonized graph.
No configuration is required.
Slide 38
Use the SPARQL Update Query
Transformer to map existing
properties and values to the ones of
recommended by the DCAT-AP.
39. DATASUPPORTOPEN
Example – 3. Transformation : Adding and
configuring plug-ins to harmonise data (3/3)
Result
The final result of your harmonisation pipeline should look similar to
the following :
Configure the Virtuoso Loader to load the harmonized data into
Virtuoso.
Slide 39
40. DATASUPPORTOPEN
Example – 4. Loading: Load the extracted data in
a Virtuoso RDF Store via the Virtuoso Loader
The Virtuoso Loader will store the generated triples in the Virtuoso
RDF store. The triples will be inserted into a graph of your choice.
The Virtuoso Loader needs host, port and user credentials to connect to
your Virtuoso server.
Slide 40
41. DATASUPPORTOPEN
5. Scheduling a job on ODIP
A job can be scheduled to run at a set interval or chained after another
job:
• Interval Scheduling:
<sec> <min> <hour> <day-of-month> <month> <day-of-week>
Example:
0 0 4 * * * - each day at 4 am
0 0 0 * * 1 - each Monday at midnight
0 30 * * * - every half past the hour
• Chained scheduling: Select a job after which this job should be
executed.
Slide 41
42. DATASUPPORTOPEN
ODIP Reporting tool
Slide 42
Whenever a “job” is ran, a report is created and can be reviewed as can
be seen in the following screenshot:
Informs user whether or not a
plug-in functioned correctly or
not.
Select the appropriate job
43. DATASUPPORTOPEN
Discover datasets
through ODIP
The Open Data Interoperability Platform (ODIP) enables
you to share metadata of datasets described using the
DCAT-AP, thus improving the discoverability and
visibility of your datasets, eventually leading to wider
reuse.
Slide 43
46. DATASUPPORTOPEN
More about ODIP
• ODIP is based on the LOD Management Suite,
originally created by the Semantic Web
Company in the context of LOD2 FP7 project.
• The LOD Manager Suite was further extended by
TenForce in the context of Open Data Support
for the deployment of ODIP.
• It will be made available on GitHub
under GPLv2.
Slide 46
47. DATASUPPORTOPEN
Conclusions
• Good quality description metadata can improve the discoverability of
open datasets.
• DCAT-AP can be used for homogenising metadata of datasets hosted
on different Open Data portals and allows for querying them using a
uniform vocabulary.
• ODIP can support harvesting, harmonising according to the DCAT-
AP and publishing as linked data metadata of datasets published on
different Open Data portals.
• ODIP, through its public SPARQL endpoint, provides a single point
of access to datasets from all over Europe.
• Easier access to datasets means higher reuse of datasets.
Slide 47
48. DATASUPPORTOPEN
Group questions
Slide 48
How many Open Government Data portals do you know in
your country?
In your country, are you aware of any applications or services
that were built upon Open Government Data?
How would you compare the visibility of Open Government
Data portals with that of traditional data providers such as
national statistics offices?
Have you heard about the Open Government Data initiatives
of the European Commission?
http://www.visualpharm.com
http://www.visualpharm.com
http://www.visualpharm.com
http://www.visualpharm.com
Take also the online test here!
50. DATASUPPORTOPEN
References
Slide 4, 6, 9, 10, 11 & 12:
• Open Data Support: How can we help you?. Open Data Support.
http://www.slideshare.net/OpenDataSupport/open-data-support-service-
description
Slide 12:
• Data Catalogue Vocabulary. http://www.w3.org/TR/vocab-dcat/
Slide 13-21:
• DCAT Application Profile for data portals in Europe Community. ISA Programme.
https://joinup.ec.europa.eu/asset/dcat_application_profile/description
https://joinup.ec.europa.eu/asset/dcat_application_profile/asset_release/all
Slide 23-35:
• LODMS User Manual for Open Data Support. Open Data Support
Slide 29:
• Figure from http://www.semantic-web.at/linked-open-data-management-suite-
lodms
Slide 50
51. DATASUPPORTOPEN
Related projects and initiatives
DCAT Application Profile for Data Portals in Europe,
https://joinup.ec.europa.eu/asset/dcat_application_profile/description
Publicdata.eu, http://www.w3.org/2011/gld/wiki/Main_Page
LOD2 FP7 Project, http://lod2.eu/
The Semantic Web Company, http://www.semantic-web.at/
Linked Open Data Management Suite, http://www.semantic-
web.at/linked-open-data-management-suite-lodms
OpenLink Virtuoso, http://virtuoso.openlinksw.com/
Data Catalog Interoperability Protocol, http://spec.datacatalogs.org/
Slide 51
52. DATASUPPORTOPEN
Be part of our team...
Find us on
Contact us
Join us on
Follow us
Open Data Support
http://www.slideshare.net/OpenDataSupport
http://www.opendatasupport.euOpen Data Support
http://goo.gl/y9ZZI
@OpenDataSupport contact@opendatasupport.eu
Slide 52