XML Schema is the language used to define the structure of messages exchanged between OGC-based web service clients and providers. The size of these schemas has been growing with time, reaching a state that makes its understanding and effective application a hard task. A first step to cope with this situation is to provide different ways to measure the complexity of the schemas. In this regard, we present in this paper an analysis of the complexity of XML schemas in OGC web services. We use a group of metrics found in the literature and introduce new metrics to measure size and or complexity of these schemas. The use of adequate metrics allows us to quantify the complexity, quality and other properties of the schemas, which can be very useful in different scenarios.
Literature about measuring XML schemas complexity has increased in the last few years, based mainly on adapting metrics for assessing complexity on software systems or XML documents @cite_21 @cite_2 @cite_13 . To our best knowledge the most relevant attempt in this topic is presented in @cite_12 . Here, a comprehensive set of metrics is defined and applied to a large corpus of real-world XML schemas. Based on the resulting metrics, the authors define a categorization for a set of schema files according to its size. Another relevant study is @cite_15 which defines eleven metrics to measure the quality and complexity of XML Schemas.
@cite_21 @cite_2 @cite_15 @cite_13 @cite_12
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Despite the ubiquity of XML, research in metrics for XML documents is scarce. This paper proposes and discusses eleven metrics to measure the quality and complexity of XML Schema and conforming XML documents. To provide an easy view of these metrics, two composite indices have been defined to measure quality and complexity. An open source metric analyzer tool for XML Schema has been developed. The tool can easily be extended to add new metrics and alter the composition of the indices to best fit the requirements of a given application. Recently, a large number of XML documents are available on the Internet. This trend motivated many researchers to analyze them multi-dimensionally in the same way as relational data. In this paper, we propose a new framework for multidimensional analysis of XML documents, which we call XML-OLAP. We base XML-OLAP on XML warehouses where every fact data as well as dimension data are stored as XML documents. We build XML cubes from XML warehouses. We propose a new multidimensional expression language for XML cubes, which we call XML-MDX. XML-MDX statements target XML cubes and use XQuery expressions to designate the measure data. They specify text mining operators for aggregating text constituting the measure data. We evaluate XML-OLAP by applying it to a U.S. patent XML warehouse. We use XML-MDX queries, which demonstrate that XML-OLAP is effective for multi-dimensionally analyzing the U.S. patents. We consider the problem of (macro) F-measure maximization in the context of extreme multilabel classification (XMLC), i.e., multi-label classification with extremely large label spaces. We investigate several approaches based on recent results on the maximization of complex performance measures in binary classification. According to these results, the F-measure can be maximized by properly thresholding conditional class probability estimates. We show that a naive adaptation of this approach can be very costly for XMLC and propose to solve the problem by classifiers that efficiently deliver sparse probability estimates (SPEs), that is, probability estimates restricted to the most probable labels. Empirical results provide evidence for the strong practical performance of this approach. The XML has undoubtedly become a standard for data representation and manipulation. But most of XML documents are still created without the respective description of their structure, i.e. an XML schema. Hence, in this paper we focus on the problem of automatic inferring of an XML schema for a given sample set of XML documents. Contrary to existing works, whose aim is to infer as concise schema as possible, we focus on inferring of a more realistic result, i.e. a schema that is closer to human-written ones and bears more precise information. For this purpose we extend and combine the existing verified techniques (such as ACO heuristics or MDL principle) with a set of heuristics exploiting semantics of element attribute names, thesauri or statistical analysis of input data. Using a set of examples we show and discuss advantages of our proposal.