How does graph data help inform a self-organizing network?
Who is this presentation for?Data engineers, data architects, developers
If you use electricity in the United States, you likely contribute to a distributed and hierarchical graph every moment of your day.
Within those green boxes outside your home or business, your power company likely uses an edge-computing network to distribute readings of voltage levels from one power recipient to another. Readings are collected over a time interval and communicated back to a central monitoring system via a self-organizing sensor network. In the real world, these sensors are free to communicate with any nearby sensor or tower. This communication structure creates dynamic, hierarchical structures within a graph dynamic and ever evolving in structure.
The transfer of power between a plant and your home creates one of the most beautiful, dynamic, distributed, and complex graph problems we interact with constantly.
Denise Gosnell demonstrates how hierarchical data works within the nation’s power grid. She introduces you to hierarchies in edge computing with IoT sensors, the problem statement, data, and schema she uses in her examples. You’ll set up and apply the concepts from hierarchically structured data to solve a real-world problem from the power industry. Once you’re familiar with the hierarchies, you’ll become comfortable with the flood of terminology and discover where they fit into the overarching problem.
There are two main styles of queries for working with hierarchical data. The first query pattern walks from the bottom of the hierarchy to the top, or from leaves to roots. As you can probably guess, the other query pattern walks the reverse direction, from roots to leaves. Before you leave, you’ll see how the dynamic topology of a self-organizing network proactively informs failure scenarios for a power company.
The extended version of this content is available in The Practitioners Guide to Graph Data.
- General knowledge of graph data
What you'll learn
- Identify hierarchical graph data within a communication network about the power grid
- Learn how to use the Apache TinkerPop Gremlin query language to perform basic recursive queries on the data using depth-first search and translate the structure of the hierarchical data into a way to proactively inform failure scenarios within the problem domain
As the Chief Data Officer of DataStax, Dr. Denise Koessler Gosnell applies her experiences as a machine learning and graph data practitioner to make more informed decisions with data. Prior to this role, Dr. Gosnell joined DataStax to create and lead the Global Graph Practice, a team that builds some of the largest distributed graph applications in the world. Dr. Gosnell earned her Ph.D. in Computer Science from the University of Tennessee as an NSF Fellow. Her research coined the concept “social fingerprinting” by applying graph algorithms to predict user identity from social media interactions.
Dr. Gosnell’s career centers on her passion for examining, applying, and advocating the applications of graph data. She has patented, built, published, and spoken on dozens of topics related to graph theory, graph algorithms, graph databases, and applications of graph data across all industry verticals. Prior to her roles with DataStax, Gosnell worked in the healthcare industry, where she contributed to software solutions for permissioned blockchains, machine learning applications of graph analytics, and data science.
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