Scalable Graph Analytics on Big Graphs (Social, Supply Chain)

"Scalable Graph Analytics on Big Graphs" is a considerable advancement in the state of the art and science of large-scale data processing and graph analytics By addressing and overcoming the two most important challenges of scalability performance and ease of use and consequently improving the ability of graph processing systems this effort will provide a practical and efficient platform with the potential of finding out a wealth of insights from the interconnected data of social networks and supply chains The novel hybrid compute model along with intelligent graph partitioning is expected to deliver a dramatic speedup over the classical synchronous models for analyzing large graphs The native support for dynamic graphs is an integral part of the distinguishing feature set that enables applications from fraud detection to dynamic logistics optimization The availability of both high-level query language and programmatic APIs is expected to make the system accessible for from data analysts to data scientists democratizing the graph analytics capability The integrated visualization and exploration capabilities will greatly impact how users will be exposed to graph data from a static report to an interactive process of discovery and knowledge building and by seamlessly integrating with the bigger data ecosystem this proposed system will make it easy for organizations to create complex analytical processes

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