Querying and Seeing Time Series Data within a Self-Organizing Mesh Network
💻 Abstract:
Self-organizing networks, like the power grid in the United States, rely on communication between sensors and call towers for transmitting the network's status. This communication network forms a dynamic graph that helps power companies have real-time visibility into different network failure scenarios. For instance, what happens if the tower goes down? And, how does a graph data structure get involved in the network's healing process? In this session, Denise Gosnell will show you how we built a graph database to model a dynamic network within the power grid. Within a notebook environment, she will walk through the code for the database model and graph database queries. Then, we will show how to apply path information to triage network sensors that are at risk. You will walk away with knowledge of a new use case for graph data in the power industry and assets to play with the data and code on your own. Get ready to go deep into the world of graph data and graph queries. Technical Assets for Audience Usage Afterwards: Content Repository on GitHubhttps://github.com/datastax/graph-book
🔊 Speaker bio:
Chief Data Officer of DataStax
Denise Koessler Gosnell is the chief data officer at DataStax, where she applies her experiences as a machine learning and graph data practitioner to make more informed decisions with data. Her 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.
Previously, Denise created and led the global graph practice, a team that builds some of the largest distributed graph applications in the world at DataStax. She also has deep experience working in data engineering and data science roles in the bioinformatics, telecommunications, and healthcare industries. Denise 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.
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Timestamps:
0:00 Intro
0:10 Introduction of the speaker
2:30 Our Session
4:29 Why Graph Technology?
9:30 Self-Organizing Sensor Networks
10:14 Where do we find these graphs?
14:49 Understanding Failure Scenarios
16:48 What constitutes risk/failure in the network?
19:50 1a: Which sensors sent information to this tower?
22:39 Data & Code
25:04 What does the data look like? Tower Vertices
25:43 What does the data look like? Edges
26:33 What is the graph model?
27:28 What is the graph model? Vertex Labels
28:30 What is the graph model? Edge Labels
29:26 How do we load data?
31:11 1b: To which towers did this sensor send information? 4 Steps:
34:03 Demo
53:16 Wrapping things up
Questions Throughout The Presentation
18:58 What is the tower?
50:02 What would happen if we have multiple paths if we reach two towers?
❓ Q&A ❓
57:04 Is there a way to analyze the JSON result visually?
58:02 How and who designed the network in the first place? Can we assume the path connections are reasonable?
1:00:35 Is there a way to improve performance of the query which keeps track of previously visited tower? How do you visualize the performance?
1:02:31 How would you use the conditional probabilities between nodes to help?
1:04:13 What tools do you prefer for visualization of graphs?
1:06:36 Have you used any graph embedding techniques?
1:11:38 How does Gremlin compare with Neo4J?
1:12:23 Have you ever used deep learning to generate graphs?
1:15:26 Closing remarks