改进的XML智能数据清洗策略

摘要：针对XML数据的质量问题,以XML键为基础,借助多模板隐马尔可夫模型信息抽取策略与粒子群优化算法构建新的XML数据清洗方法。为了提高XML相似性数据并行检测效率,利用波函数对粒子群优化算法进行优化。仿真实验表明,与其他XML数据清洗算法相比,该方法的白适应学习能力强、人工参与程度低、计算量小,时间性能有94％左右的提升。     

面向XML数据库的智能数据清洗策略

针对XML数据质量问题,通过引入贝叶斯学习方法与马尔可夫链概率转移策略建立XML数据清洗过程的元数据模型,根据综合清洗结构化数据中相似重复记录的思想,提出一种智能清洗XML数据的新方法。实验表明,与其他方法比较,该方法不仅自动化程度较高,降低人工参与的程度,而且精确率和查全率提升了2%~5%。     

基于改进粒子群算法的隐马尔可夫模型训练

针对隐马尔可夫模型传统训练算法易收敛于局部极值的问题,提出一种带极值扰动的自适应调整惯性权重和加速系数的粒子群算法,将改进后的粒子群优化算法引入到隐马尔可夫模型的训练中,分别对隐马尔可夫模型的状态数与参数进优化.通过对手写数字识别的实验说明,提出的基于改进粒子群优化算法的隐马尔可夫模型训练算法与传统隐马尔可夫模型训练算法Baum-Welch算法相比,能有效地跳出局部极值,从而使训练后的隐马尔可夫模型具有较高的识别能力.     

融合粒子群与蚁群算法优化XML群体智能搜索

随着Web技术及其应用的快速发展,XML已经成为互联网上信息表示和数据交换的一个重要标准,其作用已深入到网络社区的每个角落;针对XML文档进行群体搜索的特点与不足,提出利用群智能算法的概率变换规则对其进行改进,首先采用路径离散化规则,结合XML半结构化的特点及概率知识,再融合粒子群算法与蚁群算法进行动态群体搜索,而群体自适应杂交、多次编码、迭代选择等不仅可以提高数据搜索的范围、精度和收敛的效率,而且可以避免早熟,降低算法的复杂度.仿真实验表明这种融合方法具有更好的查询效果.     

多策略融合的改进粒子群优化算法

为解决传统粒子群算法收敛精度低、收敛速度慢和易陷入局部最优的问题,提出了一种多策略融合的改进粒子群算法。首先,设计了一种基于中垂线算法的游离粒子位置更新方法,加快了游离粒子的收敛速度;其次,设计了一种在最优粒子附近生成爆炸粒子的策略,以增强算法的寻优精度和寻优速度,为适应前两个策略,还设计了一种仅依靠全局最优粒子位置的粒子速度更新策略;最后,将基于概率分层的简化粒子群优化算法的惯性权重和粒子位置更新方法用于本算法。与其他五种改进粒子群算法进行了对比实验,结果表明提出的改进算法无论是处理低维问题还是高维问题表现均具有较大优势,性能更优越。     

蚁群算法与粒子群算法的融合策略

蚁群算法和粒子群算法是群智能算法的两种主要算法,本文介绍了两种算法的原理,总结出两种算法的优缺点,并针对这两种算法的不足,着重分析了两种算法的混合策略以提高算法性能。     

融合多种策略的改进粒子群算法

为有效解决粒子群优化算法(Particle Swarm Optimization, PSO)容易陷入局部极值及进化后期收敛速度慢、精度低等缺点, 提出了一种融合多种策略的改进粒子群算法(Improved Particle Swarm Optimization, IPSO). 该算法包括以下4点改进:(1)采取分组控制策...     

基于改进粒子群算法的P2P流媒体数据调度策略

根据P2P流媒体数据调度特点,改进了粒子群优化算法,并提出适用于离散粒子群算法（MDPSOA）的数字串编码方式。调度策略引入资源紧急度以及资源稀缺度来选择调度数据片,然后用改进离散粒子群算法来进行节点寻优,找出最优调度节点集。最后通过实验仿真算法收敛性、数据调度时间、网络利用带宽和节点负载平衡,从而验证调度策略的可行性和有效性。     

改进的粒子群算法

针对基本粒子群算法容易陷入局部最优点,进化后期速度慢等缺点,设计了一种新的粒子群算法,将基本粒子群算法粒子行为基于个体极值点转化为个体自身极值与其他某一个个体极值的加权平均值,而全局极值点转化为群体中优秀个体极值的加权平均值。数值仿真实验表明,新算法比PSO具有更好的收敛性,能更快地找到问题的最优解。     

改进的混合粒子群算法

从研究分析粒子群算法和郭涛算法的特点出发,提出一种综合两算法优点的混合算法。新算法改变了粒子的更新方式,以子空间搜索和串行搜索相结合的多点并行搜索,扩大了算法的搜索范围,减少了粒子对初值的依赖,增强了算法跳出局部最优的能力;通过后代较优个体变异产生子群,提高了算法局部寻优能力;实验证明,该算法正确高效。     

抽取XML文档到关系数据库

XML在关系数据库中的存储问题是XML研究领域中的一个重要问题。在总结多种映射方法的基础上,提出了一种方法将多个相似的XML文档进行解析,根据映射关系,生成各自的关系模式,并分析归纳出一个集成的关系模式,然后创建一个关系数据库,并在映射关系的基础上提取并存储XML文档数据到关系数据库。此方法以较为简洁的结构保存了XML文档的数据信息,其最大的特点就是不用考虑文档的模式信息（DTD,XML Schema）。并通过一个具体的实验结果来说明这种方法的有效性。     

利用关系数据库实现无模式XML数据管理平台

基于目前无模式XML数据被广泛应用的现状,研究了如何利用关系数据库对无模式XML数据进行统一管理,设计并验证了一种基于关系数据库的统一无模式XML数据管理平台。分析了什么是无模式XML数据,介绍了管理平台的总体结构,详细叙述了管理平台的设计思想和实现技术,结合实际的应用项目验证了无模式XML数据管理平台。     

XML data update management in XML-enabled database

With the increasing demand for a proper and efficient XML data storage, XML-Enabled Database (XEnDB) has emerged as one of the popular solutions. It claims to combine the pros and limit the cons of the traditional Database Management Systems (DBMS) and Native XML Database (NXD). In this paper, we focus on XML data update management in XEnDB. Our aim is to preserve the conceptual semantic constraints and to avoid inconsistencies in XML data during update operations. In this current era when XML data interchange mostly occurs in a commercial setting, it is highly critical that data exchanged be correct at all times, and hence data integrity in XML data is paramount. To achieve our goal, we firstly classify different constraints in XML documents. Secondly, we transform these constraints into XML Schema with embedded SQL annotations. Thirdly, we propose a generic update methodology that utilizes the proposed schema. We then implement the method in one of the current XEnDB products. Since XEnDB has a Relational Model as the underlying data model, our update method uses the SQL/XML as a standard language. Finally, we also analyze the processing performance.     

Cooperative strategy for web data mining and cleaning

While the Internet and World Wide Web have put a huge volume of low-quality information at the easy access of an information gathering system, filtering out irrelevant information has become a big challenge. In this paper, a Web data mining and cleaning strategy for information gathering is proposed. A data-mining model is presented for the data that come from multiple agents. Using the model, a data-cleaning algorithm is then presented to eliminate irrelevant data. To evaluate the data-cleaning strategy, an interpretation is given for the mining model according to evidence theory. An experiment is also conducted to evaluate the strategy using Web data. The experimental results have shown that the proposed strategy is efficient and promising.     

An adaptive path index for XML data using the query workload

Due to its flexibility, XML is becoming the de facto standard for exchanging and querying documents over the Web. Many XML query languages such as XQuery and XPath use label paths to traverse the irregularly structured XML data. Without a structural summary and efficient indexes, query processing can be quite inefficient due to an exhaustive traversal on XML data. To overcome the inefficiency, several path indexes have been proposed in the research community. Traditional indexes generally record all label paths from the root element in XML data and are constructed with the use of data only. Such path indexes may result in performance degradation due to large sizes and exhaustive navigations for partial matching path queries which start with the self-or-descendent axis(“//”). To improve the query performance, we propose an adaptive path index for XML data (termed APEX). APEX does not keep all paths starting from the root and utilizes frequently used paths on query workloads. APEX also has a nice property that it can be updated incrementally according to the changes of query workloads. Experimental results with synthetic and real-life data sets clearly confirm that APEX improves the query processing cost typically 2–69 times compared with the traditional indexes, with the performance gap increasing with the irregularity of XML data.     

用于数据交换的XML文档和关系数据库转换

XML以其结构性、标签性、可扩展性和可移植性成为网络数据交换方面的标准技术,广泛地应用于计算机软件,尤其是电子商务应用领域。但目前数据存储和管理的主流手段仍是成熟而稳健的数据库。这就决定了用于数据交换的XML文档与数据库之间必须频繁的进行转换。在介绍了XML带来的“用数据库存储、XML文档传输”的全新工作方式之后,提出了一种利用良好数据结构和递归算法提取XML文档节点,进而把XML文档转换为关系数据库的方法。最后介绍了一个转换平台,验证了算法的可行性。     

An XML query engine for network-bound data

XML has become the lingua franca for data exchange and integration across administrative and enterprise boundaries. Nearly all data providers are adding XML import or export capabilities, and standard XML Schemas and DTDs are being promoted for all types of data sharing. The ubiquity of XML has removed one of the major obstacles to integrating data from widely disparate sources - namely, the heterogeneity of data formats. However, general-purpose integration of data across the wide are a also requires a query processor that can query data sources on demand, receive streamed XML data from them, and combine and restructure the data into new XML output - while providing good performance for both batch-oriented and ad hoc, interactive queries. This is the goal of the Tukwila data integration system, the first system that focuses on network-bound, dynamic XML data sources. In contrast to previous approaches, which must read, parse, and often store entire XML objects before querying them, Tukwila can return query results even as the data is streaming into the system. Tukwila is built with a new system architecture that extends adaptive query processing and relational-engine techniques into the XML realm, as facilitated by a pair of operators that incrementally evaluate a query's input path expressions as data is read. In this paper, we describe the Tukwila architecture and its novel aspects, and we experimentally demonstrate that Tukwila provides better overall query performance and faster initial answers than existing systems, and has excellent scalability.     

基于关系数据库的实时XML数据查询处理

力图用成熟的关系数据库查询机制处理符合DTD的XML文档。提出了一整套数据模型、转换规则和算法描述,可以将XML文档转换为关系元组,从而达到用XML实现基于关系数据库的实时数据一致性查询处理的目的。     

XTRON: An XML data management system using relational databases

Recently, there has been plenty of interest in XML. Since the amount of data in XML format has rapidly increased, the need for effective storage and retrieval of XML data has arisen. Many database researchers and vendors have proposed various techniques and tools for XML data storage and retrieval in recent years. In this paper, we present an XML data management system using a relational database as a repository. Our XML management system stores XML data in a schema independent manner, and translates a comprehensive subset of XQuery expressions into a single SQL statement. Also, our system does not modify the relational engine. In this paper, we also present the experimental results in order to demonstrate the efficiency and scalability of our system compared with well-known XML processing systems.     

基于XML的时态数据库的隐私数据访问控制方案

为了保护时态隐私数据的隐私性,提出了一种对其安全访问控制方案.根据时态隐私数据的特点,在RBAC96模型的基础上,建立了一种基于角色的时态隐私数据访问控制模型(TPD-RBAC模型).依据该模型设计相应的隐私策略,然后将其应用于基于XML的时态数据库中,并从文件级别和元素级别对隐私数据的访问进行运级控制,进一步地满足隐私对象的保护需求.最后以电子病历系统为例讨论了该方案可行性.