空间流数据的多维度、多层次在线回归分析及趋势预测研究摘要.doc

学 位 论 文题目 空间流数据的多维度、多层次在线回归分析及趋势预测研究摘 要数据仓库及数据挖掘技术与3S技术相结合，导致了空间数据仓库及空间数据挖掘技术的产生与发展。空间数据仓库能集成、存储来自不同数据源包括不同数据类型的空间数据，能从多角度、多层面进行分析、展示这些空间数据，为空间信息共享、空间分析决策提供支持。空间数据挖掘技术能在空间数据仓库中挖掘与空间相关的隐含知识，为空间数据理解、空间分析决策提供可能。在空间数据仓库中进行空间数据挖掘，成为了当前数据仓库和信息决策领域的前沿研究和热点。由于空间数据往往与时间紧密相关，是一些连续的、动态增长变化着的流数据，空间流数据描述了不同空间位置的动态环境中的连续变化行为或状态，它不仅涉及数据的时态特征，还涉及数据的空间特征。对空间流数据进行回归分析，反映自然界不同时空尺度下的动态数据信息变化趋势，可以预测未来的时空状态，这对于气象预测、环境监测、市场规划、城市规划及经济发展等具有重大意义。本文对空间流数据进行了多方面的研究：首先使用R*树对空间对象进行了有效的空间索引，根据R*树和用户指定的查询窗口检索该窗口内的所有空间对象；其次对检索到的空间对象的流数据进行多维度、多层次的在线回归分析，从而对空间流数据进行趋势预测，并根据相关的抽象概念层次对检索到的空间对象进行自动的聚合操作，为用户提供更多实际的信息。实验表明，本文对空间流数据的多维度、多层次的在线回归分析及趋势预测是行之有效的。本文的创新点在于：第一，将多维度、多层次的在线回归分析思想引入到空间流数据中，在空间流数据中进行有效的在线回归分析及趋势预测；第二，引入倾斜时间的技术，收集足够的（而非完全的）空间流数据，节省大量的存储空间供回归分析、计算使用；同时ISB表示法为回归立方体的多度量计算提供很好的支持；第三，对空间数据立方体进行技术扩展，使其支持多个度量的同时计算，并存储较少的分析层次，即数据立方体中只需包含m-层（最小兴趣层）及o-层（观察层），为空间流数据的分析提供了更充足的存储空间。关键字：空间数据仓库，空间数据挖掘，空间流数据，空间索引，R*-树，回归分析，线性回归，空间回归，回归数据立方体。AbstractThe Integration of the technology of data warehouse and data mining leads to the generation and development of spatial data warehouse and spatial data mining. Spatial data warehouse integrates and stores spatial data that comes from different data sources and that is of different data types, which makes it possible to analyze and show such spatial data from multi-angle and from multi-level so that spatial data warehouse can support information sharing and decision. Spatial data mining can find hiding knowledge related to space from spatial data warehouse, which provides support to spatial data comprehension and spatial decision. Spatial data mining in certain spatial data warehouse has become so animated in the field of data warehouse and information decision. Usually, spatial data is relevant to time so closely. It is generated continuously in a dynamic environment, with huge volume, infinite flow, and fast changing behavior. Spatial data streams describe the continuous behavior or status in various positions and dynamic environments. Spatial data streams associated to the characters of time series and spatial ones as well. By regression analyzing spatial stream data, we can forecast the future time and spatial status, which is important to weather forecasting, environment supervising, market planning, city planning and economy development, and e.t.In this paper, we make some research to three aspects. Firstly, we index the spatial objects accessed by a given query window using some effective and efficient methods. Secondly, we do some online regression analysis to the stream data of the obtained spatial objects so that we can forecast the future changing tendency. At the same time, aggregations should be conducted automatically according to relevant abstract concept hierarchy tree in order to provide users with more practical and useful information. Experiment indicates that our algorithms of regression analysis to spatial stream data are effective and efficient.Contributions of this paper lie to three aspects: (1) regression analysis is introduced into spatial stream data; (2) tilt time frame is adopted in data collection and the ISB expression of spatial data streams provides many advantages to the computation of regression data cubes; (3) we develop the technology of data cube to make it support multi-measure computation and store less analysis levels, that is only the minimal interest layer and the observation layer are memorized so that we have enough storage space to analyze and compute the spatial data streams.KEY