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Overview for graph processing

NuGraph: GraphDB as a Cloud Service Built Upon JanusGraph and FoundationDB - Jun Li & Hieu Nguyen, eBay

Ebay has built a GraphDB cloud service called NuGraph, which is based on JanusGraph. FoundationDB is chosen as the JanusGraph’s storage plugin, because of its high-performance and distributed transaction support. Jun Li and Hieu Nguyen from eBay present the GraphDB architecture, and focus on how they deploy and manage FoundationDB in Kubernetes, how they improve JansGraph query performance in a cross-data center environment, how they bulk load the graph into FoundationDB with its transactional support, and how they secure the 3-tier cloud service with limited security support from FoundationDB.

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Graph-based ML Anomaly Detection and Insights for Envoy Systems - Anoop Koloth & Hanzhang Wang, eBay

In this talk Anoop Koloth and Hanzhang Wang from eBay present how they managed to build a monitoring system and leveraged data generated from envoy clusters:
(1) Processing billions of hits served from different platforms from worldwide in real-time.
(2) Key Performance Indicators from Envoy ecosystem.
(3) Effective ML solution for proactive monitoring diversified eBay systems.
(4) Graph-based modeling and algorithms to deal with system complexity.
(5) Symbiosis and enhancement with existing SRE solution.

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People You May Know: Fast Recommendations Over Massive Data

This discussion presents the evolution “People You May Know” (PYMK) to its current architecture. The focus is on various systems built along the way, with an emphasis on systems built for LinkedIn most recent architecture, namely Gaia, a real-time graph computing capability, and Venice an online feature store with scoring capability, and how LinkedIn integrates these individual systems to generate recommendations in a timely and agile manner, while still being cost-efficient. 

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Scaling Geographic Analytics with Azure Databricks and Spark GraphX

Devon Energy has developed a solution that analyzes subsurface attributes and readings in order to predict the drill’s position. This solution which is built on Azure Databricks, scales to over a thousand cores in order to process a full well in one to two hours and uses graph and statistics libraries to help maximize oil and gas production.

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Graph Representation Learning to Prevent Payment Collusion Fraud

PayPal is at the forefront of applying large scale graph processing and machine learning algorithms to keep fraudsters at bay. In this talk, Venkatesh Ramanathan a data scientist at PayPal presents how advanced graph processing and machine learning algorithms are applied at PayPal for fraud prevention.

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Data science in practice: Examining events in social media

Jennifer Webb explains how cloud-based marketing company SuprFanz uses data science techniques and graph theory with Neo4j to generate live event attendance from social media platforms, email, and SMS to gain better insight into the behavior of music fans, event guests, and target audiences and predictive analytics to optimize promotional efforts. She also offers an overview of the unique technologies SuprFanz uses to drive traffic to events via social media.

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