基于时空分区聚簇的海量时空数据性能优化方法研究

时空一体化的海量数据管理及相应的时序分析能力是新一代GIS软件体系的重要研究目标之一。当前，基于无缝海量大表的空间及时态空间数据的存取效率亟待提高。为了对海量时空数据进行有效管理和提高时空检索效率，以扩充关系型时空模型为基础，对大型对象一关系型数据库平台所提供的数据分区与聚簇方法进行了时空维的扩展，提出了基于时空分区聚簇（spatio-temporal partition clustering，STPC）的海量时空数据性能优化方法。基于2GB～60GB的单表所进行的检索效率对比测试结果表明，STPC机制较普通的数据组织方式时空检索效率平均提高了10．1％。     

时空数据模型的研究与进展*

时态地理信息系统(TGIS)以表达、管理和分析动态变化的地理现象为目的,其核心是时空数据库,因此对时空数据库的理论基础——时空数据模型的研究十分必要。目前的时空数据模型主要有简单模型、时空联合模型、时空属三域模型、基于对象/特征的模型和基于事件/过程的模型等。首先对这些模型进行了回顾,对其优缺点和侧重点进行了分析对比,然后在此基础上对时空数据模型的进一步研究方向进行了剖析。     

时空聚类分析的普适性方法

时空聚类分析是时空数据挖掘领域近年来研究的热点问题,对于揭示时空要素的发展变化趋势、规律以及本质特征具有重要意义.目前,时空聚类分析的研究仍还初步,缺乏具有普适性的时空聚类分析方法.为此,本文首先建立了一套时空聚类分析的普适性理论方法框架.进而,借助时空统计学、智能计算等工具,提出了一种时空一体化的时空聚类方法.该方法很好地顾及了时空数据的时空耦合、时空相关与时空异质特征,避免了过多人为主观因素的干扰,时空聚类结果具有较好的可靠性.通过采用中国陆地区域42年(1951～1992)年平均气温时空数据进行分析,验证了本文提出的理论与方法的可行性与有效性.     

海战场时空数据建模

目前的海战场信息系统中数据表达是基于时刻的,以反映瞬时的战场信息为主,对历史信息、空间信息处理简单,难以满足复杂应用的需要.综合分析现有时空数据模型的基础上,立足于海战场数据特性,引入时空代数系统并加以扩展,建立了海战场时空数据表达模型.模型的支持下设计的原型系统实现了海战场时空数据存储、查询和时空关系运算的优化.     

时空数据模型研究

当前,相关定位设备及数据库技术的发展使得模拟和查询位置或形状随时间变化的移动物体成为可能。设计时空查询语言是构建移动对象数据库必不可少的步骤。本文在前人研究成果的基础上,提出了时空数据类型以及为了在移动对象数据库中进行时空查询而引入的相关操作符。     

一种基于数据融合和方法融合的时空综合预测算法

时空数据挖掘是数据挖掘中的重要研究内容,其中时空预测的应用领域最为广泛．针对目前时空预测方法中的不足,提出了一种基于数据融合和方法融合的时空综合预测算法．该方法首先采用统计学原理对目标对象本身的时序进行预测;然后通过神经网络解算相邻对象的空间影响,继而对混合数据序列使用时空自回归预测模型;最后使用线性回归将单个的时间预测、空间预测和时空预测有效地融合在一起,得到综合预测结果．应用该方法预测铁路客流,突破了传统铁路客流预测方法的局限,实验结果表明了算法的有效性．     

个性化时空数据隐私保护

智能移动终端的普及导致收集的时空数据中个人位置隐私、签到数据隐私、轨迹隐私等敏感信息容易泄露,且当前研究分别针对上述隐私泄露单独提出保护技术,而没有面向用户给出防止上述隐私泄露的个性化时空数据隐私保护方法。针对这个问题,提出一种面向时空数据的个性化隐私保护模型（p,q,ε）-匿名和基于该模型的个性化时空数据隐私保护（PPP_ST）算法,从而对用户个性化设置的隐私数据（位置隐私、签到数据隐私和轨迹隐私）加以保护。设计了启发式规则对时空数据进行泛化处理,保证了发布数据的可用性并实现了时空数据的高可用性。对比实验中PPP_ST算法的数据可用率比个性化信息数据K-匿名（IDU-K）和个性化Clique Cloak（PCC）算法分别平均高约4.66%和15.45%。同时,设计了泛化位置搜索技术来提高算法的执行效率。基于真实时空数据进行实验测试和分析,实验结果表明PPP_ST算法能有效地保护个性化时空数据隐私。     

基于车牌识别流数据的车辆伴随模式发现方法

针对伴随车辆检测这一新兴的智能交通应用,在一种特殊的流式时空大数据-车牌识别流式大数据下,重新定义Platoon伴随模式,提出PlatoonFinder算法,即时地在车牌识别数据流上挖掘Platoon伴随模式.本文的主要贡献包括：第一,将Platoon伴随模式发现问题映射为数据流上的带有时空约束的频繁序列挖掘问题.与传统频繁序列挖掘算法仅考虑序列元素之间位置关系不同,本文算法能够在频繁序列挖掘的过程中有效处理序列元素之间复杂的时空约束关系;第二,本文算法融入了伪投影等性能优化技术,针对数据流的特点进行了性能优化,能够有效应对车牌识别流式大数据的速率和规模,从而实现车辆Platoon伴随模式的即时发现.通过在真实车牌识别数据集上的实验分析表明,PlatoonFinder算法的平均延时显著低于经典的Aprior和PrefixSpan等频繁模式挖掘算法,也低于真实情况下交通摄像头的车牌识别最小时间间隔.因此,本文所提出的算法可以有效的发现伴随车辆组及其移动模式.     

Classes of Spatio-Temporal Objects and their Closure Properties

We present a data model for spatio-temporal databases. In this model spatio-temporal data is represented as a finite union of objects described by means of a spatial reference object, a temporal object and a geometric transformation function that determines the change or movement of the reference object in time.We define a number of practically relevant classes of spatio-temporal objects, and give complete results concerning closure under Boolean set operators for these classes. Since only few classes are closed under all set operators, we suggest an extension of the model, which leads to better closure properties, and therefore increased practical applicability. We also discuss a normal form for this extended data model.     

基于GA-BP神经网络模型的登革热时空扩散模拟

目的 登革热是一个全球性公共卫生问题,从地理学时空数据分析的视角,探究登革热的时空特质、构建登革热时空过程模型,是有效预防、控制登革热的新方法、研究新热点。方法 基于时空数据挖掘、时空过程建模,综合环境、气象、地理、人口4大因素,分析登革热的空间相关性及登革热病例的空间自相关,挖掘登革热影响因子;针对BP(back propagation)神经网络模型易陷入局部最优的缺陷,引入遗传算法(GA)改进BP神经网络模型,用于登革热时空模拟。结果 登革热的时空扩散与温度、湿度、居民地、交通、人口密度呈显著相关;登革热病例之间呈显著自相关;登革热发生、扩散与环境、气象、地理、人口中的多种因子存在非线性关系;利用改进的GA-BP神经网络模型模拟登革热时空扩散,均方根误差达到0.081。结论 登革热发生、扩散是由多种因素综合影响的结果;GA-BP神经网络模型能够有效模拟登革热时空过程;此模型同样适用于其他伊蚊类传染病的模拟。     

时空推理中自动生成复合表的通用算法

时空推理是面向时间/空间问题的研究领域,在人工智能(如语义Web、机器人导航、自然语言处理、物理过程的定性模拟和常识推理等)和其他领域有着广泛的应用前景.复合推理在时空推理中具有重要作用,是约束满足问题等其他定性推理的基础.复合推理是由R(a,b)和R(b,c)决定R(a,c)的一种演绎推理.一般将关系复合结果放在复合表中备查.但目前复合表的建立需要逐个模型进行手工推导,少数模型给出了独立的复合表生成算法,没有适合多种时空关系模型、能自动生成复合表的通用算法.为此,提出了一种能自动生成复合表的通用算法.首先,给出了基于空间划分的通用时空表示模型.在此基础上,提出了基于场景检测的通用复合表生成算法.通过理论分析和对RCC、宽边界、区间代数等20余种典型时空模型的测试,证明了本算法对于所有以精确区域(或区间)为基础的确定、不确定时空模型均能正确快速地生成复合表.     

基于UML类图的模糊时空数据建模

模糊性广泛存在于时空应用领域,现有的时空数据模型缺乏描述和表达模糊时空对象内在机制和语义关系的能力。通过研究模糊时空数据语义,给出了模糊时空数据模型的形式化定义,在此基础上对UML类图进行扩展,提出一种模糊时空UML数据模型,并用例子说明本文所提模糊时空数据模型的可用性。     

数字农业时空信息管理平台

数字农业中大量时空数据分散在异构系统中,有着不同格式规范、概念术语、数学模型和分析推理方法.采用时空推理、本体论、语义Web和专家系统等技术建立一个数字农业时空信息管理平台,对多源、异构的农业时空数据和推理分析方法进行集中统一的规范化管理.基于该平台构建数字农业应用系统更加方便快捷.     

特征-版本时空数据模型的研究

讨论传统时空数据模型的特点,设计了Geodatabase支持下的基于特征-版本的时空数据模型,并详细介绍该模型的时态逻辑关系和存储结构.该模型节约了数据存储空间,有效保持地理现象的完整性,并具有较高的时空查询效率.针对扎龙湿地,构建了湿地时空数据库,并设计扎龙湿地时态地理信息系统,实现历史重构、地理对象发展与回溯、时空复合查询等功能,有效完成湿地时空数据的管理任务.     

Network and data location aware approach for simultaneous job scheduling and data replication in large-scale data grid environments

Data Grid integrates graphically distributed resources for solving data intensive scientific applications. Effective scheduling in Grid can reduce the amount of data transferred among nodes by submitting a job to a node, where most of the requested data files are available. Scheduling is a traditional problem in parallel and distributed system. However, due to special issues and goals of Grid, traditional approach is not effective in this environment any more. Therefore, it is necessary to propose methods specialized for this kind of parallel and distributed system. Another solution is to use a data replication strategy to create multiple copies of files and store them in convenient locations to shorten file access times. To utilize the above two concepts, in this paper we develop a job scheduling policy, called hierarchical job scheduling strategy （HJSS）, and a dynamic data replication strategy, called advanced dynamic hierarchical replication strategy （ADHRS）, to improve the data access efficiencies in a hierarchical Data Grid. HJSS uses hierarchical scheduling to reduce the search time for an appropriate computing node. It considers network characteristics, number of jobs waiting in queue, file locations, and disk read speed of storage drive at data sources. Moreover, due to the limited storage capacity, a good replica replacement algorithm is needed. We present a novel replacement strategy which deletes files in two steps when free space is not enough for the new replica： first, it deletes those files with minimum time for transferring. Second, if space is still insufficient then it considers the last time the replica was requested, number of access, size of replica and file transfer time. The simulation results show that our proposed algorithm has better performance in comparison with other algorithms in terms of job execution time, number of intercommunications, number of replications, hit ratio, computing resource usage and storage usage.     

Feature selection on probabilistic symbolic objects

In data analysis tasks, we are often confronted to very high dimensional data. Based on the purpose of a data analysis study, feature selection will find and select the relevant subset of features from the original features. Many feature selection algorithms have been proposed in classical data analysis, but very few in symbolic data analysis （SDA） which is an extension of the classical data analysis, since it uses rich objects instead to simple matrices. A symbolic object, compared to the data used in classical data analysis can describe not only individuals, but also most of the time a cluster of individuals. In this paper we present an unsupervised feature selection algorithm on probabilistic symbolic objects （PSOs）, with the purpose of discrimination. A PSO is a symbolic object that describes a cluster of individuals by modal variables using relative frequency distribution associated with each value. This paper presents new dissimilarity measures between PSOs, which are used as feature selection criteria, and explains how to reduce the complexity of the algorithm by using the discrimination matrix.     

Spatio-Temporal Data Types: An Approach to Modeling and Querying Moving Objects in Databases

Spatio-temporal databases deal with geometries changing over time. In general, geometries cannot only change in discrete steps, but continuously, and we are talking about moving objects. If only the position in space of an object is relevant, then moving point is a basic abstraction; if also the extent is of interest, then the moving region abstraction captures moving as well as growing or shrinking regions. We propose a new line of research where moving points and moving regions are viewed as 3-D (2-D space+time) or higher-dimensional entities whose structure and behavior is captured by modeling them as abstract data types. Such types can be integrated as base (attribute) data types into relational, object-oriented, or other DBMS data models; they can be implemented as data blades, cartridges, etc. for extensible DBMSs. We expect these spatio-temporal data types to play a similarly fundamental role for spatio-temporal databases as spatial data types have played for spatial databases. The paper explains the approach and discusses several fundamental issues and questions related to it that need to be clarified before delving into specific designs of spatio- temporal algebras.     

Spatial Data Mining to Support Environmental Management and Decision Making--A Case Study in Brazil

The growth of geo-technologies and the development of methods for spatial data collection have resulted in large spatial data repositories that require techniques for spatial information extraction, in order to transform raw data into useful previously unknown information. However, due to the high complexity of spatial data mining, the need for spatial relationship comprehension and its characteristics, efforts have been directed towards improving algorithms in order to provide an increase of performance and quality of results. Likewise, several issues have been addressed to spatial data mining, including environmental management, which is the focus of this paper. The main original contribution of this work is the demonstration of spatial data mining using a novel algorithm with a multi-relational approach that was applied to a database related to water resource from a certain region of S~o Paulo State, Brazil, and the discussion about obtained results. Some characteristics involving the location of water resources and the profile of who is administering the water exploration were discovered and discussed.     

Continuous Outlier Monitoring on Uncertain Data Streams

Outlier detection on data streams is an important task in data mining. The challenges become even larger when considering uncertain data. This paper studies the problem of outlier detection on uncertain data streams. We propose Continuous Uncertain Outlier Detection （CUOD）, which can quickly determine the nature of the uncertain elements by pruning to improve the efficiency. Furthermore, we propose a pruning approach -- Probability Pruning for Continuous Uncertain Outlier Detection （PCUOD） to reduce the detection cost. It is an estimated outlier probability method which can effectively reduce the amount of calculations. The cost of PCUOD incremental algorithm can satisfy the demand of uncertain data streams. Finally, a new method for parameter variable queries to CUOD is proposed, enabling the concurrent execution of different queries. To the best of our knowledge, this paper is the first work to perform outlier detection on uncertain data streams which can handle parameter variable queries simultaneously. Our methods are verified using both real data and synthetic data. The results show that they are able to reduce the required storage and running time.     

面向交通流量预测的时空超关系图卷积网络

交通流量预测是智能交通系统中的重要研究课题,然而,交通对象（如站点、传感器）之间存在的复杂局部时空关系使得这项研究颇具挑战。尽管以往的一些研究将流量预测问题转化为一个时空图预测问题从而取得了较大的进展,但是它们忽略了交通对象们跨时空维度的直接关联性。目前仍缺乏一种全面建模局部时空关系的方法。针对这一问题,首先提出一种新颖的时空超图建模方案,通过构造一种时空超关系来全面地建模复杂的局部时空关系;然后提出一种时空超关系图卷积网络（STHGCN）预测模型来捕获这些关系用于交通流量预测。在四个公开交通数据集上进行了大量对比实验,结果表明,相比ASTGCN、时空同步图卷积网络（STSGCN）等时空预测模型,STHGCN在均方根误差（RMSE）、平均绝对误差（MAE）、平均绝对百分比误差（MAPE）这三个评价指标上均取得了更优的结果,不同模型运行时间的对比结果也表明,STHGCN有着更高的推理速度。