In this talk I will show Visualbox, a "visualization server" based on LODSPeaKr that can make easy for non javascript experts to create simple but meaningful visualizations.
This document discusses concurrent stream processing. It describes representing queries as trees of processing nodes connected by pipes. The nodes transform tuples flowing through the pipes and include operators like joins, filters, and projections. The execution model is event-driven with I/O threads streaming data to compute threads that run node tasks. Memory is managed using buffered pipes that can store data on disk. The system supports parallelism across plans, nodes, and tasks using a fork/join thread pool and concurrency primitives like semaphores and transactions.
Stream Execution with Clojure and Fork/joinAlex Miller
One of the greatest benefits of Clojure is its ability to create simple, powerful abstractions that operate at the level of the problem while also operating at the level of the language.
This talk discusses a query processing engine built in Clojure that leverages this abstraction power to combine streams of data for efficient concurrent execution.
* Representing processing trees as s-expressions
* Streams as sequences of data
* Optimizing processing trees by manipulating s-expressions
* Direct execution of s-expression trees
* Compilation of s-expressions into nodes and pipes
* Concurrent processing nodes and pipes using a fork/join pool
This document summarizes Jeff Thompson's contributions of Spark API visual diagrams to the Spark community under an open source license. It also describes how Databricks further developed the diagrams and commissioned Adam Breindel for this work. The document briefly mentions Databricks' background and products.
The document discusses Scala concepts like implicit conversions and parameters as well as data processing frameworks like Hadoop, Cascading, and Scalding. It provides examples of using these concepts and frameworks to count the frequency of the first characters in words from a file using different approaches, from basic Scala to implementations with Cascading and Scalding.
MySQL 8.0 makes it possible to write queries that do more. MySQL can now traverse hierarchies, analyze data in new ways, and combine JSON and spatial data with traditional types — all in the same query.
In this presentation, we'll look at common table expressions (CTEs), window functions, geography support and JSON functionality, and how these can be used to do things no MySQL query has ever done before.
PostgreSQL: Advanced features in practiceJano Suchal
The document discusses several advanced features of PostgreSQL including:
1) Transactional DDL which allows DDL statements to be executed transactionally.
2) Cost-based query optimization and graphical EXPLAIN plans which help choose the most efficient query plan.
3) Features like partial indexes, function indexes, k-nearest search, views, and window functions which provide powerful ways to query and analyze data.
Creating Visualizations with Linked Open DataAlvaro Graves
This document discusses creating visualizations with linked open data using Visualbox. It begins with an overview of linked data and how to query it using SPARQL. Examples are provided of SPARQL queries to retrieve data about London and schools in London from public linked data sets. The document then introduces Visualbox as a tool for applying visualizations to the query results. It describes the components of Visualbox including using a SPARQL query to obtain data, selecting a visualization type and filters, testing the query, and viewing or embedding the final visualization.
Creation of visualizations based on Linked DataAlvaro Graves
A common task with any relatively large amount of data is to create visual representations that help users to make sense of such data and observe trends that otherwise would be hard for them to appreciate. The creation of these visual- izations usually requires some knowledge in a programming language, making it difficult for non-technical savvy users to create visualizations. In this paper we present Visualbox, a system that makes it easier for non-programmers to create web visualizations based on Linked Data. These visualiza- tions can be accessed by any modern web browser and can be easily embedded in web pages and blogs. We describe how people can create visualizations using Visualbox and we show examples of work done by real users. Finally we present a study that shows that Visualbox makes it easier for users to create Linked Data-based visualizations.
Presentación para ABRELATAM13 donde hablo de la necesidad de mejores estándares y tecnología para las iniciativas de Datos Abiertos y cómo la tecnología afecta la utilidad y transparencia de estas iniciativas.
El documento describe un proyecto para crear una plataforma que ayude a los padres a elegir colegios considerando factores como la calidad, distancia y costo. El objetivo también es ayudar a autoridades a entender la segregación geográfica en la educación chilena. Se desarrolló un prototipo que visualiza datos sobre colegios en Google Maps, incluyendo su puntaje SIMCE. El proyecto busca incorporar más datos y mejorar la interfaz para que sea útil a padres y colegios.
Towards a better understanding of Social MachinesAlvaro Graves
The document discusses how human cognition can involve the use of the web and how the web can be enhanced by human cognition. It outlines conditions for the web to be considered part of cognitive processes and describes different types of "social machines" where humans and machines collaborate. The document proposes creating a framework to study these social machines and how web technologies could improve trust, collaboration, and efficiency in social machines.
This document discusses the Linked Data publishing framework LODSPeaKr. It allows organizations to publish Linked Data in multiple formats and build applications on top of Linked Data. LODSPeaKr makes it simple to create services, APIs, and web applications using SPARQL, HTML, and the Haanga templating language. It provides default functionality like search and entity browsing. Applications can integrate data from multiple SPARQL endpoints and visualize results.
Integrating and publishing public safety data using semantic technologiesAlvaro Graves
The document outlines a project to integrate and publish public safety data from multiple sources using semantic technologies. It discusses the motivations for the project, including the need to make public safety information more accessible to citizens, policymakers, and law enforcement. It then describes the implementation of building a platform called PublicSafetyMap.org that aggregates public safety data semantically and visualizes it in maps and feeds that can be accessed online and on mobile devices. Challenges and next steps are also outlined, such as gaining trust, adding more data sources, and enabling annotation of events.
This document provides an adaptation of a webinar on querying linked data for Android devices using a modified triple store implementation. It outlines instructions for installing OntoQuad, deploying a preloaded MusicBrainz dataset, and includes sample SPARQL queries adapted from the original webinar to query the data within the limitations of mobile devices. The sample queries are demonstrated to retrieve album and track information for the band Queen from the loaded dataset.
Max De Marzi gave an introduction to graph databases using Neo4j as an example. He discussed trends in big, connected data and how NoSQL databases like key-value stores, column families, and document databases address these trends. However, graph databases are optimized for interconnected data by modeling it as nodes and relationships. Neo4j is a graph database that uses a property graph data model and allows querying and traversal through its Cypher query language and Gremlin scripting language. It is well-suited for domains involving highly connected data like social networks.
Talk about Exploring the Semantic Web, and particularly Linked Data, and the Rhizomer approach. Presented August 14th 2012 at the SRI AIC Seminar Series, Menlo Park, CA
This document discusses using graphs to represent relationships between places instead of people. It describes how researchers analyzed anonymized phone call records from Belgium to construct a "call graph" and identify separate clusters of Dutch and French speakers. The author analyzed routing logs from a location app to create a "place graph" showing connections between cities based on how often people drive between them. This place graph could be used to recommend neighborhoods in an unfamiliar city based on similarities to neighborhoods a traveler likes in cities they know.
Il seminario presenta il tema emergente del Web of Data, nell'ambito del Semantic Web. Vengono esaminate le criticità incontrate nell'accedere all'enorme quantità di informazione presente attualmente nel Web e i vantaggi di un approccio basato sulla creazione interattiva di interrogazioni.
Choices, modelling and Frankenstein Ontologiesbenosteen
This document discusses an ontology project at the University of Bristol. It addresses issues with representing research information, which changes frequently. The project uses a combination of ontologies like FOAF, Bio, and Dcterms to model "Things" like people and publications. Context about these Things, like time periods of validity, is represented using named graphs. The current implementation stores this information in a Fedora object store with RDF serialization. The project aims to gather relevant domain taxonomies and provide APIs for researchers to maintain them, taking a "Frankenstein" approach of combining relevant standards. It notes some design flaws of the CERIF interchange format compared to the linked data approach taken.
Session 3 "Challenges and Opportunities with Big Linked Data Visualization" t...Laura Po
HANDS-ON- SESSION - Challenges and Opportunities with Big Linked Data Visualization - tutorial @ISWC 2018
A book on the topic published by the author is
"Linked Data Visualization: Techniques, Tools and Big Data"
Laura Po, Nikos Bikakis, Federico Desimoni & George Papastefanatos
Synthesis Lectures on Data, Semantics and Knowledge
Morgan & Claypool, 2020
ISBN: 9781681737256 | 9781681737263 (ebook)
DOI: 10.2200/S00967ED1V01Y201911WBE019
Morgan & Claypool: https://www.morganclaypool.com/doi/abs/10.2200/S00967ED1V01Y201911WBE019
Homepage: http://www.linkeddatavisualization.com
Since the irruption in the market of the NoSQL concept, graph databases have been traditionally designed to be used with Java or C. With some honorable exceptions, there isn't an easy way to manage graph databases from Python. In this talk, I will introduce you some of the tools that you can use today in order to work with those new challenging databases, from our favorite languge, Python.
The document provides an overview of structured data presentation tools for digital humanities scholars. It discusses the difference between data presentation and analysis, and highlights some early pioneers of data visualization like William Playfair and Charles Minard. The document then examines challenges in using visualization for the humanities. It also profiles several structured data presentation tools, including TimeFlow, Google Fusion Tables, Many Eyes, and Omeka. Hands-on examples are provided using the Exhibit framework to create interactive visualizations like faceted browsing, searching, tables, timelines, and maps.
- The document discusses best practices for visualizing research findings using visualization tools and publicly available tools for creating and deploying humanities visualizations.
- It considers Edward Tufte's teachings on data visualization and provides a guided tutorial on using the Exhibit framework to create interactive web pages for presenting linked data visually.
- Examples are given of visualizing a dataset of Nobel Prize winners using Exhibit to create faceted browsing, searching, sorting, and different views including a table, timeline, and map.
Provenance and Reuse of Open Data (PILOD 2.0 June 2014)Rinke Hoekstra
The document summarizes a converts' rally held at Carnegie Hall in New York City on September 14, 1908 by the Evangelistic Committee. It discusses ingredients for publishing open data, including using URIs, versioning, repeatable transformations, choosing an appropriate level of detail, combining vocabularies, contextualizing information, and provenance. Provenance, or the origin and history of data, is a key issue in publishing open government data and builds trust for application developers and the public. Standards like the W3C PROV ontology can help represent provenance.
From text to entities: Information Extraction in the Era of Knowledge GraphsGraphRM
Incontro del 23/07/2018
In recent years there has been a proliferation of free and commercial "knowledge graphs" (KGs), which represent real-world entities together with their semantic relationships in a graphical form. Those are becoming a powerful asset both for tech giants, with Google Knowledge Graph, IBM’s Watson QA system and Facebook’s Open Graph, as well as for startups that are developing AI products, such as, semantic search, data analytics, recommender systems. While KGs provide a structured access to a large amount of knowledge, a vast majority of the information available on the Web is still inaccessible because encoded only in the form of natural-language text. The talk will present an overview of public available KGs and the main techniques used to bridge unstructured text with them, enabling a wide variety of knowledge-based applications.
Speaker: Matteo Cannaviccio
Providing open data is of interest for its societal and commercial value, for transparency, and because more people can do fun things with data. There is a growing number of initiatives to provide open data, from, for example, the UK government and the World Bank. However, much of this data is provided in formats such as Excel files, or even PDF files. This raises the question of
- How best to provide access to data so it can be most easily reused?
- How to enable the discovery of relevant data within the multitude of available data sets?
- How to enable applications to integrate data from large numbers of formerly unknown data sources?
One way to address these issues to to use the design principles of linked data (http://www.w3.org/DesignIssues/LinkedData.html), which suggest best practices for how to publish and connect structured data on the Web. This presentation gives an overview of linked data technologies (such as RDF and SPARQL), examples of how they can be used, as well as some starting points for people who want to provide and use linked data.
The presentation was given on August 8, at the Hacknight event (http://hacknight.se/) of Forskningsavdelningen (http://forskningsavd.se/) (Swedish: “Research Department”) a hackerspace in Malmö.
Session 2 of the Using Linked Data in Learning Analytics tutorial at lak2013
http://linkedu.eu/event/lak2013-linkeddata-tutorial/
Presentación para ABRELATAM13 donde hablo de la necesidad de mejores estándares y tecnología para las iniciativas de Datos Abiertos y cómo la tecnología afecta la utilidad y transparencia de estas iniciativas.
El documento describe un proyecto para crear una plataforma que ayude a los padres a elegir colegios considerando factores como la calidad, distancia y costo. El objetivo también es ayudar a autoridades a entender la segregación geográfica en la educación chilena. Se desarrolló un prototipo que visualiza datos sobre colegios en Google Maps, incluyendo su puntaje SIMCE. El proyecto busca incorporar más datos y mejorar la interfaz para que sea útil a padres y colegios.
Towards a better understanding of Social MachinesAlvaro Graves
The document discusses how human cognition can involve the use of the web and how the web can be enhanced by human cognition. It outlines conditions for the web to be considered part of cognitive processes and describes different types of "social machines" where humans and machines collaborate. The document proposes creating a framework to study these social machines and how web technologies could improve trust, collaboration, and efficiency in social machines.
This document discusses the Linked Data publishing framework LODSPeaKr. It allows organizations to publish Linked Data in multiple formats and build applications on top of Linked Data. LODSPeaKr makes it simple to create services, APIs, and web applications using SPARQL, HTML, and the Haanga templating language. It provides default functionality like search and entity browsing. Applications can integrate data from multiple SPARQL endpoints and visualize results.
Integrating and publishing public safety data using semantic technologiesAlvaro Graves
The document outlines a project to integrate and publish public safety data from multiple sources using semantic technologies. It discusses the motivations for the project, including the need to make public safety information more accessible to citizens, policymakers, and law enforcement. It then describes the implementation of building a platform called PublicSafetyMap.org that aggregates public safety data semantically and visualizes it in maps and feeds that can be accessed online and on mobile devices. Challenges and next steps are also outlined, such as gaining trust, adding more data sources, and enabling annotation of events.
This document provides an adaptation of a webinar on querying linked data for Android devices using a modified triple store implementation. It outlines instructions for installing OntoQuad, deploying a preloaded MusicBrainz dataset, and includes sample SPARQL queries adapted from the original webinar to query the data within the limitations of mobile devices. The sample queries are demonstrated to retrieve album and track information for the band Queen from the loaded dataset.
Max De Marzi gave an introduction to graph databases using Neo4j as an example. He discussed trends in big, connected data and how NoSQL databases like key-value stores, column families, and document databases address these trends. However, graph databases are optimized for interconnected data by modeling it as nodes and relationships. Neo4j is a graph database that uses a property graph data model and allows querying and traversal through its Cypher query language and Gremlin scripting language. It is well-suited for domains involving highly connected data like social networks.
Talk about Exploring the Semantic Web, and particularly Linked Data, and the Rhizomer approach. Presented August 14th 2012 at the SRI AIC Seminar Series, Menlo Park, CA
This document discusses using graphs to represent relationships between places instead of people. It describes how researchers analyzed anonymized phone call records from Belgium to construct a "call graph" and identify separate clusters of Dutch and French speakers. The author analyzed routing logs from a location app to create a "place graph" showing connections between cities based on how often people drive between them. This place graph could be used to recommend neighborhoods in an unfamiliar city based on similarities to neighborhoods a traveler likes in cities they know.
Il seminario presenta il tema emergente del Web of Data, nell'ambito del Semantic Web. Vengono esaminate le criticità incontrate nell'accedere all'enorme quantità di informazione presente attualmente nel Web e i vantaggi di un approccio basato sulla creazione interattiva di interrogazioni.
Choices, modelling and Frankenstein Ontologiesbenosteen
This document discusses an ontology project at the University of Bristol. It addresses issues with representing research information, which changes frequently. The project uses a combination of ontologies like FOAF, Bio, and Dcterms to model "Things" like people and publications. Context about these Things, like time periods of validity, is represented using named graphs. The current implementation stores this information in a Fedora object store with RDF serialization. The project aims to gather relevant domain taxonomies and provide APIs for researchers to maintain them, taking a "Frankenstein" approach of combining relevant standards. It notes some design flaws of the CERIF interchange format compared to the linked data approach taken.
Session 3 "Challenges and Opportunities with Big Linked Data Visualization" t...Laura Po
HANDS-ON- SESSION - Challenges and Opportunities with Big Linked Data Visualization - tutorial @ISWC 2018
A book on the topic published by the author is
"Linked Data Visualization: Techniques, Tools and Big Data"
Laura Po, Nikos Bikakis, Federico Desimoni & George Papastefanatos
Synthesis Lectures on Data, Semantics and Knowledge
Morgan & Claypool, 2020
ISBN: 9781681737256 | 9781681737263 (ebook)
DOI: 10.2200/S00967ED1V01Y201911WBE019
Morgan & Claypool: https://www.morganclaypool.com/doi/abs/10.2200/S00967ED1V01Y201911WBE019
Homepage: http://www.linkeddatavisualization.com
Since the irruption in the market of the NoSQL concept, graph databases have been traditionally designed to be used with Java or C. With some honorable exceptions, there isn't an easy way to manage graph databases from Python. In this talk, I will introduce you some of the tools that you can use today in order to work with those new challenging databases, from our favorite languge, Python.
The document provides an overview of structured data presentation tools for digital humanities scholars. It discusses the difference between data presentation and analysis, and highlights some early pioneers of data visualization like William Playfair and Charles Minard. The document then examines challenges in using visualization for the humanities. It also profiles several structured data presentation tools, including TimeFlow, Google Fusion Tables, Many Eyes, and Omeka. Hands-on examples are provided using the Exhibit framework to create interactive visualizations like faceted browsing, searching, tables, timelines, and maps.
- The document discusses best practices for visualizing research findings using visualization tools and publicly available tools for creating and deploying humanities visualizations.
- It considers Edward Tufte's teachings on data visualization and provides a guided tutorial on using the Exhibit framework to create interactive web pages for presenting linked data visually.
- Examples are given of visualizing a dataset of Nobel Prize winners using Exhibit to create faceted browsing, searching, sorting, and different views including a table, timeline, and map.
Provenance and Reuse of Open Data (PILOD 2.0 June 2014)Rinke Hoekstra
The document summarizes a converts' rally held at Carnegie Hall in New York City on September 14, 1908 by the Evangelistic Committee. It discusses ingredients for publishing open data, including using URIs, versioning, repeatable transformations, choosing an appropriate level of detail, combining vocabularies, contextualizing information, and provenance. Provenance, or the origin and history of data, is a key issue in publishing open government data and builds trust for application developers and the public. Standards like the W3C PROV ontology can help represent provenance.
From text to entities: Information Extraction in the Era of Knowledge GraphsGraphRM
Incontro del 23/07/2018
In recent years there has been a proliferation of free and commercial "knowledge graphs" (KGs), which represent real-world entities together with their semantic relationships in a graphical form. Those are becoming a powerful asset both for tech giants, with Google Knowledge Graph, IBM’s Watson QA system and Facebook’s Open Graph, as well as for startups that are developing AI products, such as, semantic search, data analytics, recommender systems. While KGs provide a structured access to a large amount of knowledge, a vast majority of the information available on the Web is still inaccessible because encoded only in the form of natural-language text. The talk will present an overview of public available KGs and the main techniques used to bridge unstructured text with them, enabling a wide variety of knowledge-based applications.
Speaker: Matteo Cannaviccio
Providing open data is of interest for its societal and commercial value, for transparency, and because more people can do fun things with data. There is a growing number of initiatives to provide open data, from, for example, the UK government and the World Bank. However, much of this data is provided in formats such as Excel files, or even PDF files. This raises the question of
- How best to provide access to data so it can be most easily reused?
- How to enable the discovery of relevant data within the multitude of available data sets?
- How to enable applications to integrate data from large numbers of formerly unknown data sources?
One way to address these issues to to use the design principles of linked data (http://www.w3.org/DesignIssues/LinkedData.html), which suggest best practices for how to publish and connect structured data on the Web. This presentation gives an overview of linked data technologies (such as RDF and SPARQL), examples of how they can be used, as well as some starting points for people who want to provide and use linked data.
The presentation was given on August 8, at the Hacknight event (http://hacknight.se/) of Forskningsavdelningen (http://forskningsavd.se/) (Swedish: “Research Department”) a hackerspace in Malmö.
Session 2 of the Using Linked Data in Learning Analytics tutorial at lak2013
http://linkedu.eu/event/lak2013-linkeddata-tutorial/
This document provides an introduction to data visualization for analysis. It discusses exploring datasets that can include textual, numerical, and other data. The document outlines the data visualization process and mentions some common tools and methods used. It also discusses extending your toolset and provides an example exercise exploring a dataset and creating a visualization to gain insights. The objective is to appreciate the variety of techniques available to digital humanities scholars for data analysis and visualization.
NoSQL, Neo4J for Java Developers , OracleWeek-2012Eugene Hanikblum
This seminar covered using Neo4j, a graph database for Java developers. It began with an overview of big data and NoSQL databases, including key-value stores, column databases, document databases, and graph databases. It then discussed why graph databases are useful when data is highly interconnected. The remainder of the seminar focused on Neo4j, explaining what it is, how it compares to relational databases, and how to model and query data using Neo4j and tools like Cypher, Spring Data Neo4j, the Neo4j browser, and Neoclipse plugin. Code examples were also provided.
The document provides an introduction to Prof. Dr. Sören Auer and his background in knowledge graphs. It discusses his current role as a professor and director focusing on organizing research data using knowledge graphs. It also briefly outlines some of his past roles and major scientific contributions in the areas of technology platforms, funding acquisition, and strategic projects related to knowledge graphs.
Network Mapping & Data Storytelling for BeginnersRenaud Clément
5-hour Workshop about network mapping and data storytelling.
This includes examples about data, networks, visualization, etc.
Given on Jan 31st, 2013 during a lecture in the Master Information, Technology and Territories in the Institute of Geography and Social Sciences, Toulouse 2 University. France.
Many thanks to @graphcommons for the inspiration.
Neo4j Spatial provides spatial/GIS capabilities for Neo4j, allowing it to store and query geospatial data. It aims to make GIS more accessible and allow for complex spatial mapping and analytics by connecting location data to other domain data stored in the graph. Features include support for OpenStreetMap data, dynamic layers, and topological queries and persistence of spatial relationships directly in the graph.
The document discusses the challenges of making open data accessible and usable for non-technical citizens and small communities. While large amounts of data are available from governments, it is often inconsistent, boring, or unusable for most people. Better tools are needed to empower citizens to use open data, similar to how tools like blogs and wikis simplified publishing on the web. Visualizations could be an easy way for non-experts to consume and understand large datasets, but current tools are still difficult to use. The document calls for the development of better tools to serve the "long tail" of open data users.
Explotando la Web de Datos: Como crear aplicaciones usando Linked Open DataAlvaro Graves
Este documento presenta una introducción a Linked Data y discute los desafíos y oportunidades de crear aplicaciones basadas en Linked Data. Explica que publicar y consumir Linked Data debería ser sencillo y que se necesitan mejores herramientas que faciliten la integración y el uso de datos enlazados de manera abierta. También destaca la importancia de modelar los datos correctamente y respetar los protocolos y formatos establecidos.
Improving decision-making based on government data and visualizationsAlvaro Graves
The document discusses challenges in using open government data for decision making and proposes using visualizations to help more people consume and communicate data, but notes it can be difficult to create visualizations from distributed government data and reuse existing visualizations. It suggests using personas to understand different stakeholder types and exploring how to ease the processes of creating and reusing visualizations from open data to allow more informed decisions.
Creating web applications with LODSPeaKrAlvaro Graves
LODSPeaKr is a framework for creating web applications using Linked Open Data. It allows for rapid development of applications with features like content negotiation, workflow execution across multiple SPARQL endpoints, and templates to generate HTML, RDF, and JSON representations from SPARQL queries. Developers can build applications like open data portals, APIs, and mobile apps using the templating system and services provided by LODSPeaKr without having to handle different representations manually. Future work aims to improve crawling, add better data management controls, and documentation.
Publicando RDF y Linked Data con LODSPeaKrAlvaro Graves
Este documento describe cómo publicar datos como Linked Data utilizando LODSPeaKr. LODSPeaKr es una herramienta sencilla que permite publicar datos en formato RDF en menos de un minuto y hacerlos fácilmente explorables. El documento también presenta un ejemplo de cómo los datos sobre mercados de agricultores se pueden convertir a RDF y publicar con LODSPeaKr.
Este documento trata sobre los datos abiertos y la participación ciudadana. Explica que los gobiernos poseen grandes cantidades de datos que podrían ser de valor para la ciudadanía si fueran públicos y de libre acceso. Sin embargo, estos datos suelen estar encerrados en bases de datos internas. El documento argumenta que publicar los datos de manera abierta permitiría una mayor transparencia, colaboración ciudadana y creación de oportunidades.
Web semántica y linked data la web como bdAlvaro Graves
Este documento introduce los principios básicos de la Web Semántica y Linked Data. Explica conceptos como RDF, URIs, vocabularios y ontologías para describir recursos en la Web de una manera que las máquinas puedan procesar. También describe las buenas prácticas de Linked Data y ejemplos de su uso, como enlazar y compartir datos de forma abierta a través de la Web.
LODSPeaKr is a simple kit for publishing Linked Open Data that allows creating mobile webapps based on Linked Data. It shows existing classes and instances of a particular class available from multiple data sources. By defining workflows, it can obtain more information based on already retrieved data.
Publishing Linked Open Data in 15 minutesAlvaro Graves
In this presentation I will show why Linked Open Data is the best technique available to publish government data and how can you use LODSPeaKr, a simple kit for publishing Linked Data, to create from prototypes in minutes to Open Data Portals, APIs and mobile webapps.
TWC LOGD: A Portal for Linking Government DataAlvaro Graves
Experiencias de LOGD un portal sobre open government data. En él es posible encontrar datasets, demos, tutoriales, etc. El mayor colaborador del Linked Data cloud y un socio importante del gobierno de EEUU.
POMELo, a simple, web-based PML (Proof Markup Language) editor. The objective of POMELo is to allow users to create, edit, validate and export provenance information in the form of PML documents. This application was developed with provenance novices in mind, making it usable in various settings, from educational to scientific. Since this is a web-based application, users do not need to install or run any software aside from a normal web browser, which simplifies its adoption and makes it more attractive for inexperienced users.
Breaking it Down: Microservices Architecture for PHP Developerspmeth1
Transitioning from monolithic PHP applications to a microservices architecture can be a game-changer, unlocking greater scalability, flexibility, and resilience. This session will explore not only the technical steps but also the transformative impact on team dynamics. By decentralizing services, teams can work more autonomously, fostering faster development cycles and greater ownership. Drawing on over 20 years of PHP experience, I’ll cover essential elements of microservices—from decomposition and data management to deployment strategies. We’ll examine real-world examples, common pitfalls, and effective solutions to equip PHP developers with the tools and strategies needed to confidently transition to microservices.
Key Takeaways:
1. Understanding the core technical and team dynamics benefits of microservices architecture in PHP.
2. Techniques for decomposing a monolithic application into manageable services, leading to more focused team ownership and accountability.
3. Best practices for inter-service communication, data consistency, and monitoring to enable smoother team collaboration.
4. Insights on avoiding common microservices pitfalls, such as over-engineering and excessive interdependencies, to keep teams aligned and efficient.
Pushing the Limits: CloudStack at 25K HostsShapeBlue
Boris Stoyanov took a look at a load testing exercise conducted in the lab. Discovered how CloudStack performs with 25,000 hosts as we explore response times, performance challenges, and the code improvements needed to scale effectively
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The CloudStack European User Group 2025 took place on May 8th in Vienna, Austria. The event once again brought together open-source cloud professionals, contributors, developers, and users for a day of deep technical insights, knowledge sharing, and community connection.
Storage Setup for LINSTOR/DRBD/CloudStackShapeBlue
Deciding on a good storage layout is crucial for good performance and reliability on later operations of your LINSTOR/CloudStack installation. This session gave the attendees an overview on different storage setups (LVM-Thin, striping, ZFS) and explaining differences in failure domains and performance implications and how to use them in LINSTOR.
--
The CloudStack European User Group 2025 took place on May 8th in Vienna, Austria. The event once again brought together open-source cloud professionals, contributors, developers, and users for a day of deep technical insights, knowledge sharing, and community connection.
AI Unboxed - How to Approach AI for Maximum ReturnMerelda
Keynote for a client.
In this session, Merelda addressed common misconceptions about AI technologies, particularly the confusion between Predictive AI and Generative AI, and provided clarity on when to use each. Predictive AI analyzes historical data to forecast future outcomes, while Generative AI creates new content, from text to images, rapidly. Understanding the differences between these technologies is crucial for making informed, strategic decisions.
She introduced the three archetypes of AI adoption: Takers, Shapers, and Makers, inviting the audience to identify which role their organisation plays. Based on these archetypes, she presented industry-specific examples relevant to Schauenburg’s portfolio, showcasing how Predictive AI can drive operational efficiency (e.g., predicting equipment maintenance), while Generative AI enhances customer interactions (e.g., generating technical documents).
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2. MOTIVATION
Linked Data brings tons of multidimensional data that
is easy(*) to query
However, having to make use of all that data we still
need to being able to process it (as humans)
One good way to make sense of this data is by using
visualizations
(*) Once you learn SPARQL
3. EXAMPLE
versus
Images: Few, Stephen (2010): Data Visualization for Human Perception. In: Soegaard, Mads and Dam, Rikke Friis (eds.). "Encyclopedia of Human-Computer Interaction". Aarhus, Denmark:
The Interaction Design Foundation. Available online at http://www.interaction-design.org/encyclopedia/data_visualization_for_human_perception.html
4. VISUALBOX
Based on LODSPeaKr
Make use of new and already existing visualization
filters
Creating of visualization via new GUI
Principle: Good data representation leads to good/easy
data manipulation
Corolary: Effort should be focused on obtaining the
right data
6. MODELS (SPARQL QUERY)
Using the SELECT query form will return a table, similar
to SQL.
main.query
PREFIX foaf: <http://xmlns.com/foaf/0.1/>
SELECT ?person1 ?person2 WHERE{
?person1 foaf:knows ?person2 .
}
person1 person2
http://example.org/john http://example.org/paul
http://example.org/john http://example.org/ringo
http://example.org/george http://example.org/paul
7. VIEWS (TEMPLATES)
We can decide how to operate with the data.
html.template
<ul>
{{for row in models.main}}
<li>{{row.person1.value}} knows {{row.person2.value}}</li>
{{endfor}}
</ul>
output
http://example.org/john knows http://example.org/paul
http://example.org/john knows http://example.org/ringo
http://example.org/george knows http://example.org/paul
8. FILTERS
It is possible to apply filters to the data.
Template
{{for row in models.main}}
{{row.person1.value|upper}}
{{endfor}}
Output
HTTP://EXAMPLE.ORG/JOHN
HTTP://EXAMPLE.ORG/JOHN
HTTP://EXAMPLE.ORG/GEORGE
9. VISUALIZATION FILTERS
It is possible to apply visualization filters directly to all
the results. These filters will generate the necessary
code to create a visualization
Model (main.query)
PREFIX cat: <http://dbpedia.org/resource/Category:>
SELECT ?countryLabel (COUNT(?nobel) as ?total) WHERE {
?nobel dcterms:subject cat:Nobel_laureates_in_Physics;
a foaf:Person;
dbp:placeOfBirth ?country .
?country a schema:Country ;
rdfs:label ?countryLabel
FILTER(LANG(?countryLabel) = "en")
}GROUP BY ?country ?countryLabel
ORDER BY DESC(?total)
LIMIT 100
10. countryLabel total
United States 23
Germany 14
England 9
Japan 6
Austria-Hungary 5
France 5
Netherlands 4
German Empire 3
Soviet Union 3
United Kingdom 3
Italy 3
11. View (html.template)
<body>
<h2>Total of Nobel laureates in Physics by country</h2>
{{models.main|GoogleVizPieChart:"countryLabel,total"}}
</body>
15. MAPS
Represent data as latitude, longitude and label
SELECT DISTINCT ?city SAMPLE(?lat) AS ?latitude SAMPLE(?long) AS ?longitude ?area WHERE{
?city a sch:City ;
<http://dbpedia.org/ontology/country> <http://dbpedia.org/resource/United_States> ;
geo:lat ?lat ;
geo:long ?long;
dbp:areaTotalKm ?area .
}GROUP BY ?city ?area
ORDER BY DESC(?area)
LIMIT 10
21. GRAPHS
Question: How to express a graph in a table?
Answer: Table child → parent
Model (main.query)
SELECT ?person1 ?person2 WHERE{
?person1 foaf:knows ?person2 .
}
person1 person2
Elizabeth Engstrom Ray Bradbury
Neil Gaiman Robert A. Heinlein
Neil Gaiman Ray Bradbury
23. TREE STRUCTURES
Problem: How do we retrieve a tree as a table?
Solution (so far): Table child,parent with one row with no
parent (root)
child parent area
Averill Park, New York Rensselaer County, New York 8.02896e+06
Sand Lake, New York Rensselaer County, New York 9.36e+07
Schaghticoke (town), New York Rensselaer County, New York 1.3442e+08
Poestenkill (town), New York Rensselaer County, New York 8.44336e+07
Schaghticoke (village), New York Rensselaer County, New York 2.33099e+06
Rensselaer County, New York
27. child parent
Pablo Neruda
Dane Zajc Pablo Neruda
Gary Soto Pablo Neruda
James Tate (writer) Pablo Neruda
Richard Aitson Pablo Neruda
Erin Siegal Pablo Neruda
Jože Snoj Dane Zajc
Rudi Šeligo Dane Zajc
Veno Taufer Dane Zajc
Rigoberto González Gary Soto
Thomas Lux James Tate (writer)
html.template
{{models.main|D3Dendrogram:"child,parent"}}
29. NOW YOU CREATE YOUR OWN
VISUALIZATION
Use data that is of interest for you
Describe to tell a story or support a statement
Give me feedback on how does visualbox works for
you