Fuzzy XML Data Research Articles

A general characterization of integrating and querying heterogeneous fuzzy spatiotemporal XML data

A general characterization of integrating and querying heterogeneous fuzzy spatiotemporal XML data

An Algebra for Fuzzy Spatiotemporal Data in XML

A formal algebra is essential for applying standard database-style query optimization to XML queries. We develop such algebra for manipulating fuzzy spatiotemporal XML data. We also introduce several formal characterizations of fuzzy spatiotemporal algebraic equivalences and study how XQuery expressions can be transformed into algebraic expressions. It shows that the algebra can lay a firm foundation for managing fuzzy spatiotemporal XML data.

A structure-based approach of keyword querying for fuzzy XML data

A structure-based approach of keyword querying for fuzzy XML data

Heterogeneous fuzzy XML data integration based on structural and semantic similarities

Web data integration has become a crucial requirement for Web data management. A considerable number of approaches for integrating Extensible Markup Language (XML) data from heterogeneous data sources have been proposed. Yet these existing approaches are not fit for integration of fuzzy XML data because of their fuzzy characteristics. In this article, we provide a framework to deal with fuzzy XML document integration. Firstly, we propose a new fuzzy XML tree model. Secondly, we present an effective algorithm based on the tree edit distance to identify the structural and semantic similarities between the fuzzy documents represented in the proposed fuzzy XML tree model. Thirdly, we propose an integration strategy that is applied to integrate the fuzzy documents from different data sources. Finally, we conduct experiments to demonstrate that our approach can efficiently integrate fuzzy XML documents.

A Formal Approach of Construction Fuzzy XML Data Model Based on OWL 2 Ontologies

With the rapid development of the Web, a large number of electronic resources have been generated. Currently, XML has been an important tool for data representation and exchange over the Web. The incompleteness of information in the real-world is inherent. To deal with imprecise and uncertain data, fuzzy XML and fuzzy ontology modeling recently receive more attention. In order to represent the fuzzy information, we concentrate on fuzzy information modeling in a fuzzy XML model and fuzzy OWL 2 ontology in this paper. Furthermore, we propose an approach of transforming fuzzy OWL 2 ontologies (including structures and instances) into fuzzy XML models. Then we prove that the sementics of this transformation approach are preserved and propose a transforming example to explain the transforming process. This paper provides a new approach for the fuzzy XML modeling and fuzzy XML mapping based on the fuzzy OWL 2 ontologies.

Object-stack: An object-oriented approach for top-k keyword querying over fuzzy XML

Keyword search is the most popular technique of searching information from XML (eXtensible markup language) document. It enables users to easily access XML data without learning the structure query language or studying the complex data schemas. Existing traditional keyword query methods are mainly based on LCA (lowest common ancestor) semantics, in which the returned results match all keywords at the granularity of elements. In many practical applications, information is often uncertain and vague. As a result, how to identify useful information from fuzzy data is becoming an important research topic. In this paper, we focus on the issue of keyword querying on fuzzy XML data at the granularity of objects. By introducing the concept of "object tree", we propose the query semantics for keyword query at object-level. We find the minimum whole matching result object trees which contain all keywords and the partial matching result object trees which contain partial keywords, and return the root nodes of these result object trees as query results. For effectively and accurately identifying the top-K answers with the highest scores, we propose a score mechanism with the consideration of tf*idf document relevance, users' preference and possibilities of results. We propose a stack-based algorithm named object-stack to obtain the top-K answers with the highest scores. Experimental results show that the object-stack algorithm outperforms the traditional XML keyword query algorithms significantly, and it can get high quality of query results with high search efficiency on the fuzzy XML document.

Tree pattern matching in heterogeneous fuzzy XML databases

Dealing with heterogeneous data underlying fuzzy XML databases is challenging for any task of document management and knowledge discovery, since the structural heterogeneity and uncertainty of the large number of XML data sources make it difficult to effectively answer the structured query, especially the tree-pattern query. To address this issue, we propose a novel framework for managing fuzzy XML queries in a heterogeneous environment in this paper. In particular, we devise a holistic algorithm for matching tree-patterns over heterogeneous fuzzy XML data. Our approach adopts a compact stack technique and generates the matches by one scan on the relevant data associated with the tree-pattern, which eliminates re-scanning unnecessary portions of XML documents and redundant intermediate results. Finally, a comprehensive experimental evaluation conducted on real and synthetic data sets is carried out to show the significance of our approach as a solution for querying heterogeneous data in fuzzy XML documents.

Reengineering Fuzzy Spatiotemporal UML Data Model Into Fuzzy Spatiotemporal XML Model

Spatiotemporal information and application have received considerable attention. Spatiotemporal information is often imprecise or uncertain; therefore, the establishment of a reasonable and effective fuzzy spatiotemporal data model is vital. The traditional tool-based modeling method cannot meet the needs of fuzzy spatiotemporal data modeling. XML has become the standard of Web data representation and exchange. Based on the fuzzy set and probability theory, this paper analyzes the characteristics of fuzzy spatiotemporal data and establishes a fuzzy spatiotemporal XML data model. We present a fuzzy spatiotemporal UML data model and provide the rules for converting the fuzzy spatiotemporal UML data model to the fuzzy spatiotemporal XML model. We demonstrate our model and the conversion by applying them to a meteorological event.

Efficient keyword search in fuzzy XML

Evaluation of keyword queries over XML documents is one of the most fundamental tasks for XML data management. Previous methods have focused on the processing of deterministic XML data. However, uncertain data are inherent in practical applications, and how to support efficient keyword search over fuzzy XML data remains at large an open problem. In this paper, we tackle the problem of efficiently producing SLCA (smallest lowest common ancestor) results for keyword queries in fuzzy XML documents. We propose an efficient approach that can find all SLCA results for a given keyword query over fuzzy XML data. In particular, we introduce an effective method to transform a simple keyword query into a segmented keyword query that captures the original query requirements and conforms to the underlying fuzzy XML data. The proposed approach could help us eliminate irrelevant SLCA results and speed up the query processing. The final experiments show the effectiveness and efficiency of our proposed approach in generating SLCA results.

Incorporating fuzziness in spatiotemporal XML and transforming fuzzy spatiotemporal data from XML to relational databases

Since XML could benefit greatly from database support and more specifically from relational database systems, we study the methodology of modeling fuzzy spatiotemporal data in XML and transforming fuzzy spatiotemporal data from XML to relational databases as well. To accomplish this, we devise a fuzzy spatiotemporal data model in XML to capture the semantics of fuzzy spatiotemporal features. To allow for better and platform independent sharing of fuzzy spatiotemporal data stored in a relational format, we propose a temporal edge approach to transform fuzzy spatiotemporal XML data into relational databases. The unique feature of our approach is that no schema information is required for transformation of fuzzy spatiotemporal data. Moreover, temporal, spatial, and fuzzy features of fuzzy spatiotemporal data in XML documents are taken into consideration. Finally, the experimental results demonstrate the performance advantages of our approach. Such approach of transformation could provide a significant consolidation of the interoperability of fuzzy spatiotemporal data from XML to relational databases.

Data integration in fuzzy XML documents

In real-world Web applications, information is often vague or ambiguous. Fuzzy XML databases have been proposed to manage data exchanges among different imprecise and uncertain XML documents. However, data collected from different data sources referring to the same real-world object may conflict. It is the task of the data management system to resolve the conflicts and integrate the data without any interference from a user. In order to integrate data from multiple autonomous fuzzy XML databases, an integration of fuzzy XML data has to be performed. In this paper, we take a first step in a fundamental consolidation of the fuzzy XML data model. In particular, we investigate the integration technique, which is designed to integrate fuzzy XML data collected from different data sources. We develop a basic framework for detecting conflicts and overlaps in fuzzy XML documents. In addition, we propose an approach for reconciling the fuzzy XML data collected from different data sources.

Answering ordered tree pattern queries over fuzzy XML data

The widespread employment of XML requires the development of efficient methods to support advanced tree pattern queries. In addition, in real-world applications that involve analysis and management of databases, uncertainty is often an inherent property of data. Although a large number of researches have been dedicated to modeling uncertain XML and answering queries over uncertain XML data, how to conduct order-based tree pattern queries on fuzzy XML data remains an open problem. In this work, we aim to fill this gap and tackle the problem of order-based queries on fuzzy XML data. Based on the node encoding, we propose efficient tree pattern matching algorithms serving for ordered queries over fuzzy XML data. In particular, our algorithms adopt a two-phase processing technique: the first phase generates the root-to-leaf path solutions, and the second phase merges these path solutions for producing the desired matches to the ordered tree pattern. Typically, our approach only needs to access data related to the query nodes in the tree pattern once to obtain all the matching answers. Finally, a comprehensive experimental evaluation is carried out to investigate the performance of the proposed approaches.

Storing and querying fuzzy XML data in relational databases

Information imprecision and uncertainty exist in many real-world applications and for this reason fuzzy data management has been extensively investigated in various database management systems. Currently, introducing native support for XML data in relational database management systems (RDBMs) has attracted considerable interest with a view to leveraging the powerful and reliable data management services provided by RDBMs. Although there is a rich literature on XML-to-relational storage, none of the existing solutions satisfactorily addresses the problem of storing fuzzy XML data in RDBMs. In this paper, we study the methodology of storing and querying fuzzy XML data in relational databases. In particular, we present an edge-based approach to shred fuzzy XML data into relational data. The unique feature of our approach is that no schema information is required for our data storage. On this basis, we present a generic approach to translate path expression queries into SQL for processing XML queries.

Construction of fuzzy ontologies from fuzzy XML models

The success and proliferation of the Semantic Web depends heavily on construction of Web ontologies. However, classical ontology construction approaches are not sufficient for handling imprecise and uncertain information that is commonly found in many application domains. Therefore, great efforts on construction of fuzzy ontologies have been made in recent years. In particular, XML is imposing itself as a standard for representing and exchanging information on the Web, topics related to the modeling of fuzzy data have become very interesting in the XML data context. Therefore, constructing fuzzy ontologies from fuzzy XML data resources may make the existing fuzzy XML data upgrade to Semantic Web contents, and the constructed fuzzy ontologies may be useful for improving some fuzzy XML applications.This paper proposes a formal approach and an automated tool for constructing fuzzy ontologies from fuzzy XML data resources. Firstly, we propose a formal definition of fuzzy XML models (including the document structure fuzzy DTDs and the document content fuzzy XML documents). On this basis, we propose a formal approach for constructing fuzzy ontologies from fuzzy XML models, i.e., transforming a fuzzy XML model (including fuzzy DTD and fuzzy XML document) into a fuzzy ontology. Also, we give the proof of correctness of the construction approach, and provide a detailed construction example. Furthermore, we implement a prototype tool called FXML2FOnto, which can automatically construct fuzzy ontologies from fuzzy XML models. Finally, in order to show that the constructed fuzzy ontologies may be useful for improving some fuzzy XML applications, we focus on investigating how to reason on fuzzy XML models (e.g., conformance, inclusion, and equivalence) based on the constructed fuzzy ontologies, and it turns out that the reasoning tasks of fuzzy XML models can be checked by means of the reasoning mechanism of fuzzy ontologies.

Efficient processing of twig query with compound predicates in fuzzy XML

In order to find all occurrences of a twig pattern in XML documents, a considerable number of twig pattern matching algorithms have been proposed. Previous algorithms mainly focus on the conjunctive twig queries whose sibling edges are only connected by AND connectives. However, meaningful twig queries typically contain arbitrarily specified compound predicates including AND, OR and NOT in practical applications. Moreover, as far as we know, none of these twig matching algorithms have examined the processing of twig queries which contain all the compound predicates over fuzzy XML data. In this paper, we present the first study on evaluating twig queries with AND, OR and NOT connectives in fuzzy XML. We propose a novel holistic twig matching algorithm called LTwig for answering these complex queries. Our algorithm guarantees that the answers can be obtained by scanning the relevant data of the data streams associated with the nodes appearing in the twig pattern only once. A comprehensive set of experiments is finally carried out to demonstrate the effectiveness and efficiency of our proposed approach.

Fuzzy XML data modeling with the UML and relational data models

Information imprecision and uncertainty exist in many real-world applications and for this reason fuzzy data modeling has been extensively investigated in various data models. 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. This paper focuses on fuzzy XML data modeling, which is mainly involved in the representation model of the fuzzy XML, its conceptual design, and its storage in databases. Based on “possibility distribution theory”, we developed this fuzzy XML data model. We developed this fuzzy UML data model to design the fuzzy XML model conceptually. We investigated the formal conversions from the fuzzy UML model to the fuzzy XML model and the formal mapping from the fuzzy XML model to the fuzzy relational databases.