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NSF-BSF: III: Small: Data Driven Schema

NSF-BSF：III：小型：数据驱动模式

基本信息

批准号：
2109922
负责人：
Dan Suciu
金额：
$ 50万
依托单位：
University of Washington
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-01 至 2024-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2109922&HistoricalAwards=false
关键词：
NSF BSF III Small Data

项目摘要

Data needs to be organized systematically and rigorously. Data about consumer products goes into one table, and data about micro-organisms goes into a different table. This makes it easier for humans and computers to store the data, to retrieve and query it, and to update it. But today one often finds large amounts of noisy, inconsistent, incomplete data, which are impossible to organize rigorously. The sheer volume of this data makes it very valuable, yet it is of limited utility without proper organization. This project develops methods for organizing noisy, inconsistent, incomplete data, and develops techniques for storing, querying, and updating such data. Its findings will inform organizations on how to organize and use large amounts of noisy data.This project develops a technique for approximate schema discovery for noisy data, for normalizing the data according to this schema, and for improving query processing. The input consists of a single, large relation, which may be noisy, inconsistent, incomplete, and the system discovers automatically a few candidate schemas that can represent the data with minimal loss and with high utility for downstream tasks. Each schema is associated with an information-theoretic score, which represents the amount of information that may be lost when we represent the data according to that schema. Then, the project researches new approaches for querying the data stored in an approximate schema, by either recording explicitly the number of "spurious tuples" generated by the schema, or by using probabilities to quantify the degree of confidence in the query's answer. The techniques explored in this project combine information theory with graph algorithms and with query optimization.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.

数据需要系统而严谨地组织起来。关于消费品的数据放在一张表中，关于微生物的数据放在另一张表中。这使得人类和计算机更容易存储数据、检索和查询数据以及更新数据。但今天，人们经常发现大量嘈杂、不一致、不完整的数据，这些数据不可能严格地组织起来。这些数据的绝对数量使其非常有价值，但如果没有适当的组织，它的效用有限。该项目开发了组织嘈杂、不一致、不完整数据的方法，并开发了存储、查询和更新这些数据的技术。其研究结果将为组织提供如何组织和使用大量嘈杂数据的信息。该项目开发了一种技术，用于对噪声数据进行近似模式发现，根据该模式对数据进行规范化，并改进查询处理。输入由单个大关系组成，该关系可能是嘈杂的、不一致的、不完整的，系统会自动发现一些候选模式，这些模式可以以最小的损失表示数据，并且对下游任务具有很高的效用。每个模式都与一个信息理论分数相关联，该分数表示根据该模式表示数据时可能丢失的信息量。然后，该项目研究了查询存储在近似模式中的数据的新方法，通过显式记录模式生成的“伪元组”的数量，或者通过使用概率来量化查询答案的置信度。本项目探索的技术将信息论与图算法和查询优化相结合。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。