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

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.