The Essential Criteria of Graph Databases collects several truly innovative graph applications in asset-liability and liquidity risk management to spark readers’ interest and further broaden the reach and applicable domains of graph systems. Although AI has incredible potential, it has three weak links: 1. Blackbox, lack of explainability, 2. Silos, slews of siloed systems across the AI ecosystem, 3. Low-performance, as most of ML/DL based AI systems are SLOW. Hence, fixing these problems paves the road to strong and effective AI.

• 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