SBIR Phase I: A Blockchain-Driven, Distributed Memory, Computational Platform for Industrial Analytics

SBIR 第一阶段：区块链驱动的分布式内存工业分析计算平台

• 批准号: 2112099
• 负责人: Nagi Gebraeel
• 金额: $ 25.58万
• 依托单位: BLOCKALYTICS, 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: 2022-04-01 至 2023-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2112099&HistoricalAwards=false
• 关键词: SBIR; Phase; Blockchain; Driven; Distributed

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to provide a novel, predictive analytics technology for industrial enterprises that require low-latency analytics insights, i.e., companies that cannot afford the kind of delayed insights that are widely present in almost all Cloud-based analytics solutions. Large manufacturing and energy enterprises are comprised of multiple plants sites spread across different geographical locations. These plants contain critical processes and equipment that are monitored using sensors and Internet of Things (IoT) devices. Predictive analytics, a key component in digital transformation, targets the analysis of large volumes of industrial data to generate insights to improve the efficiency of industrial operations, optimize processes, and reduce the life cycle costs of critical equipment and machinery. The prevailing solutions in today’s market rely on using the Cloud to consolidate data, perform analytics, and extract valuable insights. This process creates significant delays for many industrial processes that require immediate insights into their critical operations. The process is also not suitable for companies that have heightened security and privacy protocols (such as nuclear plants and defense manufacturing). The company seeks to enable companies to conduct predictive analytics on geographically distributed data silos without the need to move data from its location, thus reducing decision latency. This SBIR Phase I project proposes to develop a technology stack that leverages the blockchain to train advanced analytic algorithms and Machine Learning models across data silos in different locations without relying on the Cloud or any corporate server. Specifically, the project targets the innovative integration of the blockchain Smart Contracts with distributed memory programming frameworks like the Message Passing Interface (MPI). This integration raises several interesting research challenges one of which requires designing basic primitives similar in functionality to a Map-Reduce framework for the blockchain. The second research component revolves around the development of novel algorithmic decomposition schemes for popular Machine Learning algorithms and artificial intelligence (AI) models to facilitate their training in a decentralized manner using the blockchain. If successful, this technology may provide analytic insights faster than the Cloud. It may also significantly reduce the cost and labor associated with implementing and deploying industrial analytics. Data science teams will be provided with the agility and flexibility to modify and redeploy algorithms without having to rebuild and redesign data pipeline infrastructure to a centralized server.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.

这个小型企业创新研究（SBIR）第一阶段项目的更广泛的影响/商业潜力是为需要低延迟分析见解的工业企业提供一种新颖的预测分析技术，即，无法承受几乎所有基于云的分析解决方案中广泛存在的延迟洞察的公司。大型制造和能源企业由分布在不同地理位置的多个工厂组成。这些工厂包含使用传感器和物联网（IoT）设备进行监控的关键流程和设备。预测分析是数字化转型的关键组成部分，其目标是分析大量工业数据，以生成见解，从而提高工业运营效率，优化流程，并降低关键设备和机械的生命周期成本。当今市场上流行的解决方案依赖于使用云来整合数据、执行分析并提取有价值的见解。这一过程对许多需要立即了解其关键操作的工业过程造成了重大延迟。这个过程也不适合那些加强了安全和隐私协议的公司（如核电站和国防制造业）。该公司旨在使公司能够对地理分布的数据孤岛进行预测分析，而无需将数据从其位置移动，从而减少决策延迟。SBIR第一阶段项目提出开发一个技术堆栈，利用区块链在不同位置的数据孤岛上训练高级分析算法和机器学习模型，而不依赖于云或任何企业服务器。 具体来说，该项目的目标是将区块链智能合约与分布式内存编程框架（如消息传递接口（MPI））进行创新集成。这种集成提出了几个有趣的研究挑战，其中之一需要设计功能类似于区块链Map-Reduce框架的基本基元。第二个研究部分围绕流行的机器学习算法和人工智能（AI）模型的新算法分解方案的开发，以便于使用区块链以分散的方式进行训练。如果成功的话，这项技术可能比云计算更快地提供分析见解。它还可以显著降低与实施和部署工业分析相关的成本和劳动力。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。