The document discusses setting up an Open Archives Initiative Protocol for Metadata Harvesting (OAI-PMH) target. It covers how OAI works, harvesting metadata through a browser, and setting up an OAI-PMH target, which involves mapping metadata from a repository to the OAI-PMH service and serving the results.
Introduction to Pig & Pig Latin | Big Data Hadoop Spark Tutorial | CloudxLabCloudxLab
Pig is an engine for executing data flows in parallel on Hadoop. It uses a language called Pig Latin to analyze large datasets. Pig provides relational operators like FOREACH, GROUP, and FILTER to process data in parallel. A hands-on example demonstrates loading dividend data, grouping it by stock symbol, calculating the average dividend for each symbol, and storing the results.
Apache Drill 1.0 has been released after nearly three years of development involving 45 code contributors and countless other contributors. Drill provides a SQL interface for analyzing both structured and unstructured data across numerous data sources. It aims to execute queries fast by leveraging columnar encodings and scaling out queries rather than scaling up. Drill also aims to support iterative exploration and querying of data without requiring data preparation. Future plans for Drill include continued monthly releases, integration with other technologies like JDBC and Cassandra, and tools to deploy Drill on EMR and EC2.
Swiss Big Data User Group - Introduction to Apache DrillMapR Technologies
This document provides an introduction and overview of Apache Drill, an open source distributed SQL query engine designed for interactive analysis of large-scale datasets. It describes Drill's architecture as being inspired by Google's Dremel, with support for standard SQL queries, pluggable data sources, and schema flexibility. Drill distributes query execution across multiple nodes to maximize data locality and parallelism. Key features highlighted include full ANSI SQL support, support for nested data, optional schemas, and extensibility points.
Apache Drill is new Apache incubator project. It's goal is to provide a distributed system for interactive analysis of large-scale datasets. Inspired by Google's Dremel technology, it aims to process trillions of records in seconds. We will cover the goals of Apache Drill, its use cases and how it relates to Hadoop, MongoDB and other large-scale distributed systems. We'll also talk about details of the architecture, points of extensibility, data flow and our first query languages (DrQL and SQL).
This document discusses Apache Drill, an open source SQL query engine for analysis of large scale datasets across various data sources like Hadoop, HBase etc. It provides interactive queries on large datasets with low latency. The document explains Drill's architecture, data model, query processing, extensibility features and how it integrates with Hadoop ecosystem. It encourages readers to get involved with the Apache Drill community.
This is a talk that I gave on July 20, 2012 at the Southern California Python Interest Group meetup at Cross Campus, with food and drinks provided by Graph Effect.
Apache Drill (http://incubator.apache.org/drill/) is a distributed system for interactive analysis of large-scale datasets, inspired by Google’s Dremel technology. It is designed to scale to thousands of servers and able to process Petabytes of data in seconds. Since its inception in mid 2012, Apache Drill has gained widespread interest in the community, attracting hundreds of interested individuals and companies. In the talk we discuss how Apache Drill enables ad-hoc interactive query at scale, walking through typical use cases and delve into Drill's architecture, the data flow and query languages as well as data sources supported.
Introduction to Pig & Pig Latin | Big Data Hadoop Spark Tutorial | CloudxLabCloudxLab
Pig is an engine for executing data flows in parallel on Hadoop. It uses a language called Pig Latin to analyze large datasets. Pig provides relational operators like FOREACH, GROUP, and FILTER to process data in parallel. A hands-on example demonstrates loading dividend data, grouping it by stock symbol, calculating the average dividend for each symbol, and storing the results.
Apache Drill 1.0 has been released after nearly three years of development involving 45 code contributors and countless other contributors. Drill provides a SQL interface for analyzing both structured and unstructured data across numerous data sources. It aims to execute queries fast by leveraging columnar encodings and scaling out queries rather than scaling up. Drill also aims to support iterative exploration and querying of data without requiring data preparation. Future plans for Drill include continued monthly releases, integration with other technologies like JDBC and Cassandra, and tools to deploy Drill on EMR and EC2.
Swiss Big Data User Group - Introduction to Apache DrillMapR Technologies
This document provides an introduction and overview of Apache Drill, an open source distributed SQL query engine designed for interactive analysis of large-scale datasets. It describes Drill's architecture as being inspired by Google's Dremel, with support for standard SQL queries, pluggable data sources, and schema flexibility. Drill distributes query execution across multiple nodes to maximize data locality and parallelism. Key features highlighted include full ANSI SQL support, support for nested data, optional schemas, and extensibility points.
Apache Drill is new Apache incubator project. It's goal is to provide a distributed system for interactive analysis of large-scale datasets. Inspired by Google's Dremel technology, it aims to process trillions of records in seconds. We will cover the goals of Apache Drill, its use cases and how it relates to Hadoop, MongoDB and other large-scale distributed systems. We'll also talk about details of the architecture, points of extensibility, data flow and our first query languages (DrQL and SQL).
This document discusses Apache Drill, an open source SQL query engine for analysis of large scale datasets across various data sources like Hadoop, HBase etc. It provides interactive queries on large datasets with low latency. The document explains Drill's architecture, data model, query processing, extensibility features and how it integrates with Hadoop ecosystem. It encourages readers to get involved with the Apache Drill community.
This is a talk that I gave on July 20, 2012 at the Southern California Python Interest Group meetup at Cross Campus, with food and drinks provided by Graph Effect.
Apache Drill (http://incubator.apache.org/drill/) is a distributed system for interactive analysis of large-scale datasets, inspired by Google’s Dremel technology. It is designed to scale to thousands of servers and able to process Petabytes of data in seconds. Since its inception in mid 2012, Apache Drill has gained widespread interest in the community, attracting hundreds of interested individuals and companies. In the talk we discuss how Apache Drill enables ad-hoc interactive query at scale, walking through typical use cases and delve into Drill's architecture, the data flow and query languages as well as data sources supported.
This document discusses metadata harvesting and validation. It describes a validation service that checks metadata against an application profile using XML schema and Schematron rules. The validation service is modular and reusable. Metadata can be validated during harvesting or mapping. An online validation service demo is also presented. The harvesting component harvests metadata from external repositories using OAI-PMH, validates the metadata, and stores valid metadata in a repository while logging invalid records. Validation reports are generated after harvesting.
Системный подход в организации и проведении Assessment Center в компании. Под...Vitaliy Mazurenko
Предлагаем вашему вниманию презентацию выступления Светланы Бадаевой, бизнес-тренера, генерального директора Ассоциации Бизнес Мастерства (г. Москва) на II Международной практической конференции «Оценка персонала», 10-11 июля 2008 года.
Pablo Alejandre del Rio presents an introduction to the Android platform. The presentation covers characteristics of Android such as it being an open source, Linux-based operating system for mobile devices. It discusses Android's architecture including the Dalvik virtual machine and app development using Java and XML. The presentation concludes with a demo and Q&A section.
H.R. Institute Of Technology.
The document discusses the Android platform and provides details in four main sections:
1. An introduction to Android, including what it is, its origins from Google and the Open Handset Alliance, and its open source licensing.
2. The Android platform, covering hardware requirements, the operating system which is based on Linux, network connectivity support, security features, and future possibilities for growth.
3. Software development for Android, including requirements like Java and the Android SDK, the Eclipse IDE, and supported programming languages like Java.
4. An overall evaluation of Android's advantages like customization but also limitations currently like some Bluetooth and Firefox support as well as conclusions
Android is an open source software stack for mobile devices that includes an operating system, middleware and key applications. It allows developers to write managed code in Java for the Dalvik virtual machine. The Android application framework provides components like activities, services and content providers that can be reused. It also supports hardware like cameras, GPS and accelerometers.
This document provides information about ARIADNE, a federation of online learning repositories. It discusses ARIADNE's centers located in various countries, its new members, and its relationship to other projects like GLOBE and MACE. Diagrams show how ARIADNE harvests and indexes content using open standards and interfaces with other systems through its federated search engine. The document outlines technologies used for communication, collaboration, content delivery, and more that enable online and blended learning applications.
Ariadne harvester and validator - technical overviewBram Vandeputte
The document describes a metadata validation framework that includes a validateMetadata() method that validates metadata using different validation schemes. It supports adding new validation schemas by configuring components and schemas. Validation components extend an interface and are configured by name and properties. The framework can be integrated as a Java library or REST service. It has been implemented and configured in the ARIADNE metadata harvesting system to validate harvested metadata against different standards.
This document discusses the GLOBE architecture for federating learning object repositories. It describes:
- GLOBE's use of LOM metadata standard and OAI-PMH protocol for metadata harvesting between repositories.
- The hybrid federated query and harvesting approach used to allow distributed and centralized searching of content.
- Key components of the GLOBE architecture including repositories, registry, harvester, validation services, and the ARIADNE tools for implementing repositories.
- Analysis of LOM usage in GLOBE repositories, including which elements are used most and quality issues around metadata completeness and consistency.
This document summarizes Dr. Joris Klerkx's work on harvesting metadata from learning object repositories. It discusses key technologies like metadata standards, harvesting metadata using OAI-PMH, and federated search. It provides examples of learning object repositories and registry data models. It describes the ARIADNE harvesting infrastructure and tools for validation, transformation, and accessing learning objects. The overall workflow of harvesting metadata from multiple repositories and aggregating it is depicted.
LOAD 'tweets.txt' USING PigStorage() AS (id, text, iso_language);
FILTER tweets BY iso_language == 'en';
GROUP filtered_tweets BY iso_language;
DUMP grouped_tweets;
This Pig Latin program loads tweets data from a text file, filters the data to only include tweets with an iso_language of 'en', groups the filtered tweets by iso_language, and dumps the results.
Big data, just an introduction to Hadoop and Scripting LanguagesCorley S.r.l.
This document provides an introduction to Big Data and Apache Hadoop. It defines Big Data as large and complex datasets that are difficult to process using traditional database tools. It describes how Hadoop uses MapReduce and HDFS to provide scalable storage and parallel processing of Big Data. It provides examples of companies using Hadoop to analyze exabytes of data and common Hadoop use cases like log analysis. Finally, it summarizes some popular Hadoop ecosystem projects like Hive, Pig, and Zookeeper that provide SQL-like querying, data flows, and coordination.
ResourceSync: Web-based Resource SynchronizationSimeon Warner
ResourceSync is a framework for synchronizing web resources between systems. The core team is developing standards for baseline synchronization using inventories, incremental synchronization using changesets, and push notifications using XMPP. The framework is based on reusing and extending existing sitemap formats to describe resources and changes in a modular way. Experiments show it can scale to synchronize large datasets like DBpedia and arXiv. Feedback is being solicited throughout 2012 to finalize the specifications.
This document discusses publishing Linked Data from relational databases (RDBs). It covers specifying ontologies and URI design, modeling data using vocabularies like FOAF and BIBO, transforming RDB data to RDF using R2O and ODEMapster, linking the generated RDF data to external datasets, and publishing the data in a Virtuoso endpoint to enable discovery through search engines and metadata through VOiD and CKAN.
The document discusses the architecture of Yandex's search cluster. It describes how Yandex handles indexing over 1012 URLs and 1010 documents in real-time, processes 107 user queries per hour, and develops search using 400+ developers and experiments on full datasets. It then provides details on the hardware requirements, how resources are distributed for indexing, serving, and development, and how the content systems for batch and real-time indexing work.
The document discusses the architecture of Yandex's search cluster. It describes how Yandex has evolved its search architecture over 15 years from a single machine setup to a large distributed system with over 100,000 servers spread across many data centers. It highlights some of the key challenges in building a large-scale search system like network performance, load balancing, fault tolerance and latency. It also provides examples of how Yandex has addressed these challenges at different stages of its growth.
Gateways 2020 Tutorial - Automated Data Ingest and Search with GlobusGlobus
We describe the automated data ingest scenario, referencing current and past research teams and their challenges. We demonstrate a web application that uses Globus to perform automated data ingest and present a faceted search interface that can be used by science gateways to simplify data discovery. We also walk through the application's GitHub repository and highlight relevant components.
This deck presents the best practices of using Apache Hive with good performance. It covers getting data into Hive, using ORC file format, getting good layout into partitions and files based on query patterns, execution using Tez and YARN queues, memory configuration, and debugging common query performance issues. It also describes Hive Bucketing and reading Hive Explain query plans.
The document provides an overview of Apache ManifoldCF, an open source content management system. It describes ManifoldCF's capabilities, including crawling repositories to index their contents and push those contents to search servers. It details the key components of ManifoldCF like the Pull Agent Daemon, jobs, connectors, and monitoring UI. The document also outlines ManifoldCF's history and major releases from its incubation at Apache to becoming a top-level project.
The rising complexity and cost of managing legacy travel distribution systems are leading many travel companies today to adopt a REST (REpresentational State Transfer) architectural style because it provides standardized resources that enable precise interaction with other REST systems. The panelists will discuss the fundamental shift in application design required to begin thinking in terms of resources rather than objects and methods and how the OpenTravel 2.0 specification is being designed to provide a common XML resource model for the travel industry.
This presentation will give you Information about :
1. What is Hadoop,
2. History of Hadoop,
3. Building Blocks – Hadoop Eco-System,
4. Who is behind Hadoop?,
5. What Hadoop is good for and why it is Good?,
This document discusses metadata harvesting and validation. It describes a validation service that checks metadata against an application profile using XML schema and Schematron rules. The validation service is modular and reusable. Metadata can be validated during harvesting or mapping. An online validation service demo is also presented. The harvesting component harvests metadata from external repositories using OAI-PMH, validates the metadata, and stores valid metadata in a repository while logging invalid records. Validation reports are generated after harvesting.
Системный подход в организации и проведении Assessment Center в компании. Под...Vitaliy Mazurenko
Предлагаем вашему вниманию презентацию выступления Светланы Бадаевой, бизнес-тренера, генерального директора Ассоциации Бизнес Мастерства (г. Москва) на II Международной практической конференции «Оценка персонала», 10-11 июля 2008 года.
Pablo Alejandre del Rio presents an introduction to the Android platform. The presentation covers characteristics of Android such as it being an open source, Linux-based operating system for mobile devices. It discusses Android's architecture including the Dalvik virtual machine and app development using Java and XML. The presentation concludes with a demo and Q&A section.
H.R. Institute Of Technology.
The document discusses the Android platform and provides details in four main sections:
1. An introduction to Android, including what it is, its origins from Google and the Open Handset Alliance, and its open source licensing.
2. The Android platform, covering hardware requirements, the operating system which is based on Linux, network connectivity support, security features, and future possibilities for growth.
3. Software development for Android, including requirements like Java and the Android SDK, the Eclipse IDE, and supported programming languages like Java.
4. An overall evaluation of Android's advantages like customization but also limitations currently like some Bluetooth and Firefox support as well as conclusions
Android is an open source software stack for mobile devices that includes an operating system, middleware and key applications. It allows developers to write managed code in Java for the Dalvik virtual machine. The Android application framework provides components like activities, services and content providers that can be reused. It also supports hardware like cameras, GPS and accelerometers.
This document provides information about ARIADNE, a federation of online learning repositories. It discusses ARIADNE's centers located in various countries, its new members, and its relationship to other projects like GLOBE and MACE. Diagrams show how ARIADNE harvests and indexes content using open standards and interfaces with other systems through its federated search engine. The document outlines technologies used for communication, collaboration, content delivery, and more that enable online and blended learning applications.
Ariadne harvester and validator - technical overviewBram Vandeputte
The document describes a metadata validation framework that includes a validateMetadata() method that validates metadata using different validation schemes. It supports adding new validation schemas by configuring components and schemas. Validation components extend an interface and are configured by name and properties. The framework can be integrated as a Java library or REST service. It has been implemented and configured in the ARIADNE metadata harvesting system to validate harvested metadata against different standards.
This document discusses the GLOBE architecture for federating learning object repositories. It describes:
- GLOBE's use of LOM metadata standard and OAI-PMH protocol for metadata harvesting between repositories.
- The hybrid federated query and harvesting approach used to allow distributed and centralized searching of content.
- Key components of the GLOBE architecture including repositories, registry, harvester, validation services, and the ARIADNE tools for implementing repositories.
- Analysis of LOM usage in GLOBE repositories, including which elements are used most and quality issues around metadata completeness and consistency.
This document summarizes Dr. Joris Klerkx's work on harvesting metadata from learning object repositories. It discusses key technologies like metadata standards, harvesting metadata using OAI-PMH, and federated search. It provides examples of learning object repositories and registry data models. It describes the ARIADNE harvesting infrastructure and tools for validation, transformation, and accessing learning objects. The overall workflow of harvesting metadata from multiple repositories and aggregating it is depicted.
LOAD 'tweets.txt' USING PigStorage() AS (id, text, iso_language);
FILTER tweets BY iso_language == 'en';
GROUP filtered_tweets BY iso_language;
DUMP grouped_tweets;
This Pig Latin program loads tweets data from a text file, filters the data to only include tweets with an iso_language of 'en', groups the filtered tweets by iso_language, and dumps the results.
Big data, just an introduction to Hadoop and Scripting LanguagesCorley S.r.l.
This document provides an introduction to Big Data and Apache Hadoop. It defines Big Data as large and complex datasets that are difficult to process using traditional database tools. It describes how Hadoop uses MapReduce and HDFS to provide scalable storage and parallel processing of Big Data. It provides examples of companies using Hadoop to analyze exabytes of data and common Hadoop use cases like log analysis. Finally, it summarizes some popular Hadoop ecosystem projects like Hive, Pig, and Zookeeper that provide SQL-like querying, data flows, and coordination.
ResourceSync: Web-based Resource SynchronizationSimeon Warner
ResourceSync is a framework for synchronizing web resources between systems. The core team is developing standards for baseline synchronization using inventories, incremental synchronization using changesets, and push notifications using XMPP. The framework is based on reusing and extending existing sitemap formats to describe resources and changes in a modular way. Experiments show it can scale to synchronize large datasets like DBpedia and arXiv. Feedback is being solicited throughout 2012 to finalize the specifications.
This document discusses publishing Linked Data from relational databases (RDBs). It covers specifying ontologies and URI design, modeling data using vocabularies like FOAF and BIBO, transforming RDB data to RDF using R2O and ODEMapster, linking the generated RDF data to external datasets, and publishing the data in a Virtuoso endpoint to enable discovery through search engines and metadata through VOiD and CKAN.
The document discusses the architecture of Yandex's search cluster. It describes how Yandex handles indexing over 1012 URLs and 1010 documents in real-time, processes 107 user queries per hour, and develops search using 400+ developers and experiments on full datasets. It then provides details on the hardware requirements, how resources are distributed for indexing, serving, and development, and how the content systems for batch and real-time indexing work.
The document discusses the architecture of Yandex's search cluster. It describes how Yandex has evolved its search architecture over 15 years from a single machine setup to a large distributed system with over 100,000 servers spread across many data centers. It highlights some of the key challenges in building a large-scale search system like network performance, load balancing, fault tolerance and latency. It also provides examples of how Yandex has addressed these challenges at different stages of its growth.
Gateways 2020 Tutorial - Automated Data Ingest and Search with GlobusGlobus
We describe the automated data ingest scenario, referencing current and past research teams and their challenges. We demonstrate a web application that uses Globus to perform automated data ingest and present a faceted search interface that can be used by science gateways to simplify data discovery. We also walk through the application's GitHub repository and highlight relevant components.
This deck presents the best practices of using Apache Hive with good performance. It covers getting data into Hive, using ORC file format, getting good layout into partitions and files based on query patterns, execution using Tez and YARN queues, memory configuration, and debugging common query performance issues. It also describes Hive Bucketing and reading Hive Explain query plans.
The document provides an overview of Apache ManifoldCF, an open source content management system. It describes ManifoldCF's capabilities, including crawling repositories to index their contents and push those contents to search servers. It details the key components of ManifoldCF like the Pull Agent Daemon, jobs, connectors, and monitoring UI. The document also outlines ManifoldCF's history and major releases from its incubation at Apache to becoming a top-level project.
The rising complexity and cost of managing legacy travel distribution systems are leading many travel companies today to adopt a REST (REpresentational State Transfer) architectural style because it provides standardized resources that enable precise interaction with other REST systems. The panelists will discuss the fundamental shift in application design required to begin thinking in terms of resources rather than objects and methods and how the OpenTravel 2.0 specification is being designed to provide a common XML resource model for the travel industry.
This presentation will give you Information about :
1. What is Hadoop,
2. History of Hadoop,
3. Building Blocks – Hadoop Eco-System,
4. Who is behind Hadoop?,
5. What Hadoop is good for and why it is Good?,
The document provides an overview of Redis Modules, which allow Redis to be extended through dynamically loaded libraries written in C. Some key modules discussed include ReJSON for storing and querying JSON documents natively in Redis, RediSearch for full-text search capabilities, and ReBloom for implementing scalable Bloom filters. Redis Modules can be used to add new data types, commands, and capabilities to Redis in order to adapt it to specific use cases and data models. Performance benchmarks show modules like ReJSON providing significant performance advantages over alternatives that rely on Redis' core data structures and Lua scripting.
The document discusses the architecture of Yandex's search cluster. It describes how Yandex distributes resources across indexation nodes, search nodes, and development nodes. It also discusses challenges like ensuring high availability, distributing load efficiently, and enabling large-scale experiments across many servers.
The document provides an overview of NoSQL databases and discusses various types including document databases, column-family stores, and key-value pairs. It provides examples of MongoDB, CouchDB, Redis, HBase and their data models, query operations, and architectures.
At the Devoxx 2015 conference in Belgium, Guillaume Laforge, Product Ninja & Advocate at Restlet, presented about the never-ending REST API design debate, covering many topics like HTTP status codes, Hypermedia APIs, pagination/searching/filtering, and more.
This document discusses using REST APIs for the Internet of Things. It describes how REST maps well to IoT use cases by representing resources as web resources identified by URIs. The state of resources can change in response to sensor data and actuators can change state based on updates to web resources. The document outlines key REST principles like representational state transfer and being based on hypermedia and web linking. It also discusses standards like CoAP and how a hybrid system can use both REST and messaging protocols like MQTT.
This document discusses strategies for aggregating educational content including key technologies like metadata standards, harvesting protocols, federated search, and publishing interfaces. It describes technologies like IEEE LOM for metadata, OAI-PMH for harvesting metadata, SQI for federated search across repositories, and SPI for publishing content. Examples are given of implementing these standards and protocols in systems like the ARIADNE harvester and registry.
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive functioning. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms.
Android is an open source software stack for mobile devices that includes an operating system, middleware and key applications. It allows developers to write managed code in Java and offers tools for application development including an emulator, SDK and Eclipse plugin. The Android framework supports components like activities, services and content providers that communicate via intents. It uses the Dalvik VM, supports common media formats and hardware like the camera and GPS.
In this short talk I will give an overview on ideas for using new technologies such as multi touch, tabletops and more to assist researchers in various tasks. We think of it as a research suite, where we go from very small devices and displays, such as a research pod and research pad, to extremely large displays, such as research on a tabletop, wall projections or even a projection dome. Specifically in this talk I will give an example of a researchpod app and a tabletop app. More! is a mobile application that has been developed for getting more information of a researcher during a presentation at a conference. This information includes all the papers, various contact information and even the slides of the presentation currently going on. Science table, a tabletop vizualisation, is a first attempt of providing easy access to publication data in a research field by showing connections between papers and its authors in various ways.
This document summarizes and discusses several research projects focused on openness, sharing, and collaboration in scientific research using web 2.0 technologies. It describes projects that aim to make scientific literature and data more openly accessible and connect researchers. This includes tools for finding additional information about researchers and conference presentations, visually browsing and clustering related research papers, and a proposed open collaborative platform for sharing research data, services, and applications. The document discusses challenges and opportunities for further developing these Research 2.0 ideas.
This document discusses large multi-touch displays and their technical details. It covers various touch input technologies like touchscreens, touchpads, and Wacom tablets. It also discusses multi-touch technologies like FTIR, DI, LLP, and DSI. Examples of multi-touch displays are given, including The Box, The Tabletop, and MTMini. Common multi-touch gestures like tap, drag, rotate, scale, and zoom are also listed. The document provides information on software protocols, trackers, and libraries for processing multi-touch input.
Processing is an open-source programming language and integrated development environment (IDE) that is used for multimedia and graphical applications like animation, visual art, and interactive design. It supports multitouch input through the TUIO protocol, which allows for tracking objects on a multitouch surface. Examples are provided to demonstrate how to use TUIO for multitouch input in Processing code and in Java code that interfaces with Processing.
The document discusses metadata harvesting and validation. It describes a validation service that checks metadata against an application profile using XML schema and Schematron rules. An online validation demo is also presented. The harvesting infrastructure uses the OAI-PMH protocol and includes components for harvesting from multiple targets, validating metadata, and generating validation reports. An ARIADNE Harvester manages the harvesting process and interfaces with repositories, validation services, and other systems.
The document provides an overview of the IEEE Learning Object Metadata standard (LOM) in three sections. It introduces LOM as a conceptual schema for describing digital and non-digital learning resources to enable interoperability across systems. The general overview section describes the standard's structure, data types, and vocabularies. The detailed structure section lists and explains the 9 categories and associated metadata elements that make up the LOM schema.
Android is a software stack for mobile devices that includes an operating system, middleware and key applications. It allows developers to write managed code in Java for the Dalvik virtual machine. The Android SDK provides tools and APIs to develop applications that use features like its application framework, SQLite database, media support and hardware integration. Developers can create Android applications by defining activities, services and content providers and connecting them with intents in the AndroidManifest file.
This document discusses metadata harvesting in repository networks. It provides an overview of the Open Archives Initiative Protocol for Metadata Harvesting (OAI-PMH) and the ARIADNE validation service. It then describes the ARIADNE harvesting infrastructure and projects that use it like MACE, MELT, and GLOBE. These harvest metadata from multiple repositories and enrich it. The document concludes with information on implementing an OAI-PMH target to expose a repository's metadata.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
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The simplified electron and muon model, Oscillating Spacetime: The Foundation...RitikBhardwaj56
Discover the Simplified Electron and Muon Model: A New Wave-Based Approach to Understanding Particles delves into a groundbreaking theory that presents electrons and muons as rotating soliton waves within oscillating spacetime. Geared towards students, researchers, and science buffs, this book breaks down complex ideas into simple explanations. It covers topics such as electron waves, temporal dynamics, and the implications of this model on particle physics. With clear illustrations and easy-to-follow explanations, readers will gain a new outlook on the universe's fundamental nature.
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
Thinking of getting a dog? Be aware that breeds like Pit Bulls, Rottweilers, and German Shepherds can be loyal and dangerous. Proper training and socialization are crucial to preventing aggressive behaviors. Ensure safety by understanding their needs and always supervising interactions. Stay safe, and enjoy your furry friends!
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
How to Add Chatter in the odoo 17 ERP ModuleCeline George
In Odoo, the chatter is like a chat tool that helps you work together on records. You can leave notes and track things, making it easier to talk with your team and partners. Inside chatter, all communication history, activity, and changes will be displayed.
Main Java[All of the Base Concepts}.docxadhitya5119
This is part 1 of my Java Learning Journey. This Contains Custom methods, classes, constructors, packages, multithreading , try- catch block, finally block and more.
Assessment and Planning in Educational technology.pptxKavitha Krishnan
In an education system, it is understood that assessment is only for the students, but on the other hand, the Assessment of teachers is also an important aspect of the education system that ensures teachers are providing high-quality instruction to students. The assessment process can be used to provide feedback and support for professional development, to inform decisions about teacher retention or promotion, or to evaluate teacher effectiveness for accountability purposes.
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
This presentation was provided by Steph Pollock of The American Psychological Association’s Journals Program, and Damita Snow, of The American Society of Civil Engineers (ASCE), for the initial session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session One: 'Setting Expectations: a DEIA Primer,' was held June 6, 2024.
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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4. Overview
• How OAI works
• Harvesting metadata : the simple way
• Setting up an OAI-PMH target
• Binding your database to the OAI-PMH target
• Harvesting metadata - take 2
www.ariadne-eu.org www.cs.kuleuven.be/~hmdb
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5. Overview
• Harvesting metadata : the simple way
• SettingHow OAI works target
up an OAI-PMH
• Binding your database to the OAI-PMH target
• Harvesting metadata - take 2
www.ariadne-eu.org www.cs.kuleuven.be/~hmdb
2
6. How OAI works
• OAI “VERBS” Service Provider Metadata Provider
• Identify H HTTP Request
R
E
• ListMetadataFormats A
R (OAI Verb) P
• GetRecord V O
• ListIdentifiers E OAI OAI S
S I
• ListRecords T T
O
• ListSets E
HTTP Response
R
R (Valid XML)
Y
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7. GetRecord
• Purpose
• Returns the metadata for a single item in the form
of an OAI record
• Parameters
• identifier – unique id for item (R)
• metadataPrefix – metadata format for the record
(R)
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8. ListRecords
• Purpose
• Retrieves metadata records for multiple items
• Parameters
• from – start date (O)
- greater than or equal to
• until – end date (O)
- less than or equal to
• set – set to harvest from (O)
• resumptionToken – flow control mechanism (X)
• metadataPrefix – metadata format (R)
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9. ListRecords – from until
http://localhost:8080/oaicat/OAIHandler?verb=ListRecords&
from=1999-01-15&until=2005-12-31&metadataPrefix=oai_lom
UTCdatetime
Dates and times are uniformly encoded using ISO8601
and are expressed in UTC throughout the protocol.
When time is included, the special UTC designator
("Z") must be used. UTC is implied for dates although
no timezone designator is specified. For example,
1957-03-20T20:30:00Z is UTC 8:30:00 PM on March
20th 1957. UTCdatetime is used in both protocol
requests and protocol replies, in the way described in
the following sections.
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10. ListIdentifiers
• Purpose
• List headers for all items corresponding to the specified
parameters
• Parameters
• from – start date (O)
• until – end date (O)
• set – set to harvest from (O)
• metadataPrefix – metadata format to list identifiers for (R)
• resumptionToken – flow control mechanism (X)
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11. ListSets
• Purpose
• Provide a listing of sets in which records may be
organized (may be hierarchical, overlapping, or flat)
• Parameters
• None
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13. Overview
✓ How OAI works
• Harvesting metadata : the simple way
• Setting up an OAI-PMH target
• Binding your database to the OAI-PMH target
• Harvesting metadata - take 2
www.ariadne-eu.org www.cs.kuleuven.be/~hmdb
9
14. Overview
✓ How OAI works
• Harvesting up an OAI-PMH targetway
Setting metadata : the simple
• Binding your database to the OAI-PMH target
• Harvesting metadata - take 2
www.ariadne-eu.org www.cs.kuleuven.be/~hmdb
9
17. Overview
✓ How OAI works
✓ Harvesting metadata : the simple way
• Setting up an OAI-PMH target
• Binding your database to the OAI-PMH target
• Harvesting metadata - take 2
www.ariadne-eu.org www.cs.kuleuven.be/~hmdb
11
18. Overview
✓ How OAI works
✓ Harvesting metadata : the simple way
• Setting up an OAI-PMH target
• Binding your database to the OAI-PMH target
• Harvesting metadata - take 2
www.ariadne-eu.org www.cs.kuleuven.be/~hmdb
11
19. Setting up a target
www.ariadne-eu.org www.cs.kuleuven.be/~hmdb
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20. Setting up a target
Wiki page with detailed instructions :
http://ariadne.cs.kuleuven.be/lomi/index.php/Setting_Up_OAI-PMH
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21. OAI-PMH Target Software
2. Mapping Process
Repository metadata Repository LOM metadata
1. Get metadata - identifier - lom.general.identifier
out of database - title - lom.general.title
- url - lom.general.description
- project description - lom.technical.location
- ... - ...
3. Copy results in
OAI-PMH service
Repository OAI-PMH result
DYNAMO LOM 4. Serve results ARIADNE
DYNAMO LOM
metadata LOM Harvester
DYNAMO
metadata
Repository LOM
metadata
metadata
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22. Setting up a target
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23. Setting up a target
• Install Java, Ant & apache-tomcat
• Set environment vars
set JAVA_HOME=C:j2sdk1.5.0
set ANT_HOME=C:apache-ant-1.6.5
set PATH=%JAVA_HOME%bin;%ANT_HOME%bin;%PATH%
• Download oaicat source and build
cd <your build-dir>
type “ant”
• Deploy code
put “oaicat_5.0/dist/oaicat.war” under “apache-tomcat/webapps/”
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24. Setting up a target
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25. Setting up a target
• Testing the target
- http://localhost:8080/oaitarget/OAIHandler?verb=Identify
- http://localhost:8080/oaitarget/OAIHandler?verb=ListMetadataFormats
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26. Setting up a target
www.ariadne-eu.org www.cs.kuleuven.be/~hmdb
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27. Setting up a target
• Binding to example lucene index
• Download the index :
- http://ariadne.cs.kuleuven.be/MeltSqiOai/OAI/lucene.zip
• Extract
• open properties file with text editor :
- oaicat_5.0/WEB-INF/oaicat.properties
• Enter the full path of index under the
"LuceneLomCatalog.lucenePath" property
• reload the target
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28. Setting up a target
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29. Setting up a target
• Testing the target
- http://localhost:8080/oaitarget/OAIHandler?verb=ListRecords&metadataPrefix=oai_lom
- http://localhost:8080/oaitarget/OAIHandler?verb=ListRecords&resumptionToken=
- http://localhost:8080/oaitarget/OAIHandler?verb=GetRecord&identifier=
oai:oaicat.ariadne.org:CS_LKP_v_3.0_nr_1162 &metadataPrefix=oai_lom
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30. Setting up a target
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31. Setting up a target
• Validate the result metadata in the
validationService online :
• http://ariadne.cs.kuleuven.be/validationService
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33. Overview
✓ How OAI works
✓ Harvesting metadata : the simple way
✓ Setting up an OAI-PMH target
• Binding your database to the OAI-PMH target
• Harvesting metadata - take 2
www.ariadne-eu.org www.cs.kuleuven.be/~hmdb
19
34. Overview
✓ How OAI works
✓ Harvesting metadata : the simple way
✓ Setting up an OAI-PMH target
• Binding your database to the OAI-PMH target
• Harvesting metadata - take 2
www.ariadne-eu.org www.cs.kuleuven.be/~hmdb
19
35. Binding it to your DB
www.ariadne-eu.org www.cs.kuleuven.be/~hmdb
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36. Binding it to your DB
• Implement /adapt 3 classes :
• org.ariadne.oai.server.X.catalog.XCatalog.java
➡ Implements the OAI-PMH verbs
• org.ariadne.oai.server.X.catalog.XRecordFactory.java
➡ Creates OAI-PMH headers
• org.ariadne.oai.server.X.crosswalk.XCrosswalk.java
➡ Maps arbitrary object to LOM XML object
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37. Binding it to your DB
www.ariadne-eu.org www.cs.kuleuven.be/~hmdb
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38. Binding it to your DB
• XCatalog.java
• For each “verb” :
- get parameters
- query database to get matching metadata
- parse metadata into java objects (Vector, HashMap, ...)
- call RecordFactory and Crosswalk to create records
www.ariadne-eu.org www.cs.kuleuven.be/~hmdb
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39. Binding it to your DB
www.ariadne-eu.org www.cs.kuleuven.be/~hmdb
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40. Binding it to your DB
• XRecordFactory.java
• Two important methods :
- getLocalIdentifier(Object nativeItem);
- getDatestamp(Object nativeItem);
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41. Binding it to your DB
www.ariadne-eu.org www.cs.kuleuven.be/~hmdb
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42. Binding it to your DB
• XCrosswalk.java
• Map fields of your object to LOM XML object :
- createMetadata(Object nativeItem);
www.ariadne-eu.org www.cs.kuleuven.be/~hmdb
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44. Overview
✓ How OAI works
✓ Harvesting metadata : the simple way
✓ Setting up an OAI-PMH target
✓ Binding your database to the OAI-PMH target
• Harvesting metadata - take 2
www.ariadne-eu.org www.cs.kuleuven.be/~hmdb
24
45. Overview
✓ How OAI works
✓ Harvesting metadata : the simple way
✓ Setting up an OAI-PMH target
✓ Binding your database to the OAI-PMH target
• Harvesting metadata - take 2
www.ariadne-eu.org www.cs.kuleuven.be/~hmdb
24