Data Modeling With Graph

Technologies in the Graph Space

Graph Database System (OLTP)

Technologies that provide online transactional processing (OLTP) capabilities for graph fall under this category. It is a system with Create, Read, Update and Delete (CRUD) methods that can be applied real-time on a graph data model. In contrast to index-intensive[1], set theoretic operations of relational databases, these technologies make use of index-free, local traversals which means joins are of constant time. They are optimized for high-speed graph traversals. Examples are: neo4j, OrientDB etc.

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Security RDF graph

Inference and Reasoning
RDF stores information in the form statements, given a set of statements one can derive additional statements. Inference & reasoning engine makes use of the class and property definitions, their relationships and additional rules to derive additional statements. Apart from deriving additional statements, inference engine also validates the data contained in the RDF graph for example the range and domain of a property.

Semantic Web – An Introduction

 

Abstract

World Wide Web in its current form has lot of information, which can be inferred and used only by the humans. Semantic Web is the means to give structure to this meaning contained in the web so that machines can make use of this information and create even more information and knowledge without human intervention. It is the means to give human like intelligence, which in scientific terms is called as Artificial Intelligence, to every software application running in the web. Semantic Web in its entirety has the promise to empower software applications to draw conclusions and inferences – given pieces of information just like humans do. Just like web technologies/standards such as HTTP, HTML, URLs provided the foundation to share information in the way it is done today, Semantic Web technologies/standards provides the foundation for software applications to be more intelligent or smart.