Declarative Graph Query Language support for Web and Blockchain Decentralized Applications, Analytics, and Compliance

区块链支持的智能合约和去中心化应用程序（dApps）提供了一定程度的信任，无需中介，并有可能使行业、政府和社会受益。区块链或分布式账本技术（dlt）由网络中的参与者组成，该网络存储分类账副本，并就哈希指针链上的区块序列（加密签名交易）的有效性达成共识。随着dlt领域的多样化，跨多个区块链（公共和私有）进行交易的更复杂的dapp正在出现。我们的研究目标是开发基于图的查询语言技术和工具，以解决公共和私有区块链以及跨不同领域日益复杂的分散应用程序所带来的数据管理挑战。我们讨论三个互补的研究主题。主题1)我们将研究使用区块链特定结构扩展的图形查询语言如何为区块链dApps带来好处。我们以前定义和实现图遍历语言的经验表明，具有精心设计的表达能力的查询语言可以有效地实现，支持优化和可伸缩性技术。区块链内部和周围信息的丰富性与图模型非常吻合（例如，参与者形成一个网络，交易输入、输出和其他执行属性定义了复杂的信息图）。主题2)除了改进脚本中对事务性查询的支持外，我们的研究还将着眼于在dlt背景下支持分析的机会。在dlt中支持各种分析、可视化和机器学习技术将需要从多个区块链和数据存储中提取、验证和转换数据的管道。主题3)我们计划以我们最近在基于查询的隐私日志合规性方面的工作为基础，开发新技术来解决dlt中的隐私、合规性、问责制和执行问题。我们的方法将形式化这些新问题，并发展新的语言建议，试图描述与实际应用相关的理论性质。我们将实现原型并为开源开发做出贡献。我们将通过实验验证我们的建议，在需要时开发新的基准。我们的实验将使用真实数据（公共区块链是一个丰富的来源）。我们的研究将提供基于图形的查询解决方案，以解决关键的区块链数据挑战，旨在促进可扩展的、数据丰富的dapp的开发和分析。我们期望我们的研究成果和我们的开源贡献将对区块链技术提供商和采用者产生重大影响。

Blockchain enabled smart contracts and decentralized applications (dApps) provide a level of trust without intermediation and have the potential to benefit industries, government, and society. Blockchains, or Distributed Ledger Technologies (DLTs), consists of participants in a network that store ledger replicas and come to consensus on the validity of a sequence of blocks (of cryptographically signed transactions) chained by hash pointers. As DLTs domains diversify, more complex dApps transacting across multiple blockchains (public and private) are emerging. The objectives of our research are to develop graph-based query languages techniques and tools to address the data management challenges emerging from increasingly complex decentralized applications over public and private blockchains, and across diverse domains. We address three complementary research themes. Theme 1) We will investigate how graph query languages extended with blockchain specific constructs can bring benefits to blockchain dApps. Our previous experience defining and implementing graph traversal languages suggests that query languages with carefully designed expressiveness can be efficiently implemented, supporting optimization and scalability techniques. The richness of the information present within and around blockchains fits well with graph models (e.g., the participants form a network, and the transactions inputs, outputs, and other execution properties define complex information graphs). Theme 2) In addition to improving the support for transactional queries in scripts, our research will look at the opportunities for supporting analytics in the context of DLTs. Supporting a variety of analytic, visualization, and machine learning techniques in DLTs will require pipelines extracting, validating, and transforming data from multiple blockchains and data stores. Theme 3) We plan to build upon our recent work in query-based compliance over privacy logs to develop new techniques to address privacy, compliance, accountability, and enforcement issues in DLTs. Our approach will formalize these new problems and develop new language proposals, attempting to characterize the theoretical properties that are relevant in practical applications. We will implement prototypes and contribute to open source development efforts. We will validate our proposals experimentally, developing novel benchmarks when needed. Our experiments will use real data (public blockchains are a rich source). Our research will provide graph-based query solutions to address key blockchain data challenges, aiming to facilitate the development and analysis of scalable, data rich dApps. We expect that our research results and our open source contributions will have a significant impact on blockchain technology providers and adopters.