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Books

The Essential Criteria of Graph Databases

Author: Ricky Sun
Publisher: Elsevier

This book broadens the horizons of graph applications. It reviews several truly innovative graph applications in real-time decision-making, asset-liability, and liquidity risk management, which enable readers to further broaden their grasp of the reach and applicable domains of graph systems. This book paves the way for strong and effective artificial intelligence (Al) by means of graph databases, especially high-performance graph databases or graph computing by resolving the three weak links of Al: its "blackbox" nature, its low performance and low efficiency, and its building of silos.

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Papers

A Unified Graph Framework for Storage-Compute Coupled Cluster and High-Density Computing Cluster

Authors: Lynsey Lin, Jamie Chen, Ricky Sun, Jason Zhang, and Victor Wang
Published Year: June 2024

In this paper, we present a novel unified framework that seamlessly integrates distributed computing and high-density graph computing. Our approach leverages a hybrid architecture that combines the strengths of both paradigms, enabling efficient graph traversal and computation while ensuring scalability and flexibility.

The key contributions of our work include: 1. A distributed graph storage and partitioning strategy that maximizes data locality and minimizes cross-machine communication overhead. 2. A high-density graph computing cluster optimized for memory-efficient operations and parallel execution. 3. Query optimization, include partition pruning, network optimization, parallel processing, and predicate pushdown. 4. Two approaches for implementing Pregel for graph-wide algorithms: a shard-based SCC (Storage-Compute Coupling) implementation and an HDC (High-Density Computing) based implementation. Our unified framework addresses the critical challenges faced by contemporary graph databases and analytics frameworks, providing a robust and versatile solution tailored to the diverse requirements of modern graph-based applications. Expand

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Graph XAI: Graph-augmented AI with ADEV

Authors: Ricky Sun, Yuri Simione, Jason Zhang, and Victor Wang
Published Year: 2023

Today's big data and AI frameworks face problems like questionable accuracy, shallow data processing depth, black-box in-explainability, and oftentimes low processing speed. This paper summarizes the work of Ultipa, introducing Graph XAI (Graph-augmented AI) and highlighting ADEV (Accuracy, Depth, Explainability, and Velocity). In contrast to many systems that sample data due to inability to traverse datasets thoroughly and quickly, particularly hindered by hotspot supernodes, Ultipa's graph system is designed from data structure and system architecture perspective to allow for ultra-low latency deep penetration, and accuracy is achieved with exhaustive traversal, which also allows for exponentially faster velocity. As graph data are ideally queried and processed using graph query languages and algorithms instead of the two-dimensional SQL and stored procedures, the intuitiveness and explainability are crucial in ensuring ADEV being fulfilled, this paper highlights how Ultipa's graph-native query language facilitates real-time recursive queries like path-finding, K-hopping, auto-networking, or identification of topological structures and communities works hand-in-hand with Ultipa's WebGL-powered graph manager to ensure end-toend celerity and explainability. Expand

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Design of Highly Scalable Graph Database Systems without Exponential Performance Degradation

Authors: Ricky Sun, Jamie Chen
Published Year: June 2023

The main challenge faced by today's graph database systems is sacrificing performance (computation) for scalability (storage). Such systems probably can store a large amount of data across many instances but can't offer adequate graph-computing power to deeply penetrate dynamic graph dataset in real time. A seemingly simple and intuitive graph query like K-hop traversal or finding all shortest paths may lead to deep traversal of large amount of graph data, which tends to cause a typical BSP (Bulky Synchronous Processing) system to exchange heavily amongst its distributed instances, therefore causing significant latencies. This paper proposes three schools of architectural designs for distributed and horizontally scalable graph database while achieving highly performant graph data processing capabilities. The first school, coined HTAP, augments distributed consensus algorithm RAFT paired with vector-based computing acceleration to achieve fast online data ingestion and real-time deep-data traversal in a TP and AP hybrid mode. The second school, named as GRID, leverages human-intelligence for data partitioning, and preserving the HTAP data processing capabilities across all partitioned clusters. The last school incorporates SHARD and advanced GQL optimization techniques to allow data partitioning to be done fully automated yet strive to achieve lower latency via minimum I/O cost data migration model when queries spread across multiple clusters. Expand

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The Linked Data Benchmark Council (LDBC): Driving Competition and Collaboration in the Graph Data Management Space

Authors: Jason Zhang, Bin Yang, Xinsheng Li, et al
Published Year: 2023
Publication Venue: TPCTC

Graph data management is instrumental for several use cases such as recommendation, root cause analysis, financial fraud detection, and enterprise knowledge representation. Efficiently supporting these use cases yields a number of unique requirements, including the need for a concise query language and graph-aware query optimization techniques. The goal of the Linked Data Benchmark Council (LDBC) is to design a set of standard benchmarks that capture representative categories of graph data management problems, making the performance of systems comparable and facilitating competition among vendors. LDBC also conducts research on graph schemas and graph query languages. This paper introduces the LDBC organization and its work over the last decade. Expand

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