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Thinking of Models as Graphs

The first step in any big data visualization and analysis process is to ingest your data. In the past, most developers thought of models as rows with attributes and references to other row identifiers. In keeping with that mentality, Perspectives pulled data from a relational database into its session-scoped model.

Relational social network data file.
Relational social network data are shown in rows of elements and attributes.

Modern graph visualization developers tend to conceptualize data differently. While many users still pull data from an RDBMS, an increasing number use graph-based data storage and think about their data in graph-like ways.

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Graph Database vs. Relational Database

Introduction

At the very beginning of most development endeavors lies an important question: What database to choose? There is such an abundance of database technologies at this moment, it’s no wonder many developers don’t have the time or energy to research new ones. If you are one of those developers and you aren’t very familiar with graph databases in general, you’ve come to the right place!

In this article, you will learn about the main differences between a graph database and a relational database, what kind of use-cases are best suited for each database type, and what are their strengths and weaknesses.

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Do Graph Databases Scale?

Graph Databases are a great solution for many modern use cases: Fraud Detection, Knowledge Graphs, Asset Management, Recommendation Engines, IoT, Permission Management … you name it. 

All such projects benefit from a database technology capable of analyzing highly connected data points and their relations fast – Graph databases are designed for these tasks.

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How Graph Analytics Can Transform Your Business

Introduction

Your business is operating in an ever more connected world where the understanding of complex relationships and interdependencies between different data points is crucial to many decision-making processes. This is the main reason why graph databases have gained a lot of interest in the past few years and have become that fastest-growing database category. They offer powerful data modeling and analysis capabilities your business can use to easily model real-world complex systems and answer challenging questions previously hard to address.

What Is a Graph Database?

You might not be aware of it, but many of the services you use on a daily basis are powered by a graph database. Such examples include Google’s search engine, Linkedin’s connection recommendations, UberEats food recommendations and Gmail’s autocomplete feature. Simply put, a graph database is a data management system specifically engineered and optimized to store and analyze complex networks of connected data where relationships are equally important to individual data points. As a result, they offer a highly efficient, flexible, and overall elegant way to discover connections and patterns within your data that are otherwise very hard to see.

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Graph Therapy: The Year of the Graph Newsletter, June/May 2020

Parts of the world are still in lockdown, while others are returning to some semblance of normalcy. Either way, while the last few months have given some things pause, they have boosted others. It seems like developments in the world of Graphs are among those that have been boosted. 

An abundance of educational material on all things graph has been prepared and delivered online, and is now freely accessible, with more on the way. 

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The Multi-Model Knowledge Graph

Introduction

Enterprise Knowledge Graphs (EKGs) have been on the rise and are incredibly valuable tools for harmonizing internal and external data relevant to an organization to improve operational efficiency for the enterprise and competitive advantage for the business units. On the other hand, EKGs can be difficult to develop and sustain, suffer from scalability issues, and can be difficult for business units to consume. This article describes some of these challenges and how a flexible data representation of a native multi-model database can address them (see Figure 1). 

Figure 1: The Multi-model knowledge graph blends multiple data representations in one system.

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Positive Impact of Graph Technology and Neural Networks on Cybersecurity

Take a look into the future of cybersecurity

Breaches on the Rise

The Equifax security breach was among the worst ever in terms of the number of people affected and the type of information breached. Information such as names, SSNs, birth dates and addresses are considered the Holy Grail of personal data that allows hackers to gain access to anyone’s personal, financial, and health records.

While frequent incidents of security breaches have brought enough anxiety in corporate America, it’s the complexity of managing cybersecurity and addressing unanswered questions that really have enterprises nervous.

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Accumulator 101

Motivation

GSQL is a Turing complete Graph Database query language. Compared to other graph query languages, the biggest advantage is its support of Accumulators — global or attachable to each vertex.

In addition to providing the classic pattern match syntax, which is easy to master, GSQL supports powerful run-time vertex attributes (a.k.a local accumulators) and global state variables (a.k.a global accumulators). I have seen users learning and adopting pattern match syntax within ten minutes. However, I also witnessed the uneasiness of learning and adopting accumulators for beginners.

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Applying Graph Analytics to Game of Thrones

In this post, we review how organizations are integrating graph transactions and analytic processing and then dive deeper into graph algorithms. We’ll provide examples of using graph algorithms on Game of Thrones data to illustrate how to get started. Note that portions of this content have been taken from our O’Reilly book, Graph Algorithms: Practical Examples in Apache Spark and Neo4j, which you can download for free.

Neo4j provides native graph storage, compute, and analytics in a unified platform. Our goal is to help organizations reveal how people, processes, locations, and systems are interrelated using a connections-first approach. The Neo4j Graph Platform powers applications tackling artificial intelligence, fraud detection, real-time recommendations, and master data.

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Graph Explosion and Consolidation. The Year of the Graph Newsletter: June 2019

With the knowledge graph space exploding on all accounts (interest, use cases, funding), centrifugal and centripetal forces are simultaneously at play. While the "wild, early days" of knowledge graph technology are gone, the 20 year anniversary of the Semantic Web is a good opportunity to reflect on what worked and what didn't and to move forward in a pragmatic way.

A testament to the fact that this space is booming: more offerings are available every day, the quality and quantity of knowledge sharing is rising to meet the demand, and at the same time we are starting to see consolidation — in vendors, models, and standards.