Introduction to Semantic MediaWiki Training Course

This course has been created for people who are going to use MediaWiki software as editors, users and website administrators.

This course explains how to:

• Find the information you are looking for on an existing site
• Create, edit and remove pages
• Design a structure of the page
• Analyse the best solution for the structure of the entire project
• Manage users
• Create and manage a community  

Some projects using MediaWiki:

• Wikipedia - Free encyclopedia

• Wiktionary - Dictionary and thesaurus

• Wikinews - Free content news source

• Wikiversity - Free learning tools

• Commons - Shared media repository

• Wikibooks - Free textbooks and manuals

• Wikiquote - Collection of quotations

• Wikisource - Free source documents

• Wikispecies - Directory of species

This course has been created for anyone who would like to learn how to create, edit and organize content using MediaWiki software.

FitNesse is a test framework written in Java that allows testers, developers and customers to collaboratively create test cases on a wiki. It enables an agile style of acceptance testing where testers collaborate with developers to develop a testing suite. FitNesse supports applications written in Java, .Net, Ruby, Python, C, and PHP using FitNesse.

In this instructor-led, live training, participants will learn how to use a Wiki based collaborative approach for creating and maintaining test cases, then executing those tests against a demo application.

By the end of this training, participants will be able to:

• Take examples of desired software behavior and turn them into automated tests that are viewable and editable through a Wiki
• Write and execute tests via the browser, command line (using RESTful syntax) or a JUnit test
• Create test fixtures that allow non-technical people to write tests by modifying a Wiki page
• Use FitNesse as a knowledge base and repository for story and theme requirements

Audience

• Test engineers
• Developers

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

This instructor-led, live training in Taiwan (online or onsite) is aimed at end users who wish to learn how to use XWiki to better collaborate with team members within their organization.

By the end of this training, participants will be able:

• Create, manage, and customize team pages and blogs.
• Customize XWiki for the right look and feel.
• Track changes to content using version control features.
• Set up a blog, manage comments, and customize the display.

This instructor-led, live training in Taiwan (online or onsite) is aimed at system administrators who wish to deploy and manage an XWiki server.

By the end of this training, participants will be able:

• Install a configure an XWiki server on premise.
• Create and manage users.
• Secure and optimize an XWiki server.
• Upgrade, back up and restore an XWiki server.
• Extend and customize the functionality and look and feel of XWiki.

In this instructor-led, live training in Taiwan, participants will learn about the technology offerings and implementation approaches for processing graph data. The aim is to identify real-world objects, their characteristics and relationships, then model these relationships and process them as data using a Graph Computing (also known as Graph Analytics) approach. We start with a broad overview and narrow in on specific tools as we step through a series of case studies, hands-on exercises and live deployments.

By the end of this training, participants will be able to:

• Understand how graph data is persisted and traversed.
• Select the best framework for a given task (from graph databases to batch processing frameworks.)
• Implement Hadoop, Spark, GraphX and Pregel to carry out graph computing across many machines in parallel.
• View real-world big data problems in terms of graphs, processes and traversals.

The Semantic Web is a collaborative movement led by the World Wide Web Consortium (W3C) that promotes common formats for data on the World Wide Web. The Semantic Web provides a common framework that allows data to be shared and reused across application, enterprise, and community boundaries.

Apache Jena is an open source Java framework for building Semantic Web and Linked Data applications.

In this instructor-led, live training, participants will learn how to use Apache Jena to build and deploy a Semantic Web Application. 

By the end of this training, participants will be able to:

• Install and configure Apache Jena
• Convert and store data in RDF format
• Query RDF data using SPARQL
• Test and deploy a semantic web application

Audience

• Developers
• Data Engineers

Format of the Course

• Part lecture, part discussion, exercises and heavy hands-on practice

Note

• To request a customized training for this course, please contact us to arrange.

Blazegraph is an open source, Java-based RDF graph database for storing and representing data with complex relationships. It supports Blueprints and RDF/SPARQL 1.1.

In this instructor-led, live training, participants will learn how to use Blazegraph to capture complex data in graph format for retrieval from a number of sample applications. All exercises will be carried out hands-on in a live-lab environment.

By the end of this training, participants will be able to:

• Install and configure Blazegraph in standalone mode, clustered mode (optional) or embedded mode (optional)
• Create, test and deploy a sample application to query complex data in a Blazegraph data store
• Understand how to leverage GPU (graphics processing unit) to accelerate computations

Audience

• Developers

Format of the Course

• Part lecture, part discussion, exercises and heavy hands-on practice

Note

• To request a customized training for this course, please contact us to arrange.

This instructor-led, live training in Taiwan (online or onsite) is aimed at technical persons who wish to query RDF data stored in a Semantic Web database.

By the end of this training, participants will be able to:

• Understand the difference between semantic web data and relational data.
• Query public datasets based on Semantic Web standards.
• Model data for querying with SPARQL.
• Transition a website's data to semantic web linked data.
• Run SPARQL queries from within an existing application.