BACKGROUND

Big Data refers to a collection of complex and large-scale data sets. Big data technologies include storage components, processing capabilities, and applications that have been developed to handle such large volumes of data. In this field, most of graph analysis solutions are based on Hadoop, at the expense of performance and flexibility. To overcome these weaknesses, BigGrph architecture has been developed to load and process distributed graph data in the quickest and easiest way possible.

KEY BENEFITS vs. STATE OF THE ART

• Easy to install and use
• Provides flexible application programming models (BSP, centralized, mobile-agents, map/reduce)
• High-performance (memory-efficient data structures, optimized implementation)
• Compatible with Shared-Nothing (SNCs), NAS-based, and Torque clusters.
• Suited for the experimentation of graph distributed algorithms

DEVELOPMENT STATUS

Prototype available

APPLICATIONS

Healthcare Retail / Supply Chain Travel and Transportation Social Network Energy