Marko Knepper, University Library Frankfurt am Main: From Library Data to Linked Open Data - Presentation given at DM2E event 'Putting Linked Library Data to Work: the DM2E Showcase' (18 Nov 2014, ONB, Vienna)
A hands-on intro to using U1Db to store permanent app data and in the future sync it across Ubuntu devices. Watch the video at http://youtu.be/-Tuczd1IVR8
This document provides an overview of querying and aggregation on MongoDB. It discusses querying concepts like find(), indexes, and $where queries. It also covers aggregation methods like count, sum, distinct, and group to perform more complex aggregation by grouping documents. Examples are given for common queries and aggregations including counting documents, finding population sums by state, and finding largest/smallest cities by state.
This document summarizes a presentation about Apache CouchDB. Some key points:
- CouchDB is a scalable, distributed key-value database that uses peer-to-peer replication. It has an append-only file structure and is designed to handle crashes well.
- Data is stored in JSON documents with dynamic schemas. Views are built using JavaScript map-reduce functions.
- The API is RESTful HTTP and works natively with the web. Data can be queried and rendered directly in the browser using JavaScript.
- CouchDB embraces web technologies and can scale from smartphones to server clusters. It is open source and embraces an open philosophy.
Developers love MongoDB because its flexible document model enhances their productivity. But did you know that MongoDB supports rich queries and lets you accomplish some of the same things you currently do with SQL statements? And that MongoDB's powerful aggregation framework makes it possible to perform real-time analytics for dashboards and reports?
Attend this webinar for an introduction to the MongoDB aggregation framework and a walk through of what you can do with it. We'll also demo using it to analyze U.S. census data.
This document provides an overview of CouchDB, an open-source document-oriented NoSQL database that uses JSON for documents, JavaScript for querying and HTTP for an API. It discusses CouchDB's support for high availability, easy replication between clusters, flexible schemas using JSON documents, and its use of web technologies like HTTP, JavaScript and JSON. Views are used for querying and aggregating documents using JavaScript functions. The document also outlines CouchDB's support for clustering, load balancing, caching and multi-master replication.
The document discusses MongoDB's Aggregation Framework, which allows users to perform ad-hoc queries and reshape data in MongoDB. It describes the key components of the aggregation pipeline including $match, $project, $group, $sort operators. It provides examples of how to filter, reshape, and summarize document data using the aggregation framework. The document also covers usage and limitations of aggregation as well as how it can be used to enable more flexible data analysis and reporting compared to MapReduce.
Webinar: Exploring the Aggregation FrameworkMongoDB
Developers love MongoDB because its flexible document model enhances their productivity. But did you know that MongoDB supports rich queries and lets you accomplish some of the same things you currently do with SQL statements? And that MongoDB's powerful aggregation framework makes it possible to perform real-time analytics for dashboards and reports?
Watch this webinar for an introduction to the MongoDB aggregation framework and a walk through of what you can do with it. We'll also demo an analysis of U.S. census data.
A hands-on intro to using U1Db to store permanent app data and in the future sync it across Ubuntu devices. Watch the video at http://youtu.be/-Tuczd1IVR8
This document provides an overview of querying and aggregation on MongoDB. It discusses querying concepts like find(), indexes, and $where queries. It also covers aggregation methods like count, sum, distinct, and group to perform more complex aggregation by grouping documents. Examples are given for common queries and aggregations including counting documents, finding population sums by state, and finding largest/smallest cities by state.
This document summarizes a presentation about Apache CouchDB. Some key points:
- CouchDB is a scalable, distributed key-value database that uses peer-to-peer replication. It has an append-only file structure and is designed to handle crashes well.
- Data is stored in JSON documents with dynamic schemas. Views are built using JavaScript map-reduce functions.
- The API is RESTful HTTP and works natively with the web. Data can be queried and rendered directly in the browser using JavaScript.
- CouchDB embraces web technologies and can scale from smartphones to server clusters. It is open source and embraces an open philosophy.
Developers love MongoDB because its flexible document model enhances their productivity. But did you know that MongoDB supports rich queries and lets you accomplish some of the same things you currently do with SQL statements? And that MongoDB's powerful aggregation framework makes it possible to perform real-time analytics for dashboards and reports?
Attend this webinar for an introduction to the MongoDB aggregation framework and a walk through of what you can do with it. We'll also demo using it to analyze U.S. census data.
This document provides an overview of CouchDB, an open-source document-oriented NoSQL database that uses JSON for documents, JavaScript for querying and HTTP for an API. It discusses CouchDB's support for high availability, easy replication between clusters, flexible schemas using JSON documents, and its use of web technologies like HTTP, JavaScript and JSON. Views are used for querying and aggregating documents using JavaScript functions. The document also outlines CouchDB's support for clustering, load balancing, caching and multi-master replication.
The document discusses MongoDB's Aggregation Framework, which allows users to perform ad-hoc queries and reshape data in MongoDB. It describes the key components of the aggregation pipeline including $match, $project, $group, $sort operators. It provides examples of how to filter, reshape, and summarize document data using the aggregation framework. The document also covers usage and limitations of aggregation as well as how it can be used to enable more flexible data analysis and reporting compared to MapReduce.
Webinar: Exploring the Aggregation FrameworkMongoDB
Developers love MongoDB because its flexible document model enhances their productivity. But did you know that MongoDB supports rich queries and lets you accomplish some of the same things you currently do with SQL statements? And that MongoDB's powerful aggregation framework makes it possible to perform real-time analytics for dashboards and reports?
Watch this webinar for an introduction to the MongoDB aggregation framework and a walk through of what you can do with it. We'll also demo an analysis of U.S. census data.
- MongoDB is a non-relational, document-oriented database that scales horizontally and uses JSON-like documents with dynamic schemas.
- It supports complex queries, embedded documents and arrays, and aggregation and MapReduce for querying and transforming data.
- MongoDB is used by many large companies for operational databases and analytics due to its scalability, flexibility, and performance.
AsterixDB is an open source "Big Data Management System" (BDMS) that provides flexible data modeling, efficient query processing, and scalable analytics on large datasets. It uses a native storage layer built on LSM trees with indexing and transaction support. The Asterix Data Model (ADM) supports semistructured data types like records, lists, and bags. Queries are written in the Asterix Query Language (AQL) which supports features like spatial and temporal predicates. AsterixDB is being used for applications like social network analysis, education analytics, and more.
Search Technologies for Digital Librariescneudecker
This document discusses contemporary search technologies and how they could be applied in libraries. It begins by looking at the current state of search, including keyword, boolean, faceted, and advanced searches. It then covers new approaches like semantic search, linked data, and crowdsourcing. Several prototypes are described that demonstrate new search capabilities, like geospatial search and image annotation. The document advocates for common APIs to allow libraries to provide search without hosting all the data and functionality themselves.
This document provides an overview of MongoDB administration commands and CRUD operations. It discusses how to select databases, show collections, import/export data, and perform basic CRUD operations like insert, find, update, and remove in MongoDB. It also covers additional find methods like logical operators, array operations, and accessing embedded documents. Methods for updating include $set, $inc, $unset, and multi updates.
Semantic Tagging for old maps...and other things on the WebBernhard Haslhofer
This document discusses semantic tagging of historical maps and other digital objects on the web. It introduces Maphub, a platform for annotating and semantically tagging digitized historical maps. Maphub allows users to add comments, semantic tags linked to controlled vocabularies, and geo-references to maps. The presentation describes Maphub's features, how semantic tagging works in the system by linking visual tags to unique URIs, and plans to implement semantic tagging plugins for other annotation tools and support additional knowledge organization systems. The benefits of semantic tagging are collecting structured metadata and connecting maps and annotations to other web resources through semantic links.
Analytics with MongoDB Aggregation Framework and Hadoop ConnectorHenrik Ingo
This document provides an overview of analytics with MongoDB and Hadoop Connector. It discusses how to collect and explore data, use visualization and aggregation, and make predictions. It describes how MongoDB can be used for data collection, pre-aggregation, and real-time queries. The Aggregation Framework and MapReduce in MongoDB are explained. It also covers using the Hadoop Connector to process large amounts of MongoDB data in Hadoop and writing results back to MongoDB. Examples of analytics use cases like recommendations, A/B testing, and personalization are briefly outlined.
MongoDB is an open source document database, and the leading NoSQL database. MongoDB is a document oriented database that provides high performance, high availability, and easy scalability. It is Maintained and supported by 10gen.
Susanne Müller, EUROCORR project: Burckhardtsource - Presentation given at DM2E event 'Putting Linked Library Data to Work: the DM2E Showcase' (18 Nov 2014, ONB, Vienna)
Bernhard Haslhofer, AIT / Open Knowledge Austria and Lieke Ploeger, Open Knowledge: The value of open data and the OpenGLAM network - Presentation given at DM2E event 'Putting Linked Library Data to Work: the DM2E Showcase' (18 Nov 2014, ONB, Vienna)
1. The DM2E project aggregates metadata and content about digitized manuscripts from several European libraries and archives.
2. It develops an interoperability infrastructure using the Europeana Data Model and a DM2E extension to integrate heterogeneous metadata into a linked open data cloud.
3. The project also builds digital humanities applications like Pundit to showcase the usefulness of linked open data for research.
The document discusses a project called DM2E that is researching scholarly practices in the humanities and building digital humanities tools. It focuses on the Scholarly Domain Model (SDM) that DM2E is using to model the entities and relationships of the digital scholarship domain. The SDM identifies areas, primitives, activities, and operations of scholarly work. It also describes the Pundit suite of tools for annotating, linking, comparing, and visualizing scholarly sources that were developed based on the SDM.
Presentation of SPARQL Anything at the MEI Linked Data IG Meeting in July 2021. We try SPARQL Anything with MEI XML files and experiment with simple and difficult tasks.
Solutions for bi-directional Integration between Oracle RDMBS & Apache KafkaGuido Schmutz
A Kafka cluster stores streams of records (messages) in categories called topics. It is the architectural backbone for integrating streaming data with a Data Lake, Microservices and Stream Processing. Today’s enterprises have their core systems often implemented on top of relational databases, such as the Oracle RDBMS. Implementing a new solution supporting the digital strategy using Kafka and the ecosystem can not always be done completely separate from the traditional legacy solutions. Often streaming data has to be enriched with state data which is held in an RDBMS of a legacy application. It’s important to cache this data in the stream processing solution, so that It can be efficiently joined to the data stream. But how do we make sure that the cache is kept up-to-date, if the source data changes? We can either poll for changes from Kafka using Kafka Connect or let the RDBMS push the data changes to Kafka. But what about writing data back to the legacy application, i.e. an anomaly is detected inside the stream processing solution which should trigger an action inside the legacy application. Using Kafka Connect we can write to a database table or view, which could trigger the action. But this not always the best option. If you have an Oracle RDBMS, there are many other ways to integrate the database with Kafka, such as Advanced Queueing (message broker in the database), CDC through Golden Gate or Debezium, Oracle REST Database Service (ORDS) and more. In this session, we present various blueprints for integrating an Oracle RDBMS with Apache Kafka in both directions and discuss how these blueprints can be implemented using the products mentioned before.
Solutions for bi-directional integration between Oracle RDBMS and Apache Kafk...confluent
A Kafka cluster stores streams of records (messages) in categories called topics. It is the architectural backbone for integrating streaming data with a Data Lake, Microservices and Stream Processing. Today's enterprises have their core systems often implemented on top of relational databases, such as the Oracle RDBMS. Implementing a new solution supporting the digital strategy using Kafka and the ecosystem can not always be done completely separate from the traditional legacy solutions. Often streaming data has to be enriched with state data which is held in an RDBMS of a legacy application. It's important to cache this data in the stream processing solution, so that It can be efficiently joined to the data stream. But how do we make sure that the cache is kept up-to-date, if the source data changes? We can either poll for changes from Kafka using Kafka Connect or let the RDBMS push the data changes to Kafka. But what about writing data back to the legacy application, i.e. an anomaly is detected inside the stream processing solution which should trigger an action inside the legacy application. Using Kafka Connect we can write to a database table or view, which could trigger the action. But this not always the best option. If you have an Oracle RDBMS, there are many other ways to integrate the database with Kafka, such as Advanced Queueing (message broker in the database), CDC through Golden Gate or Debezium, Oracle REST Database Service (ORDS) and more. In this session, we present various blueprints for integrating an Oracle RDBMS with Apache Kafka in both directions and discuss how these blueprints can be implemented using the products mentioned before.
Solutions for bi-directional integration between Oracle RDBMS & Apache KafkaGuido Schmutz
Apache Kafka is a popular distributed streaming data platform. A Kafka cluster stores streams of records (messages) in categories called topics. It is the architectural backbone for integrating streaming data with a Data Lake, Microservices and Stream Processing. Data sources flowing into Kafka are often native data streams such as social media streams, telemetry data, financial transactions and many others. But these data stream only contain part of the information. A lot of data necessary in stream processing is stored in traditional systems backed by relational databases. To implement new and modern, real-time solutions, an up-to-date view of that information is needed. So how do we make sure that information can flow between the RDBMS and Kafka, so that changes are available in Kafka as soon as possible in near-real-time? This session will present different approaches for integrating relational databases with Kafka, such as Kafka Connect, Oracle GoldenGate and bridging Kafka with Oracle Advanced Queuing (AQ).
- MongoDB is a non-relational, document-oriented database that scales horizontally and uses JSON-like documents with dynamic schemas.
- It supports complex queries, embedded documents and arrays, and aggregation and MapReduce for querying and transforming data.
- MongoDB is used by many large companies for operational databases and analytics due to its scalability, flexibility, and performance.
AsterixDB is an open source "Big Data Management System" (BDMS) that provides flexible data modeling, efficient query processing, and scalable analytics on large datasets. It uses a native storage layer built on LSM trees with indexing and transaction support. The Asterix Data Model (ADM) supports semistructured data types like records, lists, and bags. Queries are written in the Asterix Query Language (AQL) which supports features like spatial and temporal predicates. AsterixDB is being used for applications like social network analysis, education analytics, and more.
Search Technologies for Digital Librariescneudecker
This document discusses contemporary search technologies and how they could be applied in libraries. It begins by looking at the current state of search, including keyword, boolean, faceted, and advanced searches. It then covers new approaches like semantic search, linked data, and crowdsourcing. Several prototypes are described that demonstrate new search capabilities, like geospatial search and image annotation. The document advocates for common APIs to allow libraries to provide search without hosting all the data and functionality themselves.
This document provides an overview of MongoDB administration commands and CRUD operations. It discusses how to select databases, show collections, import/export data, and perform basic CRUD operations like insert, find, update, and remove in MongoDB. It also covers additional find methods like logical operators, array operations, and accessing embedded documents. Methods for updating include $set, $inc, $unset, and multi updates.
Semantic Tagging for old maps...and other things on the WebBernhard Haslhofer
This document discusses semantic tagging of historical maps and other digital objects on the web. It introduces Maphub, a platform for annotating and semantically tagging digitized historical maps. Maphub allows users to add comments, semantic tags linked to controlled vocabularies, and geo-references to maps. The presentation describes Maphub's features, how semantic tagging works in the system by linking visual tags to unique URIs, and plans to implement semantic tagging plugins for other annotation tools and support additional knowledge organization systems. The benefits of semantic tagging are collecting structured metadata and connecting maps and annotations to other web resources through semantic links.
Analytics with MongoDB Aggregation Framework and Hadoop ConnectorHenrik Ingo
This document provides an overview of analytics with MongoDB and Hadoop Connector. It discusses how to collect and explore data, use visualization and aggregation, and make predictions. It describes how MongoDB can be used for data collection, pre-aggregation, and real-time queries. The Aggregation Framework and MapReduce in MongoDB are explained. It also covers using the Hadoop Connector to process large amounts of MongoDB data in Hadoop and writing results back to MongoDB. Examples of analytics use cases like recommendations, A/B testing, and personalization are briefly outlined.
MongoDB is an open source document database, and the leading NoSQL database. MongoDB is a document oriented database that provides high performance, high availability, and easy scalability. It is Maintained and supported by 10gen.
Susanne Müller, EUROCORR project: Burckhardtsource - Presentation given at DM2E event 'Putting Linked Library Data to Work: the DM2E Showcase' (18 Nov 2014, ONB, Vienna)
Bernhard Haslhofer, AIT / Open Knowledge Austria and Lieke Ploeger, Open Knowledge: The value of open data and the OpenGLAM network - Presentation given at DM2E event 'Putting Linked Library Data to Work: the DM2E Showcase' (18 Nov 2014, ONB, Vienna)
1. The DM2E project aggregates metadata and content about digitized manuscripts from several European libraries and archives.
2. It develops an interoperability infrastructure using the Europeana Data Model and a DM2E extension to integrate heterogeneous metadata into a linked open data cloud.
3. The project also builds digital humanities applications like Pundit to showcase the usefulness of linked open data for research.
The document discusses a project called DM2E that is researching scholarly practices in the humanities and building digital humanities tools. It focuses on the Scholarly Domain Model (SDM) that DM2E is using to model the entities and relationships of the digital scholarship domain. The SDM identifies areas, primitives, activities, and operations of scholarly work. It also describes the Pundit suite of tools for annotating, linking, comparing, and visualizing scholarly sources that were developed based on the SDM.
Presentation of SPARQL Anything at the MEI Linked Data IG Meeting in July 2021. We try SPARQL Anything with MEI XML files and experiment with simple and difficult tasks.
Solutions for bi-directional Integration between Oracle RDMBS & Apache KafkaGuido Schmutz
A Kafka cluster stores streams of records (messages) in categories called topics. It is the architectural backbone for integrating streaming data with a Data Lake, Microservices and Stream Processing. Today’s enterprises have their core systems often implemented on top of relational databases, such as the Oracle RDBMS. Implementing a new solution supporting the digital strategy using Kafka and the ecosystem can not always be done completely separate from the traditional legacy solutions. Often streaming data has to be enriched with state data which is held in an RDBMS of a legacy application. It’s important to cache this data in the stream processing solution, so that It can be efficiently joined to the data stream. But how do we make sure that the cache is kept up-to-date, if the source data changes? We can either poll for changes from Kafka using Kafka Connect or let the RDBMS push the data changes to Kafka. But what about writing data back to the legacy application, i.e. an anomaly is detected inside the stream processing solution which should trigger an action inside the legacy application. Using Kafka Connect we can write to a database table or view, which could trigger the action. But this not always the best option. If you have an Oracle RDBMS, there are many other ways to integrate the database with Kafka, such as Advanced Queueing (message broker in the database), CDC through Golden Gate or Debezium, Oracle REST Database Service (ORDS) and more. In this session, we present various blueprints for integrating an Oracle RDBMS with Apache Kafka in both directions and discuss how these blueprints can be implemented using the products mentioned before.
Solutions for bi-directional integration between Oracle RDBMS and Apache Kafk...confluent
A Kafka cluster stores streams of records (messages) in categories called topics. It is the architectural backbone for integrating streaming data with a Data Lake, Microservices and Stream Processing. Today's enterprises have their core systems often implemented on top of relational databases, such as the Oracle RDBMS. Implementing a new solution supporting the digital strategy using Kafka and the ecosystem can not always be done completely separate from the traditional legacy solutions. Often streaming data has to be enriched with state data which is held in an RDBMS of a legacy application. It's important to cache this data in the stream processing solution, so that It can be efficiently joined to the data stream. But how do we make sure that the cache is kept up-to-date, if the source data changes? We can either poll for changes from Kafka using Kafka Connect or let the RDBMS push the data changes to Kafka. But what about writing data back to the legacy application, i.e. an anomaly is detected inside the stream processing solution which should trigger an action inside the legacy application. Using Kafka Connect we can write to a database table or view, which could trigger the action. But this not always the best option. If you have an Oracle RDBMS, there are many other ways to integrate the database with Kafka, such as Advanced Queueing (message broker in the database), CDC through Golden Gate or Debezium, Oracle REST Database Service (ORDS) and more. In this session, we present various blueprints for integrating an Oracle RDBMS with Apache Kafka in both directions and discuss how these blueprints can be implemented using the products mentioned before.
Solutions for bi-directional integration between Oracle RDBMS & Apache KafkaGuido Schmutz
Apache Kafka is a popular distributed streaming data platform. A Kafka cluster stores streams of records (messages) in categories called topics. It is the architectural backbone for integrating streaming data with a Data Lake, Microservices and Stream Processing. Data sources flowing into Kafka are often native data streams such as social media streams, telemetry data, financial transactions and many others. But these data stream only contain part of the information. A lot of data necessary in stream processing is stored in traditional systems backed by relational databases. To implement new and modern, real-time solutions, an up-to-date view of that information is needed. So how do we make sure that information can flow between the RDBMS and Kafka, so that changes are available in Kafka as soon as possible in near-real-time? This session will present different approaches for integrating relational databases with Kafka, such as Kafka Connect, Oracle GoldenGate and bridging Kafka with Oracle Advanced Queuing (AQ).
Introducción a la web semántica - Linkatu - irekia 2012Alberto Labarga
This document provides an introduction to linked data journalism and the use of semantic web technologies like RDF, SPARQL, and linked open data. It explains key concepts such as using RDF triples to represent knowledge, querying linked data with SPARQL, and examples of data sources like DBpedia and data marketplaces.
The document discusses using generative AI to simplify workflows in museums. It describes using chatbots and language models to generate keywords, convert file formats, create websites, and translate between natural language and structured queries. The document also discusses generating synthetic text descriptions from metadata and using chatbots to have unstructured conversations about museum collections.
UKOUG 2011 - Drag, Drop and other Stuff. Using your Database as a File ServerMarco Gralike
The document discusses Oracle XML DB Repository and its features. It describes how the repository is based on XML standards and can store, consume, generate, and validate XML. It also supports resource manipulation using packages and views. Events and extensions are supported through XML configuration files and schemas. Use cases demonstrate how unstructured files can be stored and metadata extracted for additional processing and display.
Integrating Heterogeneous Data Sources in the Web of DataFranck Michel
These are the slides of a 40mn presentation I've made at the CNRS Software Development days (JDEV 2017), in Marseille (France), July 5th, 2017.
Here is the Webcast, in French: https://webcast.in2p3.fr/videos-integrer_des_sources_de_donnees_heterogenes_dans_le_web_de_donnees
Enterprise integration options with SmallworldPeter Batty
The document discusses various options for integrating databases and mapping technologies with Smallworld, including database integration, data integration, and map integration. It covers database types, importing data, database synchronization, web services, EAI, OGC/WMS standards, REST APIs, and tile services. The presentation examines challenges around connecting different data silos and typical integration solutions like enterprise application integration.
Enterprise integration options with SmallworldPeter Batty
The document discusses various options for integrating Smallworld with external data sources. It covers database integration using technologies like database syncing, web services, and EAI. It also discusses map integration standards like OGC/WMS and approaches like generating pre-rendered map tiles that can be overlaid from different sources. REST and tile-based approaches are highlighted as being simple, scalable ways to integrate data and maps across platforms.
The document discusses linked data and how it can be used to share information on the web in a structured format. It provides an overview of linked data and the Resource Description Framework (RDF), describes how URIs can be used to name things and link data on the web, and gives examples of publishing and querying linked data using RDF and SPARQL. Recent developments in using linked data by Facebook, Google, and other companies are also mentioned.
Hypothesis 1 proposes a single network and common schema. Hypothesis 2 proposes an object-oriented design approach. Hypothesis 3 states that the resource is the message. The document then discusses Catmandu, a Perl toolkit for working with complex data, and LibreCat, an example program for repository functions like search, citations, and more. It outlines the project plan and thanks collaborators.
In Pursuit of the Grand Unified Templatehannonhill
This document discusses using XSLT to create conditional page formatting and restructuring of content from a single template. It provides examples of how to determine layout rules, output alternative page elements, and expose additional metadata at the page level. Key points covered include checking for content, assigning layouts, applying layout rules to different page areas, and extracting metadata values as variables.
Midgard2 - Content Repository for mobile applicationsHenri Bergius
This document introduces Midgard2, an open source content repository for mobile applications. It provides objects instead of SQL for storing data and signals about changes. The data model is scriptable and it allows for synchronization and sharing of data. Midgard2 uses a schema-free approach and stores data as JSON which can be accessed via JavaScript map/reduce queries or pulled via HTTP. It also integrates with CouchDB for a polyglot persistence approach. The document demonstrates how to install and use Midgard2 with examples in PHP, Python and Vala. It also introduces Midgard MVC for building web applications on top of the content repository.
RDF and linked data standards allow for layering and linking of information on the web. There is a large and growing amount of RDF data available from sources like Wikipedia, Flickr, government data sets, and more. Standards like RDF, RDFS, OWL, SKOS, and SPARQL enable publishing, linking, querying and reusing this structured data on the web in a way that is machine-readable. Integrating RDF and linked data into systems like Drupal could provide benefits like improved searchability, cross-linking of content, and reuse of external taxonomies and metadata schemas.
Reasoning with Reasoning, Semantic technologies for research in the humanities and social sciences (STRiX) Göteborg, 24 November 2014 Kristin Dill, Austrian National Library (ONB) Gerold Tschumpel, Steffen Hennicke, Christian Morbidoni, Klaus Thoden, Alois Pichler
The document summarizes the tasks and results of Work Package 1 (WP1) of the DM2E project. Key points include:
- WP1 involved collecting metadata formats and requirements, testing interfaces for mapping and linking content, and setting up test scenarios for the prototype platform.
- Final content integration took longer than expected due to complex data modeling, issues mapping content, and Europeana's policy changes. Not all promised content was delivered.
- User testing found that interfaces were useful for basic tasks but complex work was done "under the hood". Guidelines were created to represent metadata and define annotatable content.
- While not all content goals were met, over 19 million pages were delivered, with
DM2E Community building (Lieke Ploeger – Open Knowledge) at Enabling humanities research in the Linked Open Web – DM2E final event (11 December 2014, Navacchio, Italy)
Open Humanities Awards DM2E track: finderapp WITTfind (Maximilian Hadersbeck – LMU University of Munich) at Enabling humanities research in the Linked Open Web – DM2E final event (11 December 2014, Navacchio, Italy)
Humanists and Linked Data (Steffen Hennicke – Humboldt Universität) at Enabling humanities research in the Linked Open Web – DM2E final event (11 December 2014, Navacchio, Italy)
Open Humanities Awards Open track: SEA CHANGE (Rainer Simon – AIT Austrian Institute of Technology) at Enabling humanities research in the Linked Open Web – DM2E final event (11 December 2014, Navacchio, Italy)
DM2E Linked Data for Digital Scholars (with talks by Christian Morbidoni – Università Politecnica delle Marche / Net7, Steffen Hennicke – Humboldt Universität and Alessio Piccioli – Net7)
DM2E Interoperability infrastructure (Kai Eckert – University of Mannheim) at Enabling humanities research in the Linked Open Web – DM2E final event (11 December 2014, Navacchio, Italy)
Open Humanities Awards Open track: Early Modern European Peace Treaties Online (Michael Piotrowski – IEG Leibniz Institute of European History) at Enabling humanities research in the Linked Open Web – DM2E final event (11 December 2014, Navacchio, Italy)
DM2E Content (Doron Goldfarb – ONB Austrian National Library) at Enabling humanities research in the Linked Open Web – DM2E final event (11 December 2014, Navacchio, Italy)
Europeana and the relevance of the DM2E results (Antoine Isaac – Europeana) at Enabling humanities research in the Linked Open Web – DM2E final event (11 December 2014, Navacchio, Italy)
Keynote : Beyond DM2E: towards sustainable digital services for humanities research communities in Europe? (Sally Chambers – DARIAH-EU, Göttingen Centre for Digital Humanities) at Enabling humanities research in the Linked Open Web – DM2E final event (11 December 2014, Navacchio, Italy)
Welcome and short introduction to DM2E (Violeta Trkulja – Humboldt University) - Enabling humanities research in the Linked Open Web – DM2E final event
The document discusses an evaluation of metadata usage and distribution in a linked data environment. It analyzes datasets from different institutions that mapped manuscript metadata to the Europeana Data Model (EDM) and a DM2E model. The evaluation aims to discover similarities and differences between datasets from different mapping institutions. It finds variations in usage of classes, properties, ontologies, and structural metrics like predicate-object-equality-ratio. The conclusion is that linked data quality assurance is important and people have a strong influence on metadata mapping.
The document summarizes the OpenGLAM CH Working Group in Switzerland, which aims to promote open data principles in cultural heritage institutions. It has around 50 subscribers and 20-25 active participants. The working group coordinates projects and task forces, including outreach to smaller institutions, an open cultural data hackathon, and plans to evaluate open data projects. It has been active since 2013 and is a loose network under the Open Knowledge Foundation that coordinates biannual meetings.
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
Building Production Ready Search Pipelines with Spark and MilvusZilliz
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology