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

基本信息

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

项目摘要

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.

这个小型企业技术转移（STTR）项目的更广泛的影响/商业潜力改善了大规模数据库，数据仓库以及用于商业数据矿山这些资源的软件。在大数据时代，许多应用程序，例如机器学习和人工智能，批判性地依赖于数据功能，包括效率，互操作性和分析。但是，由于多个技术限制，例如集中式存储，均匀的数据格式和紧密耦合的工作流程，他们的数据子系统面临满足这些需求的挑战。这个STTR项目将开发一个新的框架，以克服这些局限性，以改善各种科学领域的大数据应用，例如生物科学，天文学和计算化学。在该阶段项目的结束时，将生产用于开发基于区块链的数据仓库中间件的必要知识和基础材料。该STTR I阶段I项目建议通过创建DLT的特定知识和商业化（分布式LEDGER技术）的特定知识和商业化和商业化，并通过创建新颖的和创新的环境，以进一步开发DLTS和Blockchains，以识别DLT和Blockchains，从而开始使用“ Ottlts”，即“开始”，从而开始了“ Ottlts”，即“开始”，从而开始了“优良周期”。仓库。此处开发的DLT工具推进了区块链协议，专门用于从基于东正教协议的方法中删除二次和投机成本，以使用其他创新的编程方法（例如，概率的修剪）来实现传统硬件的区块链，并通过其他创新的编程方法来实现可线化和恒定的成本。出于这些目的，将研究和开发新的统计，拓扑和计算方法，包括用于开发适用于机器学习，人工智能，PETA规模和EXA级计算，先进的科学计算以及未来后代的教学学术发展的技术。预期的结果包括一组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常规数据仓库中，尤其是为了促进数据仓库的发展，努力与新的区块链数据，元数据和实时分析的努力保持同步，这是NSF的法定任务，并通过评估范围来弥补，该奖项的范围是众所周知的，这是众所周知的。