While distributed graph processing engines have become popular for processing large graphs, these engines are typically configured with a static set of servers in the cluster. In other words, they ...

In the era of big data, distributed graph processing frameworks have become important in processing large-scale graph datasets. Such distributed frameworks exhibit major advantages with respect to ...

Scalable graph processing system with Neo4j and distributed data pipeline using Kubernetes, Kafka, and Docker for NYC taxi trip analysis. Two-phase project implementing graph algorithms and building a ...

The considerable interest in distributed systems that can execute algorithms to process large graphs has led to the creation of many graph processing systems. However, existing systems suffer from two ...

Graph algorithms and processing form the backbone of numerous applications across science and industry, ranging from social network analysis to large-scale data management.

Objectivity, Inc., a provider of data management solutions, has released the first version of its enterprise-ready distributed graph database product, following a successful beta program which began ...

Efficiently and quickly chewing through one trillion edges of a complex graph is no longer in itself a standalone achievement, but doing so on a single node, albeit with some acceleration and ...

By having a native C++ graph storage engine (GSE) work side-by-side with a graph processing engine (GPE) to handle of data and algorithms and by using parallelism and a distributed architecture.