975 research outputs found
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
Approximate Query Answering and Result Refinement on XML Data
Today, many economic decisions are based on the fast analysis of XML data. Yet, the time to process analytical XML queries is typically high. Although current XML techniques focus on the optimization of query processing, none of these support early approximate feedback as possible in relational Online Aggregation systems. In this paper, we introduce a system that provides fast estimates to XML aggregation queries. While processing, these estimates and the assigned confidence bounds are constantly improving. In our evaluation, we show that without significantly increasing the overall execution time our system returns accurate guesses of the final answer long before traditional systems are able to produce output
Content-Aware DataGuides for Indexing Large Collections of XML Documents
XML is well-suited for modelling structured data with
textual content. However, most indexing approaches perform
structure and content matching independently, combining
the retrieved path and keyword occurrences in a third
step. This paper shows that retrieval in XML documents can
be accelerated significantly by processing text and structure
simultaneously during all retrieval phases. To this end,
the Content-Aware DataGuide (CADG) enhances the wellknown
DataGuide with (1) simultaneous keyword and path
matching and (2) a precomputed content/structure join. Extensive
experiments prove the CADG to be 50-90% faster
than the DataGuide for various sorts of query and document,
including difficult cases such as poorly structured
queries and recursive document paths. A new query classification
scheme identifies precise query characteristics with
a predominant influence on the performance of the individual
indices. The experiments show that the CADG is applicable
to many real-world applications, in particular large
collections of heterogeneously structured XML documents
RDF Querying
Reactive Web systems, Web services, and Web-based publish/
subscribe systems communicate events as XML messages, and in
many cases require composite event detection: it is not sufficient to react
to single event messages, but events have to be considered in relation to
other events that are received over time.
Emphasizing language design and formal semantics, we describe the
rule-based query language XChangeEQ for detecting composite events.
XChangeEQ is designed to completely cover and integrate the four complementary
querying dimensions: event data, event composition, temporal
relationships, and event accumulation. Semantics are provided as
model and fixpoint theories; while this is an established approach for rule
languages, it has not been applied for event queries before
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