Scalable Management of Spatial Data

空间数据的可扩展管理

• 批准号: 2115977
• 金额: --
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2115977
• 关键词: Scalable; Management; Spatial; Data

This project falls within the EPSRC Information and Communication Technologies (ICT) theme and theInformation Systems research area.In recent decades, the volume of spatial data (e.g. location, routing, navigation) is increasing dramaticallyacross the world. In this project, we are motivated by the fact that conventional data management andprocessing technologies are becoming inadequate for analyzing large-scale spatial data and there is anemergence of novel approaches. Our overall objective is to investigate the above-mentioned problem andgive solutions based on a crossover of Distributed Processing and Factorized Databases.Aims and ObjectivesInvestigate the theoretical foundations of factorized spatial data management.Spatial databases are optimized systems for storing and querying data that represent objects denedin a geometric space. As a result, they require additional functionality to be intergrated into theirquery languages in order to process spatial data types efficiently.We are going to accommodate recent development on factorized databases to evaluate typical queriesfor spatial databases. Factorizing spatial queries will enable us to avoid redundancy and as a resultachieve better performance and possibly asymptotical lower complexity.Optimized distributed processing algorithms for spatial data analysis.Our methodology will combine the developments on the algorithmic design of parallel and dis-tributed methods with the principles of factorized databases to lower the communication cost insuch distributed algorithms. We are going to use the Massively Parallel Computation (MPC) model,to analyze our results.Develop distributed algorithms for clustering, classication and pattern recognition of spatial data.Clustering, classication and pattern recognition tasks are very common in spatial data analysisbecause they provide a way of generalizing information retrieved from a database.We will propose new state-of-the-art algorithms to compute these tasks on factorized representationsof spatial data and compute them in parallel and distributed way.Distill practical lessons from applying the novel algorithms on real-world spatial datasets.We rely on our industrial partner Ordnance Survey to provide us with real-world spatial datasetsand liaise with them to nd natural ways to disseminate the outcome of our research to theirdevelopment team.Novelty of Research MethodologyFactorized databases are a fresh look at the problem of computing and representing results of relationalqueries. Consequently, our research methology is novel, since we are going to explore the special charac-teristics of spatial relational queries under the scope of the factorized representation and computation ofqueries over large spatial datasets. This is a novel research topic that has not been investigated so far.Alignment to EPSRC's StrategiesThis project aligns to EPSRC's priorities for data enabled decision making. This is because it aims tomake contribution on new methods to support people make decisions in a world that is becoming evermore data rich.CollaboratorsThe datasets will be provided by Ordnance Survey, the premier UK organization that produces topo-graphic maps. They are interested in this research and we expect collaboration with them on under-standing challenges and existing solutions for managing spatial data.

本项目福尔斯属于EPSRC信息和通信技术（ICT）主题和信息系统研究领域。近几十年来，世界各地的空间数据量（如位置、路由、导航）急剧增加。在这个项目中，我们的动机是这样一个事实，即传统的数据管理和处理技术变得不足以分析大规模的空间数据，并出现了新的方法。我们的总体目标是调查上述问题，并给出解决方案的基础上交叉的分布式处理和Factorized Databases.Aims和ObjectivesInvestigating的理论基础factorized空间数据管理。因此，他们需要额外的功能，以集成到他们的查询语言，以处理空间数据类型efficient.We要适应最近的发展因式分解数据库，以评估典型queriesfor空间数据库。空间查询的因式分解将使我们能够避免冗余，从而获得更好的性能和可能的渐近低复杂度.空间数据分析的优化分布式处理算法.我们的方法将联合收割机的并行和分布式方法的算法设计的发展与因式分解数据库的原则相结合，以降低这种分布式算法的通信成本。我们将使用大规模并行计算（MPC）模型来分析我们的结果。开发用于空间数据聚类、分类和模式识别的分布式算法。聚类，分类和模式识别任务在空间数据分析中非常常见，因为它们提供了一种从数据库中检索信息的方法。艺术算法来计算空间数据的因式分解表示的这些任务，并以并行和分布式的方式计算它们。从将新算法应用于现实世界的空间数据集中提取实际经验教训。我们依靠我们的工业合作伙伴Ordos Survey为我们提供真实的-研究方法的新奇因子化数据库是对计算和表示关系查询结果问题的一种新的看法。因此，我们的研究方法是新颖的，因为我们要探索的空间关系查询的特殊性质的范围内的因式分解表示和计算的查询在大型空间数据集。这是一个新的研究课题，目前还没有被调查。与EPSRC的目标保持一致这个项目与EPSRC的数据支持决策的优先事项保持一致。这是因为它的目标是在新的方法上做出贡献，以支持人们在一个数据越来越丰富的世界中做出决策。合作者数据集将由英国首屈一指的地形图制作组织Orlando Survey提供。他们对这项研究很感兴趣，我们希望与他们就管理空间数据的现有挑战和解决方案进行合作。