III: Small: Persistent Data Summaries: Temporal Analytics on Big Data Histories

III：小：持久数据摘要：大数据历史的时态分析

• 批准号: 1816149
• 负责人: Jeff Phillips
• 金额: $ 49.99万
• 依托单位: University of Utah
• 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 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1816149&HistoricalAwards=false
• 关键词: III; Small; Persistent; Data; Summaries

An increasing number of applications require the storage of and access to all historical data to support rich analytics, learning, and mining operations. This project develops a series of methods to summarize data so that it can be queried with respect to not just the full data set, as is standard, but with respect to the state of the data set at any historical time. These summaries integrate with large temporal databases, in both offline batched-processing and online streaming application scenarios. The effectiveness of these methods will be demonstrated on an enormous scientific database of atmospheric data collected for 20 years from over 40,000 weather stations. We will work with industry collaborators to help deploy our new algorithms, and the results will be integrated into education and outreach efforts surrounding the growth of data science initiatives. More specifically, this project extends and combines approximate query processing with temporal big data. In particular, instead of (or on top of) using a multi-version database, this project designs and implements persistent data summaries (PDSs) that offer interactive temporal analytics with strong theoretical guarantees on their approximation quality. In additional to formalizing these models, this project develops practical PDS implementations for sampling-based summaries, data sketches, and core sets that support advanced analytical queries.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.

越来越多的应用程序需要存储和访问所有历史数据，以支持丰富的分析、学习和挖掘操作。该项目开发了一系列汇总数据的方法，以便不仅可以按照标准针对整个数据集进行查询，而且可以针对任何历史时间的数据集的状态进行查询。这些摘要与大型时态数据库集成，在离线批处理和在线流应用场景中都是如此。这些方法的有效性将在一个巨大的科学数据库上得到验证，该数据库收集了40,000多个气象站20年来的大气数据。我们将与行业合作伙伴合作，帮助部署我们的新算法，结果将整合到围绕数据科学倡议增长的教育和推广工作中。更具体地说，该项目扩展并结合了近似查询处理和时态大数据。特别是，该项目不使用(或在其上)使用多版本数据库，而是设计并实现了持久数据摘要(PDS)，该PDS提供交互式时态分析，并为其近似质量提供强有力的理论保证。除了将这些模型正式化外，该项目还为基于采样的摘要、数据草图和支持高级分析查询的核心集开发了实用的PDS实施。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Scalable Spatial Scan Statistics for Trajectories

• DOI: 10.1145/3394046
• 发表时间: 2019-06
• 期刊: ACM Transactions on Knowledge Discovery from Data (TKDD)
• 作者: Michael Matheny;Dong Xie;J. M. Phillips

The GaussianSketch for Almost Relative Error Kernel Distance

• DOI: 10.4230/lipics.approx/random.2020.12
• 发表时间: 2018-11
• 作者: J. M. Phillips;W. Tai

At-the-time and Back-in-time Persistent Sketches

当时和过去的持久草图

• DOI: 10.1145/3448016.3452802
• 发表时间: 2021
• 期刊: Proceedings of the 2021 International Conference on Management of Data (SIGMOD ’21
• 作者: Shi, Benwei;Zhao, Zhuoyue;Peng, Yanqing;Li, Feifei;Phillips, Jeff M.
• 通讯作者: Phillips, Jeff M.

Sketching Multidimensional Time Series for Fast Discord Mining

绘制多维时间序列以进行快速不一致挖掘

• 发表时间: 2023
• 期刊: IEEE International Conference on Big Data
• 作者: Yeh, Chin-Chia Michael;Zheng, Yan;Pan, Menghai;Chen, Huiyuan;Zhuang, Zhongfang;Wang, Junpeng;Wang, Liang;Zhang, Wei;Phillips, Jeff M.;Keogh, Eamonn
• 通讯作者: Keogh, Eamonn

Persistent Bloom Filter: Membership Testing for the Entire History

• DOI: 10.1145/3183713.3183737
• 发表时间: 2018-05
• 期刊: Proceedings of the 2018 International Conference on Management of Data
• 作者: Yanqing Peng;Jinwei Guo;Feifei Li;Weining Qian;Aoying Zhou