SBIR Phase I: GraphSQL: Powering Relational DBMS with Fast and Easy-to-Use Graph Analytics

这个小企业创新研究（SBIR）第一阶段项目将开发新技术和软件系统，为关系数据库系统（RDBMS）增加快速，易于使用的图形分析。查询和分析海量图形数据（图形分析）不仅可以丰富和增强商业、社会和政府情报，而且可以显著促进科学技术发现。然而，大多数现有的图数据（商业、金融、政府和社会网络数据）都存储在传统的RDBMS中，这种RDBMS对图分析提供的基本支持很少。这是因为图分析需要大量的递归和遍历操作，这些操作很难表示，而且在RDBMS中执行成本也非常高。为了解决这个基本挑战，该项目将开发一个强大的图引擎，并透明地将RDBMS与图引擎连接起来。该项目的预期结果包括构建图形处理引擎和RDBMS与图形引擎之间的连接器的新技术，以及beta版本的软件系统，该系统将由两到三个早期采用者/客户进行测试和使用。该项目的更广泛的影响/商业潜力将有助于更好地洞察人们之间关系的复杂性，他们的决策以及他们在商业和智能的所有领域与他人的互动，例如在线社交网络，营销和广告，银行和金融，物流和运输，电信，医疗保健和医院，国土安全，生物工程和药物发现等。该项目开发的技术和软件还可以直接为五大分析领域（终端用户查询和报告分析、数据仓库管理、财务绩效和战略管理应用、客户关系分析应用和数据仓库生成）做出贡献，每个领域的全球收入都超过10亿美元。

This Small Business Innovation Research (SBIR) Phase I project will develop novel technologies and a software system which adds fast, and easy-to-use graph analytics to the relational database system (RDBMS). Querying and analyzing massive graph data (graph analytics) not only can enrich and enhance business, social and government intelligence, but also can help significantly advance scientific and technology discovery. However, most of the existing graph data (business, financial, governmental, and social networking data) are stored in the traditional RDBMS that provides little fundamental support for graph analytics. This is because graph analytics requires extensive recursive and traversal operations, which are hard to represent and prohibitively expensive to execute in RDBMS. To address this fundamental challenge, this project will develop a powerful graph engine and transparently connect RDBMS with the graph engine. The anticipated results of the project include novel techniques for building a graph processing engine and connectors between RDBMS and the graph engine, and a beta-version software system, which will be tested and employed by two or three early adopters/customers.The broader impact/commercial potential of this project will be to help deliver better insights into the complex nature of relationships between people, their decisions and their interactions with others in all areas of businesses and intelligence, such as online social networking, marketing and advertisement, banking and finance, logistics and transportation, telecommunications, healthcare and hospitals, homeland security, bio-engineering and drug discovery, etc. The technologies and software developed in this project can also directly contribute to the five major analytics segments (end-user query and reporting analysis, data warehouse management, financial performance and strategy management applications, custom relationship analytics applications, and data warehouse generation), each with the global revenue of more than one billion dollars.