Description
Although AI has incredible potential, it has three weak links: 1. Blackbox, lack of explainability2. Silos, slews of siloed systems across the AI ecosystem3. Low-performance, most of ML/DL based AI systems are SLOW.Fixing these problems will pave the road to strong and effective AI. Graph databases, particularly high-performance graph database or graph computing, should allow this to happen.The Essential Criteria of Graph Databases simply broadens the horizon of graph applications. The book collects several truly innovative graph applications in asset-liability and liquidity risk management, which hopefully will spark readers' interest in further broaden the reach and applicable domains of graph systems.- Presents updates on the essential criteria of graph database(s) and how they are quite different from traditional relational database or other types of NoSQL DBMS or any of those big-data frameworks (i.e., Hadoop, Spark, etc.)- Clearly points out the key criteria that readers should pay attention to- Teaches users how to avoid common mistakes and how to get hands-on with system architecture design, benchmarking or selection of an appropriate graph platform/vendor-system
Table of Contents
1. The history of graph computing and graph databases2. The fundamentals and principles of graph database3. Graph database architectural design4. Graph algorithms5. Scalable graph database6. The world powered by graph technology (or: graph database use cases)7. Planning, benchmarking, and optimization of graph systems
