Scalable Management of Spatial Data

该项目属于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(如位置、路线、导航)的数量正在急剧增加。在这个项目中，我们的动机是传统的数据管理和处理技术正在变得不足以分析大规模空间数据，并且出现了新的方法。我们的总体目标是研究上述问题，并提出基于分布式处理和因子化数据库的解决方案。目的和目标研究因子化空间数据管理的理论基础。空间数据库是用于存储和查询几何空间中表示对象的数据的优化系统。因此，它们需要在查询语言中集成额外的功能，以便高效地处理空间数据类型。我们将适应因子分解数据库的最新发展，以评估空间数据库的典型查询。空间查询分解将使我们能够避免冗余，从而获得更好的性能和可能渐近更低的复杂度。优化的分布式空间数据分析处理算法。我们的方法论将把并行和分布式方法的算法设计的发展与分解数据库的原则相结合，以降低此类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。开发用于空间数据的集群、分类和模式识别的分布式算法。集群，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检索信息的方法。我们将提出新的最先进的算法来计算空间数据的因式分解表示，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。我们依赖我们的工业合作伙伴地形测量局为我们提供真实世界的空间数据集，并与它们联系起来，以自然的方式将我们的研究成果传播给他们的开发团队。研究方法论的新奇之处因式数据库是计算和表示关系查询结果的新视角。因此，我们的研究方法是新颖的，因为我们将在大数据集上的查询的因式分解表示和计算的范围内探索空间关系查询的特殊特征。这是一个新的研究课题，到目前为止还没有被研究过。与EPSRC的战略保持一致这个项目与EPSRC的数据使能决策的优先事项保持一致。这是因为它的目标是在一个数据越来越丰富的世界里为支持人们做出决策的新方法做出贡献。合作人员这些数据集将由英国首屈一指的地形图制作组织--地形勘测局提供。他们对这项研究感兴趣，我们期待着与他们就管理空间数据的理解的挑战和现有的解决方案进行合作。

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.