Intuitionistic fuzzy XML query matching and rewriting

With the emergence of XML as a standard for data representation, particularly on the web, the need for intelligent query languages that can operate on XML documents with structural heterogeneity has recently gained a lot of popularity. Traditional Information Retrieval and Database approaches have limitations when dealing with such scenarios. Therefore, fuzzy (flexible) approaches have become the predominant. In this thesis, we propose a new approach for approximate XML query matching and rewriting which aims at achieving soft matching of XML queries with XML data sources following different schemas. Unlike traditional querying approaches, which require exact matching, the proposed approach makes use of Intuitionistic Fuzzy Trees to achieve approximate (soft) query matching. Through this new approach, not only the exact answer of a query, but also approximate answers are retrieved. Furthermore, partial results can be obtained from multiple data sources and merged together to produce a single answer to a query. The proposed approach introduced a new tree similarity measure that considers the minimum and maximum degrees of similarity/inclusion of trees that are based on arc matching. New techniques for soft node and arc matching were presented for matching queries against data sources with highly varied structures. A prototype was developed to test the proposed ideas and it proved the ability to achieve approximate matching for pattern queries with a number of XML schemas and rewrite the original query so that it obtain results from the underlying data sources. This has been achieved through several novel algorithms which were tested and proved efficiency and low CPU/Memory cost even for big number of data sources

Reasoning & Querying – State of the Art

Various query languages for Web and Semantic Web data, both for practical use and as an area of research in the scientific community, have emerged in recent years. At the same time, the broad adoption of the internet where keyword search is used in many applications, e.g. search engines, has familiarized casual users with using keyword queries to retrieve information on the internet. Unlike this easy-to-use querying, traditional query languages require knowledge of the language itself as well as of the data to be queried. Keyword-based query languages for XML and RDF bridge the gap between the two, aiming at enabling simple querying of semi-structured data, which is relevant e.g. in the context of the emerging Semantic Web. This article presents an overview of the field of keyword querying for XML and RDF

Materialized View Selection in XML Databases

Materialized views, a rdbms silver bullet, demonstrate its efficacy in many applications, especially as a data warehousing/decison support system tool. The pivot of playing materialized views efficiently is view selection. Though studied for over thirty years in rdbms, the selection is hard to make in the context of xml databases, where both the semi-structured data and the expressiveness of xml query languages add challenges to the view selection problem. We start our discussion on producing minimal xml views (in terms of size) as candidates for a given workload (a query set). To facilitate intuitionistic view selection, we present a view graph (called vcube) to structurally maintain all generated views. By basing our selection on vcube for materialization, we propose two view selection strategies, targeting at space-optimized and space-time tradeoff, respectively. We built our implementation on top of Berkeley DB XML, demonstrating that significant performance improvement could be obtained using our proposed approaches

Processing techniques for partial tree-pattern queries on XML data

In recent years, eXtensible Markup Language (XML) has become a de facto standard for exporting and exchanging data on the Web. XML structures data as trees. Querying capabilities are provided through patterns matched against the XML trees. Research on the processing of XML queries has focused mainly on tree-pattern queries. Tree-pattern queries are not appropriate for querying XML data sources whose structure is not fully known to the user, or for querying multiple data sources which structure information differently. Recently, a class of queries, called Partial Tree-Pattern Queries (PTPQs) was identified. A central feature of PTPQs is that the structure can be specified fully, partially, or not at all in a query. For this reason. PTPQs can be used for flexibly querying XML data sources. This thesis deals with processing techniques for PTPQs. In particular, it addresses the satisfiability, containment and minimization problems for PTPQs. In order to cope with structural expression derivation issues and to compare PTPQs, a set of inference rules is suggested and a canonical form for PTPQs that comprises all derived structural expressions is defined. This canonical form is used for determining necessary and sufficient conditions for PTPQ satisfiability. The containment problem is studied both in the absence and in the presence of structural summaries of data called dimension graphs. It is shown that this problem cannot be characterized by homomorphisms between PTPQs, even when PTPQs are put in canonical form. In both cases of the problem, necessary and sufficient conditions for PTPQ containment are provided in terms of homomorphisms between PTPQs and (a possibly exponential number of) tree-pattern queries. This result is used to identify a subclass of PTPQs that strictly contains tree-pattern queries for which the containment problem can be fully characterized through the existence of homomorphisms. To cope with the high complexity of PTPQ containment, heuristic approaches for this problem are designed that trade accuracy for speed. The heuristic approaches equivalently add structural expressions to PTPQs in order to increase the possibility for a homomorphism between two contained PTPQs to exist. An implementation and extensive experimental evaluation of these heuristics shows that they are useful in practice, and that they can be efficiently implemented in a query optimizer. The goal of PTPQ minimization is to produce an equivalent PTPQ which is syntactically smaller in size. This problem is studied in the absence of structural summaries. It is shown that PTPQs cannot be minimized by removing redundant parts as is the case with certain classes of tree-pattern queries. It is also shown that, in general, a PTPQ does not have a unique minimal equivalent PTPQ. Finally, sound, but not complete, heuristic approaches for PTPQ minimization are presented. These approaches gradually trade execution time for accuracy

Applications of flexible querying to graph data

Graph data models provide flexibility and extensibility that makes them well-suited to modelling data that may be irregular, complex, and evolving in structure and content. However, a consequence of this is that users may not be familiar with the full structure of the data, which itself may be changing over time, making it hard for users to formulate queries that precisely match the data graph and meet their information seeking requirements. There is a need therefore for flexible querying systems over graph data that can automatically make changes to the user's query so as to find additional or different answers, and so help the user to retrieve information of relevance to them. This chapter describes recent work in this area, looking at a variety of graph query languages, applications, flexible querying techniques and implementations

A new Nested Graph Model for Data Integration

Despite graph data gained increasing interest in several fields, no data model suitable for both querying and integrating differently structured graph and (semi)structured data has been currently conceived. The lack of operators allowing combinations of (multiple) graphs in current graph query languages (graph joins), and on graph data structure allowing neither data integration nor nested multidimensional representations (graph nesting) are a possible motivation. In order to make such data integration possible, this thesis proposes a novel model (General Semistructured data Model) allowing the representation of both graphs and arbitrarily nested contents (e.g., one node can be contained by more than just one parent node), thus allowing the definition of a nested graph model, where both vertices and edges may include (overlapping) graphs. We provide two graph joins algorithms (Graph Conjunctive Equijoin Algorithm and Graph Conjunctive Less-equal Algorithm) and one graph nesting algorithm (Two HOp Separated Patterns). Their evaluation on top of our secondary memory representation showed the inefficiency of existing query languages’ query plan on top of their respective data models (relational, graph and document-oriented). In all three algorithms, the enhancement was possible by using an adjacency list graph representation, thus reducing the cost of joining the vertices with their respective outgoing (or ingoing) edges, and by associating hash values to both vertices and edges. As a secondary outcome of this thesis, a general data integration scenario is provided where both graph data and other semistructured and structured data could be represented and integrated into the General Semistructured data Model. A new query language outlines the feasibility of this approach (General Semistructured Query Language) over the former data model, also allowing to express both graph joins and graph nestings. This language is also capable of representing both traversal and data manipulation operators