Semantic Web Overviewのトレーニングコース

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.

SPARQL is a query language for querying RDF (Resource Description Framework) data. It is similar to SQL for relational data in databases.

This instructor-led, live training (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.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

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

Many real world problems can be described in terms of graphs. For example, the Web graph, the social network graph, the train network graph and the language graph. These graphs tend to be extremely large; processing them requires a specialized set of tools and processes -- these tools and processes can be referred to as Graph Computing (also known as Graph Analytics).

In this instructor-led, live training, 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 and Distributed Graph Processing) 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.

Format of the course

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

MediaWiki is a free and open-source wiki software. This one-day course provides participants with an introduction to Semantic MediaWiki.