STTR Phase I: A DLT Machine Learning Platform for Blockchain Warehousing

STTR 第一阶段：区块链仓储的 DLT 机器学习平台

• 批准号: 2112345
• 负责人: Mohammad Sadoghi
• 金额: $ 25.59万
• 依托单位: MOKA BLOX LLC
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-03-15 至 2022-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2112345&HistoricalAwards=false
• 关键词: STTR; Phase; DLT; Machine; Learning

The broader impact/commercial potential of this Small Business Technology Transfer (STTR) project improves the use of large-scale databases, data warehouses, and the software that used to commercially data-mine these resources. In the era of big data, many applications, such as machine learning and artificial intelligence, critically rely on data functionalities including efficiency, interoperability, and analysis. However, their data subsystems are challenged to meet these needs due to multiple technical limitations, such as centralized storage, homogeneous data formats, and tightly coupled workflows. This STTR project will develop a new framework to overcome these limitations to improve big data applications in various scientific fields, such as biological sciences, astronomy, and computational chemistry. At the end of this Phase I project, the requisite knowledge and foundational materials for developing a blockchain-based data warehousing middleware will be produced.This STTR Phase I project proposes to advance the specific knowledge and commercialization of DLTs (Distributed Ledger Technologies) and blockchains by creating novel and innovative environments for further development for DLTs and blockchains as a "virtuous cycle", starting with bottlenecks identified in modern data warehouses. The DLT tool developed here advances blockchain protocols, specifically for removing quadratic and speculative costs from orthodox protocol-based approaches to blockchains on conventional hardware and instituting linearizable and constant costs with other innovative programming methods (e.g., probabilistic pruning). New statistical, topological, and computational approaches will be researched and developed for these purposes, including for development of techniques applicable for machine learning, artificial intelligence, peta-scale and exa-scale computing, advanced scientific computing, and pedagogical academic development of future generations. The expected results include a new set of protocols and a unified tool for integrating data types and multiple networks into conventional data warehouses, especially for advancement of data warehouses struggling to keep pace with new blockchain data, metadata, and real-time analytics.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该小型企业技术转让 (STTR) 项目具有更广泛的影响/商业潜力，改善了大规模数据库、数据仓库以及用于对这些资源进行商业数据挖掘的软件的使用。 在大数据时代，机器学习和人工智能等许多应用程序严重依赖数据功能，包括效率、互操作性和分析。然而，由于集中式存储、同质数据格式和紧密耦合的工作流程等多种技术限制，他们的数据子系统在满足这些需求方面面临着挑战。该STTR项目将开发一个新的框架来克服这些限制，以改善大数据在生物科学、天文学和计算化学等各个科学领域的应用。 在第一阶段项目结束时，将产生开发基于区块链的数据仓库中间件所需的知识和基础材料。该 STTR 第一阶段项目旨在推进 DLT（分布式账本技术）和区块链的具体知识和商业化从现代数据仓库中发现的瓶颈开始，为分布式账本技术和区块链的进一步发展创造新颖和创新的环境，形成“良性循环”。这里开发的 DLT 工具改进了区块链协议，特别是用于消除传统硬件上基于正统协议的区块链方法的二次和投机成本，并通过其他创新编程方法（例如概率剪枝）建立线性化和恒定成本。为此，将研究和开发新的统计、拓扑和计算方法，包括开发适用于机器学习、人工智能、千万亿级和百亿亿级计算、先进科学计算以及未来几代教学学术发展的技术。预期结果包括一套新的协议和一个统一的工具，用于将数据类型和多个网络集成到传统数据仓库中，特别是对于努力跟上新的区块链数据、元数据和实时分析步伐的数据仓库的进步。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Power-of-Collaboration: A Sustainable Resilient Ledger Built Democratically

协作的力量：以民主方式构建的可持续弹性账本

• 发表时间: 2022-07
• 期刊: IEEE Data Engineering Bulletin
• 作者: Junchao Chen;Suyash Gupta;Sajjad Rahnama;Mohammad Sadoghi
• 通讯作者: Mohammad Sadoghi