Graph & Beyond Lunch Break #15.5: Aggregating Time-Series Data with AQL
Graph & Beyond Lunch Break #15.5: Aggregating Time-Series Data with AQL The new WINDOW operation added to AQL in ArangoDB 3.8 allows you to compute running totals, rolling averages, and other statistical properties of your sensor, log, and other data. You can aggregate adjacent documents (or rows if you will), as well as documents in…
More InfoGraph & Beyond Lunch Break #15: Entity Resolution
Graph & Beyond Lunch Break #15: Entity Resolution Entity Resolution is a technique to address the problem where a real-world entity is represented as multiple digital entities. The reason for this duplication can be fraud but sometimes it is not. Depending on the use case, this duplication can be expected and intentional. In this Lunch…
More InfoGraph & Beyond Lunch Break #15: Entity Resolution
Entity Resolution is a technique to address the problem where a real-world entity is represented as multiple digital entities. The reason for this duplication can be fraud but sometimes it is not. Depending on the use case, this duplication can be expected and intentional. In this Lunch Break, join Victor Moey as he shows why…
More InfoGraph & Beyond Lunch Break #14: Monitoring ArangoDB
Graph & Beyond Lunch Break #14: Monitoring ArangoDB Calling monitoring services best practice, especially those in production, is surely understated. Not only does one gain insight into resource utilisation and can thus responsibly and optimally adjust the environment for apparent needs, but one is put into the position to understand failures on the application as…
More InfoGraph & Beyond Lunch Break #14: Monitoring
Calling monitoring services and certainly, those in production as best practices is surely understated. Not only does one gain insight into resource utilisation and can thus responsibly and optimally adjust the environment for apparent needs But one is put in the position to understand failures on the application as well as service side. ArangoDB exposes…
More InfoGraph & Beyond Lunch Break #13: Kubernetes Meets Graphs – ArangoDB
Graph & Beyond Lunch Break #13: Kubernetes Meets Graphs Join us in this Lunch Session to learn about how Kube-Arango makes deploying and managing ArangoDB on Kubernetes a breeze. Kube-Arango is ArangoDB’s Kubernetes Operator, which by the way also powers ArangoDB’s managed Service ArangoGraph. We will cover the entire Lifecycle of an ArangoDB Cluster on Kubernetes, starting…
More InfoGraph & Beyond Lunch Break #13: Kubernetes Meets Graphs – ArangoDB
In this Lunch Session you will learn how Kube-Arango makes deploying and managing ArangoDB on Kubernetes a breeze. Kube-Arango is ArangoDB’s Kubernetes Operator, which by the way also powers ArangoDB’s managed service ArangoGraph. ArangoDB CTO Jörg Schad covers the entire Lifecycle of an ArangoDB Cluster on Kubernetes, starting from deployment, over scaling, to upgrades. Furthermore, he looks into…
More InfoGraph & Beyond Lunch Break #12: Knowledge Graphs – ArangoDB
This lunch session is an introductory video and focuses on the general concepts of Knowledge Graphs. Chris Woodward, ArangoDB Developer Relations Engineer, covers: What is a Graph Database? What is a Knowledge Graph? How do you Build a Knowledge Graph? He finishes with a discussion of some existing tools for dealing with semantic data, and…
More InfoGraph & Beyond Lunch Break #11: Fuzzy Search – ArangoDB
Graph & Beyond Lunch Break #11: Fuzzy Search When dealing with real-world text retrieval, we often not only care about exact matches to our search phrase but need to consider for example typos or alternative spellings. “Fuzzy search” is an umbrella term referring to a set of algorithms for such approximate matching. Usually, such algorithms…
More InfoGraph & Beyond Lunch Break #9: ArangoML – ArangoDB
Graph & Beyond Lunch Break #9: ArangoML How are Graph Databases related to Machine Learning? In this Graph & Beyond Lunch Session Jörg Schad will give an overview of different parts of the ML pipeline and how ArangoDB fits in. In particular, we will be talking about feature engineering, Graph ML, Embeddings, MLOps, and Metadata.…
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