Collaborative Research: PPoSS: Planning: Efficient and Scalable Learning and Management of Distributed Probabilistic Graphs

协作研究：PPoSS：规划：分布式概率图的高效且可扩展的学习和管理

基本信息

批准号：
2217104
负责人：
Xiang Lian
金额：
$ 10.1万
依托单位：
Kent State University
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：
2022-10-01 至 2024-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2217104&HistoricalAwards=false
关键词：
Collaborative Research PPoSS Planning Efficient

项目摘要

The advancement of cloud-computing infrastructure and machine-learning algorithms have enabled transformative techniques that push the boundaries of various domains, ranging from automated drug design to natural-language understanding. However, understanding the full software/hardware stack remains a grand challenge for domain experts in developing scalable domain-specific machine-learning models, especially when the application data is of inherently non-relational representations. The project’s novelties are to explore, design, and implement an end-to-end system that delivers efficient and effective management of probabilistic graphs, which can serve as a general data abstraction in a variety of domains (e.g., social network, bioinformatics, sensing and communication, to name a few). The probabilistic graph model not only captures complicated correlations among real-world entities but also quantifies the intensities of correlations or influences among them. The project’s impacts are that it addresses important missing pieces from both theory and system practices to support probabilistic graph management in a systematic, inductive, and verifiable way. This planning-grant project investigates an end-to-end probabilistic graph management system that promises efficient probabilistic graph learning, representation, aggregation, and analysis with quality guarantees in a scalable distributed setting. The exploration focuses on the full software/hardware stack of probabilistic-graph management, including designing formal probabilistic-graph definition/manipulation abstractions, and the provable compiling process of inductive constraints with guaranteed correctness and efficiency of pipelining execution in a distributed setting. This computing framework can serve as a general-purpose probabilistic-graph analysis tool that benefits different research domains by discovering and understanding the complex correlations among real-world entities in a more comprehensive and transformative way. Besides this advantage, the outcomes of this project, such as open-source software, publications, and workshop tutorials, could benefit data-management research, decision-making processing in general for the industry (sensing-based automatic operations, e.g., auto-piloting, self-driving), and the government (data-driven policymaking, e.g., public health/global trading monitoring). Furthermore, products from this project can be integrated to enrich the curriculum development of undergraduate/graduate-level courses (with course projects related to cloud computing, data management, and machine learning) and therefore train/benefit a rich body of underrepresented students (including minority/female students) at the investigators' institutions.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.

云计算基础架构和机器学习算法的进步已经实现了变革性技术，从而推动了各个领域的边界，从自动化药物设计到自然语言的理解。但是，了解完整的软件/硬件堆栈对于开发可扩展域特异性机器学习模型的域专家来说，这仍然是一个巨大的挑战，尤其是当应用程序数据固有地具有非相关性表示时。该项目的新颖性是要探索，设计和实施一个端到端系统，该系统可提供对概率图的高效管理，这些系统可以在各种领域（例如，社交网络，生物信息学，敏感性和沟通，仅举几例）。概率图模型不仅捕获了现实世界实体之间的编译相关性，而且还量化了相关性或影响的强度。该项目的影响是，它解决了理论和系统实践中重要的丢失部分，以系统的，归纳和可验证的方式支持有问题的图形管理。该计划授予的项目研究了一个端到端有问题的图形管理系统，该系统承诺在可扩展的分布式设置中使用质量保证的有效的有问题的图形学习，表示，聚合和分析。该探索重点是有问题的编织管理的完整软件/硬件堆栈，包括设计正式的概率编织定义/操纵抽象，以及可证明的归纳约束过程的可证明的编译过程，并保证在分布式设置中提供管道执行的正确性和效率。该计算框架可以用作通用概率 - 图形分析工具，该工具通过以更全面和变革的方式发现和了解现实世界实体之间的复杂相关性来使不同的研究领域受益。除了这一优势之外，该项目的结果，例如开源软件，出版物和研讨会教程，可以使数据管理研究，总体上的决策处理（例如，基于感应的自动操作，例如自动启动，自动驾驶，自我驾驶）以及政府（数据驱动的政策制定）（数据驱动的政策制定，例如公共健康/全球贸易）。此外，该项目的产品可以集成以丰富本科/研究生水平课程的课程发展（其中包括与云计算，数据管理和机器学习有关的课程项目），从而培训/受益/受益于众多代表性不足的学生（包括少数派/女学生）在调查人员的基础上，这是由调查人员的授权来反映的，并且已经反映了nsf的基础，并以此为基础。优点和更广泛的影响审查标准。