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Relational databases were conceived to digitize paper forms and automate well-structured business processes, and still have their uses. But RDBMS cannot model or store data and its relationships without complexity, which means performance degrades with the increasing number and levels of data relationships and data size. Additionally, new types of data and data relationships require schema redesign that increases time to market. A native graph database like Neo4j naturally stores, manages, analyzes, and uses data within the context of connections meaning Neo4j provides faster query performance and vastly improved flexibility in handling complex hierarchies than SQL. This webinar explains why companies are shifting away from RDBMS towards graphs to unlock the business value in their data relationships.

Whether it's directly improving patient care or helping lower costs to provide more access to healthcare, organizations are continuing to use IT to move the needle for an industry that is at a pivotal point in innovation. Learn how our innovative storage solutions can help your organization meet its healthcare Big Data challenges: http://www.netapp.com/us/solutions/industry/healthcare/

Dear NSA, you can do whatever with my data. But not with my eyes. Those slides are hideous. So here's a quick revamp of your PRISM slides.

What we carry with us in our everyday lives and interactions is just as important for our success as our technical skills and achievements. This is what I carry with me. What do YOU carry? Slides designed and produced with Haiku Deck for iPad. Set your story free with Haiku Deck at http://www.haikudeck.com/ You can learn more about Jonathon Colman at http://www.jonathoncolman.org/

This document describes the design and implementation of a visualization tool to provide more information about the impact of academic publications than citation counts alone. It represents references, citations, and self-citations over time and allows viewing individual papers or collections in different contexts. The tool uses shape grammars to classify papers by impact level into glyphs for compact overviews. It has been implemented in D3 and deployed on the INSPIRE platform for high energy physics publications. Future work may include a Chrome extension and exploring better impact metrics.

1) Graph technology can be leveraged to fight financial fraud by detecting fraud rings and relationships between entities like individuals and locations that may indicate fraudulent activity. 2) A graph database like Neo4j is well suited for fraud detection because it can easily model and traverse complex relationships in connected data, identify patterns more quickly than SQL, and enable near real-time response. 3) A fraud detection demo using Neo4j showed how operational data from various sources could be integrated and analyzed to generate alerts when potential fraud cases are detected.

This document provides an agenda for the Neo4j GraphDay Stockholm event on February 21, 2017. The agenda includes speakers from Neo4j and partner companies, and sessions on use cases, hands-on demos, partner presentations, training, and Q&A clinics. The event will take place in Stockholm and cover topics like graphs in action, recommendations in retail, and the manufacturing value chain.

This document discusses how graph databases like Neo4j can help with analytics and information management. It provides examples of how graph queries in Neo4j are simpler than equivalent SQL queries for finding connections between nodes. Graph databases allow for impact analysis and easily reflecting new relationships. They also help with recommendations by incorporating events from the current user session.

The document discusses how Telia scaled its Neo4j graph database to support millions of homes using Kubernetes. It describes the zone API architecture built on Kubernetes, including microservices for the zone API, TheZone agent, API management, and databases like Neo4j, Redis, and Cloud SQL. It also discusses how Kubernetes features like namespaces, auto-scaling, node selectors, and stateful sets were used to scale the Neo4j graph database using causal clustering to support millions of users and billions of requests per day.

In this webinar we'll explore a data set using Neo4j and Cypher and compare the approach we might take with a relational database and SQL. We'll cover the following topics: Modeling the data set Importing the data Querying the data Evolving the model and queries as the data changes.

The document announces a GraphTalks event in Cologne in February 2017 hosted by Neo Technology. The agenda includes an introduction to graph databases and Neo4j, a presentation on semantic data management, and an open networking session. Complex topics like the internet of things, domain modeling, and traditional vs graph approaches to data modeling will also be discussed.

The document discusses how graph databases like Neo4j can help drive digital transformation, especially in retail. It provides examples of large retailers like Adidas, eBay, and Walmart using Neo4j to power personalized customer experiences, optimize delivery routes, and make relevant product recommendations. The document also discusses how graphs are well-suited for modeling interconnected fraud patterns and can help detect fraud in real-time. It highlights the benefits of Neo4j for augmented connected analysis over legacy technologies.

Ian closely looks at design and implementation strategies you can employ when building a Neo4j-based graph database solution, including architectural choices, data modelling, and testing.g

The document discusses how graph databases can be used to improve identity and access management (IAM) systems. Traditional IAM systems are based on rigid hierarchies and siloed data, but organizations are becoming more complex with interconnected relationships. Graph databases allow IAM data to be modeled as a graph, enabling querying of complex relationships in real-time. The document provides an example of how a telecom company improved their IAM system performance by moving it to a graph database. It also outlines how graph databases can be incorporated into existing IAM architectures and systems.

This document summarizes a seminar presentation on the graph database Neo4j. It introduces trends in big data like increasing data size and connectedness. It also discusses NoSQL databases and describes different types including column, document, key-value, and graph databases. The document focuses on graph databases, provides examples of graph-structured data, and gives an overview of the graph database Neo4j, its data model, query language Cypher, and pros and cons.