Project Grant 2312991

Award Date 7/1/23
Completion Date 6/30/27
Dollars Obligated $1M
Federal Grant Program
47.070
Assistance Type
Project Grant
Place of Performance
New York, NY 10027, USA
Similar Awards
This $600,000 federal Project Grant award was issued by the National Science Foundation (NSF) Division of Information and Intelligent Systems under the Computer and Information Science and Engineering (CFDA 47.070) program. The purpose of the grant is to lay the foundations for building tools that will enable data scientists to more effectively manage and process large datasets for machine learning applications. The key focus is extending relational database technology with the ability to...
This $547,584 National Science Foundation project grant supports research at Arizona State University to redesign analytics databases for machine learning model serving. The goal is to develop methods bridging machine learning inference and relational algebra processing through a unified intermediate representation. This will allow native deep neural network model inferences directly from databases, eliminating cross-system latency in applications like supply chain prediction, fraud detection,...
This National Science Foundation Project Grant of $600,000 supports research to improve the performance of database management systems that use log structured merge tree storage technology. Funded under the Computer and Information Science and Engineering program (CFDA 47.070), the award will support development of novel data organization and flow patterns within log structured merge tree storage to boost read and write capabilities. Key activities include designing algorithms to store...
This $258,780 project grant from the National Science Foundation's Computer and Information Science and Engineering program will fund the development of relational algorithms at the University of Pittsburgh from October 2022 through September 2025. Relational databases are ubiquitous for storing business data, but standard machine learning algorithms cannot directly analyze data across multiple database tables. This project aims to create efficient algorithms that can operate directly on...
This $600,000 federal Project Grant awarded by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CISE) program will fund research at the University of Chicago aimed at improving the integration of machine learning (ML) services into software systems. The project will create a benchmark suite of real-world applications to study software bugs that arise from the tension between general ML-as-a-Service (MLaaS) interfaces and the specific needs of...
This $2,250,000 Project Grant award from the Office of Advanced Cyberinfrastructure within the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program seeks to develop a software platform and infrastructure for training machine learning (ML) models at scale to predict the properties of molecular and materials systems. The key objectives are to: Establish a technological paradigm and software infrastructure for developing ML models capable of predicting...
The National Science Foundation awarded a $599,995 project grant to the University of Massachusetts under the Computer and Information Science and Engineering program (CFDA 47.070) for the period of September 1, 2022 through August 31, 2025. The grant funds research to develop scalable in-database prescriptive analytics capabilities for dynamic environments. Specifically, the university researchers will extend prior work on constrained optimization problems to handle data uncertainty and...
This $300,000 Project Grant was awarded by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program. The grant aims to develop and optimize a log-structured-merge tree-based key-value storage system, called Decoupled-LSM, for use in disaggregated cloud infrastructure environments. The research will address challenges such as network traffic, memory limitations, and transient errors in these environments. The goal is to redesign the...
This Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program, CFDA 47.070, provides $530,704 to Florida International University (FIU) to conduct research on improving tiered memory management in in-memory databases and analytic frameworks using machine learning. The key objectives are to: (1) design new tiered memory management techniques that leverage machine learning to optimize performance, quality of service, and...
This $167,158 Project Grant awarded by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program supports the development of a customizable, privacy-preserving database analytics system compatible with existing SQL databases. The key products to be delivered under this 4-year award include: Automated tools for analyzing a database schema and interactively developing a flexible privacy model to determine which data elements require differential...

This $1,016,318 Project Grant was awarded by the National Science Foundation (NSF) Division of Information and Intelligent Systems under the Computer and Information Science and Engineering (CFDA 47.070) program. The grant supports research and development conducted by Columbia University to design and build two open-source systems, named Marque and Zork, that enable more efficient in-database machine learning.

Marque will be a database management system that supports embedding machine learning primitives like linear algebra operations within the query processing engine. This will allow components of the machine learning pipeline to be formulated as in-database operations, avoiding data export and import. Zork will be a scalable machine learning system that leverages the Marque infrastructure to process large datasets using factorized representations. The project aims to develop new query processing techniques that tightly integrate conventional relational operators with generalized linear algebra operators, enabling optimization across the entire workflow.

Generated 4/2/24, 7:37 AM