Glenealy Tower

17F, No.1 Glenealy Central, Hong Kong, China

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Semantic Web
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Semantic Wiki

Explore Our Courses

Introduction to Graph Computing

 28 Hours
In this instructor-led, live training in Hong Kong, 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.
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Semantic Web Overview

 7 Hours
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.
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Apache Jena: Creating a Semantic Web Application

 21 Hours
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.
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Blazegraph: Creating a Graph Database Application

 21 Hours
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.
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SPARQL

 14 Hours
This instructor-led, live training in Hong Kong (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.
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Introduction to Semantic MediaWiki

 7 Hours
MediaWiki is a free and open-source wiki software. This one-day course provides participants with an introduction to Semantic MediaWiki.
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Testimonials (2)

Broad coverage and deep knowledge about Semantic Web

XINJIAN GUO - Yale University
Course - Semantic Web Overview

Very nice training

Maira Frisch - Novartis Pharma AG
Course - SPARQL

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