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

支持区块链的智能合同和去中心化应用程序(Dapp)提供无需中介的信任级别，并具有 使行业、政府和社会受益的潜力。区块链，或分布式分类帐技术 (DLTS)，由存储分类帐副本的网络中的参与者组成，并就 由散列指针链接的(加密签名事务)块序列的有效性。AS DLTS域多样化，更复杂的Dapp跨多个区块链(公共和私有)进行交易 正在涌现。 我们的研究目标是开发基于图的查询语言 应对日益复杂的数据管理挑战的技术和工具 跨公共和私有区块链以及跨不同域的分散应用。 我们解决了三个互补的研究主题。 主题1)我们将调查图形查询语言如何使用特定于区块链的 构造可以给区块链Dapp带来好处。我们之前定义和实现图遍历的经验 语言表明，具有精心设计的表达能力的查询语言可以是高效的 已实施，支持优化和可伸缩性技术。当前信息的丰富性 区块链内部和周围与图模型非常吻合(例如，参与者形成网络，并且 事务输入、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DLTS的背景下支持分析的机会。支持多种分析、 DLT中的可视化和机器学习技术将需要管道提取、验证和转换 来自多个区块链和数据存储的数据。 主题3)我们计划在基于查询的隐私日志合规性方面的最新工作基础上开发 解决DLTS中的隐私、合规、问责和执法问题的新技术。 我们的方法将使这些新问题正式化，并开发新的语言建议，试图 描述在实际应用中相关的理论属性。我们将实施 原型并为开放源码开发工作做出贡献。我们将验证我们的建议 在实验方面，在需要时开发新的基准。我们的实验将使用真实数据(公共数据 区块链是一个丰富的来源)。 我们的研究将提供基于图的查询解决方案，以解决关键区块链 数据挑战，旨在促进可扩展、数据丰富的Dapp的开发和分析。我们希望我们的 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 querybased 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.