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How I Learned to Stop Worrying and Love Graph Databases /
Presented by Michael Zelenetz - Analytics Project Leader at New York-Presbyterian Hospital Healthcare data is highly connected but often lives in silos. Graph databases are promising emerging technologies for working with highly connected data. This talk will introduce data scientists to Neo4j--the...
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| Formato: | Video |
| Idioma: | Inglés |
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[Erscheinungsort nicht ermittelbar] :
Data Science Salon,
2019
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| Edición: | 1st edition. |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
| Sumario: | Presented by Michael Zelenetz - Analytics Project Leader at New York-Presbyterian Hospital Healthcare data is highly connected but often lives in silos. Graph databases are promising emerging technologies for working with highly connected data. This talk will introduce data scientists to Neo4j--the leading graph database--and will discuss a proof of concept implementation at New York Presbyterian and will demonstrate some of the network analyses we were able to do as a result. This talk will be developer/data scientist focused and will include code snippets. We will introduce the graph data model and loading data into the database. We will discuss the pros and cons of graph databases. We will finish off with some practical examples from out proof of concept including community detection algorithms, using centrality to find providers who may be spreading infections, and examining physician referral patterns. Participants will leave being able to describe a graph database. They should be able to identify situations that may benefit from implementing a graph database. Finally, they should be able to create a simple graph model. |
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| Notas: | Online resource; Title from title screen (viewed September 10, 2019). |
| Descripción Física: | 1 online resource (1 video file, circa 20 min.) |