III: Small: Durability Queries in Databases

III：小：数据库中的持久性查询

• 批准号: 1814493
• 负责人: Jun Yang
• 金额: $ 49.78万
• 依托单位: Duke University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1814493&HistoricalAwards=false
• 关键词: III; Small; Durability; Queries; Databases

Real data tends to be noisy and often exhibits transient patterns. When using data to make decisions, one must avoid jumping to conclusions based on one particular query result. In practice, most datasets are temporal, and the abundance of historical data will continue to grow as our economic, social, and scientific activities are increasingly captured in digitized forms. Therefore, a useful way of assessing the "robustness" of a query result is to consider its durability, or how long it has remained valid. There is an abundance of real-life examples where the notion of durability can help make more meaningful decisions from data, reveal transient patterns in data, or search for interesting patterns in data that are also robust. This proposal studies the notion of durability for database queries, techniques for efficiently evaluating durability queries, and their practical applications.This project will develop a framework for durability queries, which unifies common variants and introduces novel ones. Instead of tackling each variant separately, the project identifies underlying properties of durability queries that lead to fundamental understanding and reusable algorithmic techniques. The project will take principled approaches to designing fast, scalable algorithms and indexing methods for durability query processing. These approaches combine geometric, statistical, and approximation techniques for a meaningful balance between query efficiency and result quality. Going beyond worst-case analysis, the project researches query and indexing methods as well as complexity analysis methods aimed at non-adversarial cases that arise in practice.In addition, the project brings efficient support for durability queries to an open-source database system. The project will apply durability queries to three real-world domains with public-facing websites, as a practical test bed for the effectiveness of durability-based analysis and the efficiency of durability query processing techniques.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.

真实的数据往往是有噪声的，并且经常表现出瞬态模式。当使用数据进行决策时，必须避免根据一个特定的查询结果得出结论。在实践中，大多数数据集都是时间性的，随着我们的经济、社会和科学活动越来越多地以数字化形式被捕获，历史数据的丰富程度将继续增长。因此，评估查询结果的“健壮性”的一个有用的方法是考虑其持久性，或者它保持有效的时间。有大量的现实例子表明，持久性的概念可以帮助从数据中做出更有意义的决策，揭示数据中的瞬态模式，或者在数据中搜索有趣的模式。本项目研究数据库查询持久性的概念、有效评估持久性查询的技术及其实际应用。本项目将开发一个持久性查询框架，该框架将统一常见的变体并引入新的变体。该项目没有单独解决每个变体，而是识别耐久性查询的底层属性，从而获得基本理解和可重复使用的算法技术。该项目将采取原则性的方法来设计快速，可扩展的算法和持久性查询处理的索引方法。这些方法结合了联合收割机几何、统计和近似技术，在查询效率和结果质量之间实现了有意义的平衡。在最坏情况分析的基础上，针对实际中出现的非对抗性情况，研究了查询和索引方法以及复杂性分析方法，并为开源数据库系统带来了对持久性查询的有效支持。该项目将耐久性查询应用于三个面向公众的网站的现实世界域，作为基于耐久性的分析的有效性和耐久性查询处理技术的效率的实际测试台。该奖项反映了NSF的法定使命，并被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

项目成果

Efficient Indexes for Diverse Top-k Range Queries

• DOI: 10.1145/3375395.3387667
• 发表时间: 2020-05
• 期刊: Proceedings of the 39th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems
• 作者: P. Agarwal;Stavros Sintos;Alex Steiger

Selecting Data to Clean for Fact Checking: Minimizing Uncertainty vs. Maximizing Surprise

选择要清理的数据以进行事实检查：最小化不确定性与最大化惊喜

• DOI: 10.14778/3358701.3358708
• 发表时间: 2019
• 期刊: Proceedings of the VLDB Endowment
• 影响因子: 2.5
• 作者: Sintos, Stavros;Agarwal, Pankaj K.;Yang, Jun
• 通讯作者: Yang, Jun

Dynamic Enumeration of Similarity Joins

• DOI: 10.4230/lipics.icalp.2021.11
• 发表时间: 2021-05
• 作者: P. Agarwal;Xiao Hu;Stavros Sintos;Jun Yang

Durable Top-K Instant-Stamped Temporal Records with User-Specified Scoring Functions

• DOI: 10.1109/icde51399.2021.00068
• 发表时间: 2021-02
• 期刊: 2021 IEEE 37th International Conference on Data Engineering (ICDE)
• 作者: Junyang Gao;Stavros Sintos;P. Agarwal;Jun Yang

Efficiently Answering Durability Prediction Queries

高效回答耐久性预测查询

• DOI: 10.1145/3448016.3457305
• 发表时间: 2021
• 期刊: Proceedings of the 2021 ACM SIGMOD International Conference on Management of Data
• 作者: Gao, Junyang;Xu, Yifan;Agarwal, Pankaj K.;Yang, Jun
• 通讯作者: Yang, Jun