MDA-based visual modeling approach for resources link relationships using UML profile

Current XML editors do not provide conceptual modeling for XLink. This leads to inefficient development processes, and a low potential for reuse. To address these shortcomings, this study presents a Model Driven Architecture (MDA) approach with the UML profile to build XLink applications for various domains. This investigation demonstrates how users can use the UML profile to provide a conceptual and visual modeling for XLink applications, and automatically generate different XLink-based documents for various domains. The proposed methodology enables Web-based system developers to generate relationships between resources, and to improve software quality by adopting software engineering techniques in XML development.     

Converting XML DTDs to UML diagrams for conceptual data integration

Extensible Markup Language (XML) is fast becoming the new standard for data representation and exchange on the World Wide Web, e.g., in B2B e-commerce. Modern enterprises need to combine data from many sources in order to answer important business questions, creating a need for integration of web-based XML data. Previous web-based data integration efforts have focused almost exclusively on the logical level of data models, creating a need for techniques that focus on the conceptual level in order to communicate the structure and properties of the available data to users at a higher level of abstraction. The most widely used conceptual model at the moment is the Unified Modeling Language (UML).

This paper presents algorithms for automatically constructing UML diagrams from XML DTDs, enabling fast and easy graphical browsing of XML data sources on the web. The algorithms capture important semantic properties of the XML data such as precise cardinalities and aggregation (containment) relationships between the data elements. As a motivating application, it is shown how the generated diagrams can be used for the conceptual design of data warehouses based on web data, and an integration architecture is presented. The choice of data warehouses and On-Line Analytical Processing as the motivating application is another distinguishing feature of the presented approach.     

Data mining for quality control: Burr detection in the drilling process

Drilling process is one of the most important operations in aeronautic industry. It is performed on the wings of the aeroplanes and its main problem lies with the burr generation. At present moment, there is a visual inspection and manual burr elimination task subsequent to the drilling and previous to the riveting to ensure the quality of the product. These operations increase the cost and the resources required during the process. The article shows the use of data mining techniques to obtain a reliable model to detect the generation of burr during high speed drilling in dry conditions on aluminium Al 7075-T6. It makes possible to eliminate the unproductive operations in order to optimize the process and reduce economic cost. Furthermore, this model should be able to be implemented later in a monitoring system to detect automatically and on-line when the generated burr is out of tolerance limits or not. The article explains the whole process of data analysis from the data preparation to the evaluation and selection of the final model.     

A UML profile for the conceptual modelling of structurally complex data: Easing human effort in the KDD process

ContextDomains where data have a complex structure requiring new approaches for knowledge discovery from data are on the increase. In such domains, the information related to each object under analysis may be composed of a very broad set of interrelated data instead of being represented by a simple attribute table. This further complicates their analysis.ObjectiveIt is becoming more and more necessary to model data before analysis in order to assure that they are properly understood, stored and later processed. On this ground, we have proposed a UML extension that is able to represent any set of structurally complex hierarchically ordered data. Conceptually modelled data are human comprehensible and constitute the starting point for automating other data analysis tasks, such as comparing items or generating reference models.MethodThe proposed notation has been applied to structurally complex data from the stabilometry field. Stabilometry is a medical discipline concerned with human balance. We have organized the model data through an implementation based on XML syntax.ResultsWe have applied data mining techniques to the resulting structured data for knowledge discovery. The sound results of modelling a domain with such complex and wide-ranging data confirm the utility of the approach.ConclusionThe conceptual modelling and the analysis of non-conventional data are important challenges. We have proposed a UML profile that has been tested on data from a medical domain, obtaining very satisfactory results. The notation is useful for understanding domain data and automating knowledge discovery tasks.     

OIM XML 编码研究

元数据联盟提出的开放信息模型是一个无数据描述集，为了便于应用程序的开发和数据仓库的共享和重用，采用ＵＭＬ作为基本的描述模型，使用ＸＭＬ作为元数据的交换标准。文章给出了从ＯＩＭ到ＸＭＬ映射的核心概念，包括类、属性、关联以及类的继承，并对每个描述ＯＩＭ概念的ＵＭＬ图，给出了相应的ＸＭＬ编码。     

基于Multi-agent技术的知识发现新模型KDD的设计

KDD模型是基于双库协同机制的知识发现新模型,是结构化数据挖掘领域研究的一个新的分支。为了进一步提高KDD的智能性,文章设计了一个基于Multi-agent技术的智能数据挖掘系统。利用多智能体技术,实现了数据预处理、数据挖掘、知识的自动获取、基础数据库与知识库的同步进化与协调、知识的评价与表示等功能,为智能信息系统的发展提供了重要支持。     

数据挖掘中数据预处理的研究与实现*

数据预处理将原始的真实数据库转换成适于数据挖掘的挖掘数据库,为挖掘算法更好的实现以及挖掘结果形象的显示打下了良好的基础。针对结构化数据讨论了数据预处理的两个目标：消除现实数据库中的数据缺陷;为数据挖掘做准备。并在此基础上,介绍了数据挖掘软件KDD中数据预处理技术的实现。     

一个基于XML数据仓库系统的设计与实现

黄土高原数据仓库系统以分布式数据仓库为核心,包含多个异质数据源。系统采用中间件技术不仅提供多层次资源查询服务,并且还使用数据挖掘技术和信息检索技术对资源数据进行深加工,能够主动地为用户提供黄土高原生态环境咨询、评测、预测与决策支持服务。论文重点介绍了一种基于XML的统一模式查询语言———XUSQL,用以解决多数据源中多模式数据融合问题。XUSQL使得在数据仓库中的查询与数据源模式无关,把数据源和数据仓库隔离开,从而有利于构造分布式数据仓库,便于异质数据源之间数据融合以及数据源本身的结构调整。     

基于混合优化算法的数据预处理算法Ⅱ

提出了KDD中数据预处理的一种基本算法.针对数据库中的属性,利用非监督学习算法,在获取了面向任务的目标数据子集的基础上,利用混合优化算法进行特征子集的选取.分析了遗传算法和混合遗传算法用于特征子集选择的基本算法,仿真实验说明了混合优化算法的有效性和可行性.     

Development of Secure XML Data Warehouses with QVT

ContextData warehouses are systems which integrate heterogeneous sources to support the decision making process. Data from the Web is becoming increasingly more important as sources for these systems, which has motivated the extensive use of XML to facilitate data and metadata interchange among heterogeneous data sources from the Web and the data warehouse. However, the business information that data warehouses manage is highly sensitive and must, therefore, be carefully protected. Security is thus a key issue in the design of data warehouses, regardless of the implementation technology. It is important to note that the idiosyncrasy of the unstructured and semi-structured data requires particular security rules that have been specifically tailored to these systems in order to permit their particularities to be captured correctly. Unfortunately, although security issues have been considered in the development of traditional data warehouses, current research lacks approaches with which to consider security when the target platform is based on XML technology.ObjectiveWe shall focus on defining transformations to obtain a secure XML Schema from the conceptual multidimensional model of a data warehouse.MethodWe have first defined the rationale behind the transformation rules and how they have been developed in natural language, and we have then established them clearly and formally by using the QVT language. Finally, in order to validate our proposal we have carried out a case study.ResultsWe have proposed an approach for the model driven development of Secure XML Data Warehouses, defining a set of QVT transformation rules.ConclusionThe main benefit of our proposal is that it is possible to model security requirements together with the conceptual model of the data warehouse during the early stages of a project, and automatically obtain the corresponding implementation for XML.     

Effects of the neural network s-Sigmoid function on KDD in the presence of imprecise data

This research explores a specific step in the Knowledge Discovery of Databases (KDD) process, Data Mining. The actual data mining process deals significantly with prediction, estimation, classification, pattern recognition and the development of association rules. Therefore, this analysis depends heavily on the accuracy of the database and on the chosen sample data to be used for model training and testing. Data mining is based upon searching the concatenation of multiple databases that usually contain some amount of missing data along with a variable percentage of inaccurate data, pollution, outliers and noise. The issue of missing data must be addressed as ignoring this problem can introduce bias into the models being evaluated and lead to inaccurate data mining conclusions. The objective of this research is to address the Effects of the Neural Network s-Sigmoid Function on KDD in the Presence of Imprecise Data using a three factor ANOVA test and Tukey's Honestly Significant Difference statistics.     

基于频繁叶模式的XML最大频繁查询模式挖掘算法

在XML频繁查询模式挖掘稠密数据集、长数据集中,为克服项目集挖掘过程中挖掘的项目过多、不利于结果利用等问题,提出基于频繁叶模式的最大频繁查询模式挖掘算法MFRSTMiner。该算法通过构造频繁模式扩展森林,在扩展森林的叶节点中挖掘出最大频繁子树。试验结果表明该算法能够有效地挖掘动态事务集的最大频繁查询模式。     

XML的数据建模及其应用

分析了XML文档在应用中存在的静态和动态特性，提出了静态模型和动态模型的建模准则，并利用UML将XML文档的开发融入软件统一开发过程中。重点讨论如何通过数据建模设计出一个良好的XML文档，并简单介绍了一个XML文档实例。     

模糊数据挖掘

本文在数据库中知识发现（KDD）和数据挖掘（DM）技术的基础上,提出了模糊数据库中知识发现（KDFD）和模糊数据挖掘（FDM）的概念与技术,并给出FDM的算法,它能有效地挖掘出模糊数据库中潜在的有价值的知识。本文具体讨论了模糊关联规则及模糊数据依赖的挖掘。     

基于XML的多维概念模型的研究

对于数据仓库概念模型的研究,目前缺乏统一的标准,不利于模型的交流与共享。基于XML的多维概念模型利用XML这一标准交换语言,不仅很好地解决了这一问题,而且也为元数据的集成与共享打下了基础。针对多维模型的特点,定义了一个特定的DTD,它能够完整地描述多维概念模型的各种语义特征,并针对基于UML的多维概念建模方法,定义了基于XML的多维概念模型与基于UML类图的多维概念模型的映射方法,为其应用奠定了实践基础。     

From visual data exploration to visual data mining: a survey

We survey work on the different uses of graphical mapping and interaction techniques for visual data mining of large data sets represented as table data. Basic terminology related to data mining, data sets, and visualization is introduced. Previous work on information visualization is reviewed in light of different categorizations of techniques and systems. The role of interaction techniques is discussed, in addition to work addressing the question of selecting and evaluating visualization techniques. We review some representative work on the use of information visualization techniques in the context of mining data. This includes both visual data exploration and visually expressing the outcome of specific mining algorithms. We also review recent innovative approaches that attempt to integrate visualization into the DM/KDD process, using it to enhance user interaction and comprehension.     

Knowledge discovery techniques for predicting country investment risk

This paper presents the insights gained from applying knowledge discovery in databases (KDD) processes for the purpose of developing intelligent models, used to classify a country's investing risk based on a variety of factors. Inferential data mining techniques, like C5.0, as well as intelligent learning techniques, like neural networks, were applied to a dataset of 52 countries. The dataset included 27 variables (economic, stock market performance/risk and regulatory efficiencies) on 52 countries, whose investing risk category was assessed in a Wall Street Journal survey of international experts. The results of applying KDD techniques to the dataset are promising, and successfully classified most countries as compared to the experts' classifications. Implementation details, results, and future plans are also presented.     

Tree pattern mining with tree automata constraints

Most work on pattern mining focuses on simple data structures such as itemsets and sequences of itemsets. However, a lot of recent applications dealing with complex data like chemical compounds, protein structures, XML and Web log databases and social networks, require much more sophisticated data structures such as trees and graphs. In these contexts, interesting patterns involve not only frequent object values (labels) appearing in the graphs (or trees) but also frequent specific topologies found in these structures. Recently, several techniques for tree and graph mining have been proposed in the literature. In this paper, we focus on constraint-based tree pattern mining. We propose to use tree automata as a mechanism to specify user constraints over tree patterns. We present the algorithm CoBMiner which allows user constraints specified by a tree automata to be incorporated in the mining process. An extensive set of experiments executed over synthetic and real data (XML documents and Web usage logs) allows us to conclude that incorporating constraints during the mining process is far more effective than filtering the interesting patterns after the mining process.     

Architecture for knowledge discovery and knowledge management

In this paper, we propose I-MIN model for knowledge discovery and knowledge management in evolving databases. The model splits the KDD process into three phases. The schema designed during the first phase, abstracts the generic mining requirements of the KDD process and provides a mapping between the generic KDD process and (user) specific KDD subprocesses. The generic process is executed periodically during the second phase and windows of condensed knowledge called knowledge concentrates are created. During the third phase, which corresponds to actual mining by the end users, specific KDD subprocesses are invoked to mine knowledge concentrates. The model provides a set of mining operators for the development of mining applications to discover and renew, preserve and reuse, and share knowledge for effective knowledge management. These operators can be invoked by either using a declarative query language or by writing applications.The architectural proposal emulates a DBMS like environment for the managers, administrators and end users in the organization. Knowledge management functions, like sharing and reuse of the discovered knowledge among the users and periodic updating of the discovered knowledge are supported. Complete documentation and control of all the KDD endeavors in an organization are facilitated by the I-MIN model. This helps in structuring and streamlining the KDD operations in an organization.