Fuzzy XML Data Management

A large number of data appears in various real-world application domains, and how to manage the data is particularly important. Databases are created to operate large quantities of data by inputting, storing, retrieving, and managing that data. Over the years, various database models, including conceptual data models (e.g., entity-relationship (ER) model, enhanced entity-relationship (EER) model, and UML data model) and logical database models (e.g., relational database model and object-oriented database model), are developed for information modeling and data management. Moreover, with the popularity of Web-based applications, the requirement for data management has been put on the exchange and share of data over the Web. The eXtensiable Markup Language (XML) provides a Web friendly and well-understood syntax for the exchange of data and impacts on data definition and share on Web. This is creating a new set of data management requirements involving XML. Currently, databases and XML play important roles for data management and have become the main means to realize the data management. In this chapter, databases and XML techniques for data management will be introduced.

Information imprecision and uncertainty exist in many real-world applications, and for this reason fuzzy data modeling has been extensively investigated in various database models. In particular, Zadeh’s fuzzy set theory has been identified as a successful technique for modeling imprecise and uncertain information in various database models. This has resulted in numerous contributions, mainly with respect to the popular fuzzy conceptual data models (fuzzy ER/EER model, fuzzy UML data model, and etc.) and fuzzy logical database models (fuzzy relational database model, fuzzy object-oriented database model, and etc.). Also, it is shown that fuzzy set theory is very useful in Web-based business intelligence. Therefore, topics related to the modeling of fuzzy data are considered very interesting in XML since it is the current standard data representation and exchange format over the Web. In particular, to manage fuzzy XML data, it is necessary to integrate fuzzy XML and various fuzzy databases, and various fuzzy database models (fuzzy relational database model and fuzzy object-oriented database model) need to be used for mapping to and from the fuzzy XML models. Therefore, in this chapter, we mainly introduce several fuzzy database models, including fuzzy UML data model, fuzzy relational database model, and fuzzy object-oriented database model. Before that, we briefly introduce some notions of fuzzy set theory.

Information is often imprecise and uncertain in many real-world applications, and thus fuzzy data modeling has been extensively investigated in various database models as introduced in Chap.​ 2. Currently, huge amounts of electronic data are available on the Internet, and XML has been the de facto standard of information representation and exchange over the Web. Unfortunately, XML is not able to represent and process imprecise and uncertain data. To represent and manage the imprecise and uncertain data, Zadeh’s fuzzy set theory has been introduced into XML to extend XML such that uncertain and imprecise information can be represented and manipulated. The extended XML model together with the fuzzy database models introduced in Chap.​ 2 are the key techniques for managing fuzzy data. In this chapter, we mainly introduce fuzzy XML data model.

Huge amounts of electronic data are available on the Internet, and XML has been the de-facto standard of information representation and exchange over the Web. The basic structure of XML is tree, and an XML query is often formed as a twig pattern with predicates additionally imposed on the contents or attribute values of the tree nodes. Also, the XML query technique based on index mechanism is developed to further improve the query efficiency. However, the XML fall short in their ability to handle imprecise and uncertain information in many real-world applications, and also the relevant XML query techniques cannot support twig pattern query in fuzzy XML. Currently, fuzzy XML data modeling has been extensively investigated as introduced in Chap.​ 3. Therefore, topics related to the querying of fuzzy XML can be considered very interesting in the fuzzy XML data context. In this chapter, we focus on the methods of fuzzy XML complex twig queries with predicates and of building index mechanism on fuzzy XML query.

Nowadays most of data are modeled by database modes such as UML, relational and object-oriented database models as introduced in Chap. 1. Then database administrators are faced with the challenge of ensuring their databases to interface with other heterogeneous systems using XML, which is the de facto standard for publishing and exchanging data on the Web. However, information is often imprecise and uncertain in many real-world applications, and thus fuzzy database models and fuzzy XML have been extensively investigated as introduced in Chaps. 2 and 3. Accordingly, there is an increasing need to automate the process of extracting fuzzy XML models containing information from existing fuzzy database models. In this chapter, we introduce how to extract fuzzy XML from several typical fuzzy database models, including fuzzy UML data models, fuzzy relational database models, and fuzzy object-oriented database models.

Since the simplicity and flexibility of eXtensible Markup Language (XML), it has become the lingua franca for data exchange on the Web. Also, in order to deal with imprecise and uncertain information in many real-world applications, fuzzy XML has been extensively investigated as introduced in Chap.​ 3. However, XML brings some limitations, e.g., it may be difficult to store various data in a semantics way because of the semi-structured characteristic of XML. As we have known, fuzzy databases such as fuzzy relational databases and fuzzy object-oriented databases can store a large set of semantic information. Therefore, there is an increasing need to reengineer fuzzy XML into fuzzy database models, which may satisfy the needs of storing fuzzy XML data in fuzzy databases. In this chapter, we focus on reengineering fuzzy XML into fuzzy database models, including fuzzy UML data models, fuzzy relational database models, and fuzzy object-oriented database models.

XML has been the de-facto standard of information representation and exchange over the web. However, the real world is filled with imprecision and uncertainty. This creates a new set of data management requirements involving XML with imprecision and uncertainty, such as the need to reason on and query fuzzy XML documents and structures. Reasoning on XML with imprecision and uncertainty would help to check whether a fuzzy XML document conforms to a given document structure or two fuzzy XML documents are compatible, improve the precision and efficiency of query processing, etc. In particular, among several ways to approach knowledge representation and reasoning, Description Logics and ontologies are gaining privileged places in recent years. Therefore, in this chapter, we introduce how to reason on fuzzy XML with the knowledge representation formalisms fuzzy Description Logics and fuzzy ontologies.