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QUINTON -- QUerying and INTegrating Over Nested data

QUINTON——嵌套数据的查询和集成

基本信息

批准号：
EP/T022124/1
负责人：
Michael Benedikt
金额：
$ 132.49万
依托单位：
University of Oxford
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2021
资助国家：
英国
起止时间：
2021 至无数据
项目状态：
未结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FT022124%2F1
关键词：
QUINTON QUerying INTegrating Over Nested

项目摘要

It has long been recognized that nested data models -- in which information is modelled as collections of tuples whose attributes may in turn take values that are collections -- are the most natural modelling formalism for a wide variety of information management scenarios. Query languages that support nested data have been developed decades ago. But even as emerging applications have made the need for querying of nested data more crucial, and even as many of the most important big data management frameworks assume programmatic interfaces based on nested data, processing large-scale nested data remains extremely cumbersome, radically more so than in the case of flat data. Our research hypothesis is that fundamental problems in querying and integrating nested data need to be resolved for this situation to change.This project will provide new foundations for both querying and integration nested data. On the side of querying we will establish a standard processing pipeline for queries over nested data. This will include a foundational study of the basic transformations involved in any such pipeline, such as the "shredding" of nested queries into relational queries. It will also include the development of algorithms and tools that implement this pipeline, working on top of scalable infrastructure for flat data, such as the Apache Spark project. On the side of integration, we will establish the foundations of specifying and querying virtual data sources consisting of nested data, and develop middleware that can implement queries over virtual data on top of heterogenous nested data sources.The impact of QUINTON is both practical and foundational. We will build infrastructure for querying and integration, but we also investigate the fundamental problems of scalable querying over materialized and virtual datasources, providing the foundations that can guide the research community in future implementations. We will also drill down into a particular compelling and timely application of nested data integration and management, working with an industrial partner to build components and novel analyses in the area of management for biomedical data. Our partner deals with unified interfaces to diverse biomedical datasources -- clinical, imaging, and genomic data -- and their usecases are a perfect fit for the technology we are developing.

人们早就认识到，嵌套数据模型--其中信息被建模为元组的集合，元组的属性反过来可以取集合的值--是各种信息管理方案的最自然的建模形式主义。支持嵌套数据的查询语言在几十年前就已经开发出来了。但是，即使新兴的应用程序使得查询嵌套数据的需求变得更加重要，即使许多最重要的大数据管理框架都采用了基于嵌套数据的编程接口，处理大规模嵌套数据仍然非常繁琐，比平面数据的情况更加复杂。我们的研究假设是，要改变这种状况，需要解决嵌套数据查询和集成的基本问题，本项目将为嵌套数据查询和集成提供新的基础。在查询方面，我们将为嵌套数据上的查询建立一个标准的处理管道。这将包括对任何此类管道中涉及的基本转换的基础研究，例如将嵌套查询“分解”为关系查询。它还将包括实现该管道的算法和工具的开发，在平面数据的可扩展基础设施之上工作，例如Apache Spark项目。在集成方面，我们将建立由嵌套数据组成的虚拟数据源的定义和查询的基础，并开发能够在异构嵌套数据源上实现虚拟数据查询的中间件。QUINTON的影响是实际的和基础的。我们将建立查询和集成的基础设施，但我们也调查了可扩展查询的基本问题，在物化和虚拟存储器，提供的基础，可以指导研究界在未来的实现。我们还将深入研究嵌套数据集成和管理的一个特别引人注目和及时的应用程序，与工业合作伙伴合作，在生物医学数据管理领域构建组件和新颖的分析。我们的合作伙伴处理各种生物医学数据的统一接口-临床，成像和基因组数据-他们的用例非常适合我们正在开发的技术。