Time-evolving Graph Processing on Commodity Clusters - Databricks

Time-evolving Graph Processing on Commodity Clusters

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Real-world graphs are seldom static. Applications that generategraph-structured data today do so continuously, giving rise to an underlying graph
whose structure evolves over time. Mining these time-evolving graphs
can be insightful, both from research and business perspectives. While several
works have focused on some individual aspects, there exists no general purpose
time-evolving graph processing engine.

We present Tegra, a time-evolving graph processing system built
on a general-purpose dataflow framework. We introduce Timelapse, a
flexible abstraction that enables efficient analytics on evolving graphs by
allowing graph-parallel stages to iterate over complete history of nodes. We use
Timelapse to present two computational models, a temporal analysis model for
performing computations on multiple snapshots of an evolving graph, and a generalized
incremental computation model for efficiently updating results of computations.

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About Anand Iyer

Anand Iyer is a senior product manager at Cloudera. His primary areas of focus are platforms for real-time streaming, apache spark, and tools for data ingestion into hadoop. Before joining Cloudera, he worked as an engineer at LinkedIn, where he applied machine learning techniques to improve the relevance and personalization of LinkedIn's Feed. Anand has extensive experience in leveraging big data platforms to deliver products that delight customers. He has a master's in computer science from Stanford and a bachelor's from the University of Arizona.