A spatial entropy reflecting distribution of spatial objects

Decision trees are mainly used to classify data and predict data classes. A spatial decision tree has been designed using Euclidean distance between objects for reflecting spatial data characteristic. Even though this method explains the distance of objects in spatial dimension, it fails to represent distributions of spatial data and their relationships. But distributions of spatial data and relationships with their neighborhoods are very important in real world. This paper proposes decision tree based on spatial entropy that represents distributions of spatial data with dispersion and dissimilarity. The rate of dispersion by dissimilarity presents how related distribution of spatial data and nonspatial attributes. The experiment evaluates the accuracy and building time of decision tree as compared to previous methods and it shows that the proposed method makes efficient and scalable classification for spatial decision support.     

Implementation of spatial touch system using time-of-flight camera

Recently developed timeofflight principle based depthsensing video camera technologies provide precise perpixel range data in addition to color video. Such cameras will find application in robotics and visionbased human computer interaction scenarios such as games and gesture input systems. Timeofflight principle range cameras are becoming more and more available. They promise to make the 3D reconstruction of scenes easier, avoiding the practical issues resulting from 3D imaging techniques based on triangulation or disparity estimation. A spatial touch system was presented which uses a depthsensing camera to touch spatial objects and details on its implementation, and how this technology will enable new spatial interactions was speculated.     

A solutiong for digital image management in spatial databases

Digital Orthographic Map (DOM) can be used in various applications because it contains both image features and terrain information. Spatial database management systems aim at the effective and efficient management of data related to a space，engineering design and so on. Thereby spatial database provides an efficient solution for managing DOM. According to large amounts of the DOM data in storage，a data compression based on wavelet is introduced into the storage. Another strategy to solve this problem is to decompose the raw image into tiles and store the tiles individually as separate tuples. The metadata of DOM can be used to organize and manage spatial information， especially for spatial data sharing and fast locating. A tool for browsing， zooming and querying the DOM data is also designed. We implemented these ideas in SISP (Spatial Information Sharing System) and applied te subsystem into te DOM management of Beijing City，which is an component of the Beijing Spatial Information Infrastructure.     

Research on spatial association rules mining in two direction

In data mining from transaction DB, the relationships between the attributes have been focused, but the relationships between the tuples have not been taken into account. In spatial database, there are relationships between the attributes and the tuples, and most of the associations occur between the tuples, such as adjacent, intersection, overlap and other topological relationships. So the tasks of spatial data association rules mining include mining the relationships between attributes of spatial objects, which are called as vertical direction DM, and the relationships between the tuples, which are called as horizontal direction DM. This paper analyzes the storage models of spatial data, uses for reference the technologies of data mining in transaction DB, defines the spatial data association rule, including vertical direction association rule, horizontal direction association rule and twodirection association rule, discusses the measurement of spatial association rule interestingness, and puts forward the work flows of spatial association rule data mining. During twodirection spatial association rules mining, an algorithm is proposed to get nonspatial itemsets. By virtue of spatial analysis, the spatial relations were transferred into nonspatial associations and the nonspatial itemsets were gotten. Based on the nonspatial itemsets, the Apriori algorithm or other algorithms could be used to get the frequent itemsets and then the spatial association rules come into being. Using spatial DB, the spatial association rules were gotten to validate the algorithm, and the test results show that this algorithm is efficient and can mine the interesting spatial rules.     

Domain-oriented data driven data mining:a new understanding for data mining

Recent advances in computing, communications, digital storage technologies, and highthroughput dataacquisition technologies, make it possible to gather and store incredible volumes of data. It creates unprecedented opportunities for largescale knowledge discovery from database. Data mining is an emerging area of computational intelligence that offers new theories, techniques, and tools for processing large volumes of data, such as data analysis, decision making, etc. There are many researchers working on designing efficient data mining techniques, methods, and algorithms. Unfortunately, most data mining researchers pay much attention to technique problems for developing data mining models and methods, while little to basic issues of data mining. In this paper, we will propose a new understanding for data mining, that is, domainoriented datadriven data mining (3DM) model. Some datadriven data mining algorithms developed in our Lab are also presented to show its validity.     

Selecivity estimation using compressed spatial information

Spatial selectivity estimation is one of the essential studies to get query responses rapidly and accurately with the limitation of memory space. Currently, there exist several spatial selectivity estimation techniques such as random sampling, histogram, and parametric. Especially，Cumulative Density Histogram guarantees accurate estimation for rectangle object which has multiple-count problem. However，it requires large memory space because of retaining four sub-histograms for spatial data. Therefore in this paper， we propose a new technique Cumulative Density Wavelet Histogram，called CDWH，which is the combination of Cumulative Density Histogram and Haar Wavelet Transform，a compressed technique. The proposed method simultaneously takes full advantage of their strong points，high accuracy provided by the former and economization of memory space supported by the latter. Consequently，our technique is able to support estimates with relatively low error and retain similar estimates even if memory space is small.     

Mining Hierarchical Decision Rules from Hybrid Data with Categorical and Continuous Valued Attributes

<正>Decision rules mining is an important issue in machine learning and data mining.However,most proposed algorithms mine categorical data at single level,and these rules are not easily understandable and really useful for users.Thus,a new approach to hierarchical decision rules mining is provided in this paper,in which similarity direction measure is introduced to deal with hybrid data.This approach can mine hierarchical decision rules by adjusting similarity measure parameters and the level of concept hierarchy trees.     

A New Method for Out-of-core Applications on Computational Grids

More and more out-of-core problems that involve solving large amounts of data are researched by scientists. The computational grid provides a wide and scalable environment for those large scale computations. A new method supporting out-of-core computations on grids is presented in this paper. The framework and the data storage strategy are described, based on which an easy and efficient out-of-core programming interface is provided for the programmers.     

Research of ERP model system of spatial data warehouse

The broad sharing of spatial information is demanded in the infrastructure construction of spatial data in our country. And the spatial data warehouse realizes the effective management and sharing of spatial information serving as an efficient tool. This article proposes ERP model system that of general-decision-oriented for constructing spatial data !arehouse from the aspect of decision application. In the end of article,the construction process of spatial data warehouse based on ERP model system is discussed.     

A prototype auto-human support system for spatial analysis

Spatial analysis is a multidisciplinary field that involves multiple influential factors, variation and uncertainty, and modeling of geospatial data is a complex procedure affected by spatial context, mechanism and assumptions. In order to make spatial modeling easier, some scholars have suggested a lot of knowledge from exploratory data analysis (EDA), specification of the model, fitness and diagnosis of the model, to interpretation of the model. Also an amount of software has improved some functionalities of spatial analysis, e.g. EDA by the dynamic link (GeoDa) and robust statistical calculation (R). However, there are few programs for spatial analysis that can automatically deal with unstructured declarative issues and uncertainty in machine modeling using the domain knowledge. Under this context, this paper suggests a prototype support system for spatial analysis that can automatically use experience and knowledge from the experts to deal with complexity and uncertainty in modeling. The knowledge base component, as the major contribution of the system, in support of the expert system shell, codes and stores declarative modeling knowledge, e.g. spatial context, mechanisms and prior knowledge to deal with declarative issues during the modeling procedure. With the open architecture, the system integrates functionalities of other components, e.g. GIS’ visualization, DBMS, and robust calculation in an interactive environment. An application case of spatial sampling, design and implementation of spatial modeling with such a system is demonstrated.     

Spatial aggregations for spatial based decision making in spatial data warehouses

For spatial based decision making such as choice of best place to construct a new department store, spatial data warehousing system is required more and more previous spatial data warehousing systems; however, provided decision making of non-spatial data on a map and so those cannot support enough spatial based decision making. The spatial aggregations are proposed for spatial based decision making in spatial data warehouses. The meaning of aggregation operators for applying spatial data was modified and new spatial aggregations were defined. These aggregations can support hierarchical concept of spatial measure. Using these aggregations, the spatial analysis classified by non-spatial data is provided. In case study, how to use these aggregations and how to support spatial based decision making are shown.     

A spatial entropy reflecting distribution of spatial objects

Decision trees are mainly used to classify data and predict data classes. A spatial decision tree has been designed using Euclidean distance between objects for reflecting spatial data characteristic. Even though this method explains the distance of objects in spatial dimension, it fails to represent distributions of spatial data and their relationships. But distributions of spatial data and relationships with their neighborhoods are very important in real world. This paper proposes decision tree based on spatial entropy that represents distributions of spatial data with dispersion and dissimilarity. The rate of dispersion by dissimilarity presents how related distribution of spatial data and non-spatial attributes. The experiment evaluates the accuracy and building time of decision tree as compared to previous methods and it shows that the proposed method makes efficient and scalable classification for spatial decision support.     

一种基于多属性的空间例外挖掘改进算法

空间例外是指与其邻域内其它数据表现不一致或者是偏离观测值以至使人们认为是由不同体制产生的观测点.传统的例外挖掘是根据一个非空间属性值进行例外判断,这种方法容易引起判断失误.在对多个属性进行考虑的基础上,提出了一种基于多属性的空间例外挖掘算法,并与属性加权算法在正确性和有效性方面进行了比较分析.实验证明算法可以有效地发现例外数据.     

Research on spatial association rules mining in two-direction

In data mining from transaction DB, the relationships between the attributes have been focused, but the relationships between the tuples have not been taken into account. In spatial database, there are relationships between the attributes and the tuples, and most of the associations occur between the tuples, such as adjacent, intersection, overlap and other topological relationships. So the tasks of spatial data association rules mining include mining the relationships between attributes of spatial objects, which are called as vertical direction DM, and the relationships between the tuples, which are called as horizontal direction DM. This paper analyzes the storage models of spatial data, uses for reference the technologies of data mining in transaction DB, defines the spatial data association rule, including vertical direction association rule, horizontal direction association rule and two-direction association rule, discusses the measurement of spatial association rule interestingness, and puts forward the work flows of spatial association rule data mining. During two-direction spatial association rules mining, an algorithm is proposed to get non-spatial itemsets. By virtue of spatial analysis, the spatial relations were transferred into non-spatial associations and the non-spatial itemsets were gotten. Based on the non-spatial itemsets, the Apriori algorithm or other algorithms could be used to get the frequent itemsets and then the spatial association rules come into being. Using spatial DB, the spatial association rules were gotten to validate the algorithm, and the test results show that this algorithm is efficient and can mine the interesting spatial rules.     

三维城市模型辅助空间决策支持的研究思路

传统的空间分析方法由于只考虑目标本身的几何特征及相互间存在的简单拓扑关系,缺乏与专业应用模型的有机结合,在辅助空间决策方面远没有发挥应有的作用。随着三维城市模型在城市规划设计管理和城市信息化中的广泛应用,三维城市模型的辅助空间决策支持的研究已经提到议事日程。论述了三维城市模型辅助空间决策支持的典型数学模型,如统计模型、时间序列模型、空间动力学模型等,讨论了3DCM的一些典型应用,比如通视分析、噪声污染、日照分析、电磁波覆盖等。这对进一步推进三维城市模型数据的深层次应用和增值服务具有指导意义。图6,参8。     

基于"双模结构"思想和SVG的异构空间数据转换方法

提出一种基于"双模结构"思想和SVG的异构空间数据转换新方法,并以shape和mif/mid两种格式文件间的相互转换过程为例阐述了新方法的实现过程.和传统异构数据转换方法相比,新方法拥有更高的安全性、更高的自由度和更适合网络环境下操作的优点.新方法对异构空间数据的转换有一定的参考价值.     

R-树聚集在空间数据仓库中的应用

 空间数据库存储空间对象的相关信息,在很多实际应用中需要汇总空间数据,但这种汇总非常耗费时间而且计算代价很高.受非空间数据仓库的启发,可以建立空间数据仓库来加速空间OLAP操作.考虑星型模式且着重于空间维的概念分层,这种分层主要根据空间R-树索引来建立.提出了一个空间查询算法,并考虑了数据更新.     

环境调控空间决策支持系统的设计与实现--福建海岸带环境调控决策支持系统

以福建省海岸带环境调控决策支持系统为例 ,对环境模拟建模、地理信息系统、遥感技术、空间数据仓库与数据挖掘融合而成的环境空间决策支持系统的建设与应用进行了探讨 .首先提出了空间决策支持系统可扩展的体系结构 ,并结合应用需求着重介绍环境空间数据仓库的设计、数据挖掘的过程以及若干环境模拟与评价可扩展模块的实现方法 ,最后给出应用系统的功能框架和系统界面 .