Scalable Graph ML
Graph ML is the future of data analytics
Who can introduce me to x?
Who is the most connected person?
Predict potential connections
ArangoDB as the foundation for Graph ML
Designed from ground up to scale Enterprise use cases
- Simple Ingestion
Easy integration in existing data infrastructure + connectors to all leading data processing and data ecosystems
- Open Source
Extensibility, Community, especially large community maintained library
- NLP Support
Built-In Text Processing, Search, and Similarity Ranking
NLP with ArangoSearch
ArangoSearch provides information retrieval features, natively integrated into ArangoDB’s query language and with support for all data models. It is primarily a full-text search engine, a much more powerful alternative to the full-text index type.
We have all seen product recommendations like “People who have looked that item x, also bought item y.” For this, we consider a simple dataset with user ratings for movies and then use a technique called Collaborative Filtering to identify which new movies might be worth watching based on other movies we liked.
Fraud Detection & Analytics
Today’s criminals are constantly coming up with new techniques to hide their activities by forming fraud networks with stolen or synthetic identities.
Enterprise Knowledge Graphs
Enterprise Knowledge Graphs (EKGs) have been on the rise and are incredibly valuable tools for harmonizing internal and external data relevant to an organization into a common semantic model. Enterprises benefit from improved operational efficiency and competitive advantages for their business units.
Talks & Events
Graph Analytics with ArangoDB
In this lunch break, ArangoDB Head of Engineering Jörg Schad will explore a number of use cases suitable for Graph Analytics — and in particular leverage ArangoDB’s Graph Algorithms Library.
O'Reilly: Graph Powered
Many powerful machine learning algorithms—including PageRank (Pregel), recommendation engines (collaborative filtering), and text summarization and other NLP tasks—are based on graphs. And there are even more applications once you consider data preprocessing and feature engineering, which are both vital tasks in machine learning pipelines.
Many powerful Machine Learning algorithms are based on graphs, e.g., Page Rank (Pregel), Recommendation Engines (collaborative filtering), text summarization and other NLP tasks.
In this hands-on workshop, ArangoDB VP of Engineering Jörg Schad and Developer Relations Engineer Chris Woodward will explore a number of use cases suitable for Graph Analytics — and in particular leverage ArangoDB’s Graph Algorithms Library.