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.
Last about schemas complexity, in @cite_5 the authors present a set of schema metrics in the context of schema mapping. A combined metric is defined based in simpler metrics considering schemas size, use of different schema features, and naming strategies. The combined metric is evaluated in the context of business document standards.
@cite_5
97127447 1989875612 2156543375 1503955515
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. Exchanging structured business documents is inevitable for successful collaboration in electronic commerce. A prerequisite, for fostering the interoperability between business partners utilizing different business document standards, is a mapping between different standards. However, the effort involved in creating those mappings is hard to estimate. For example, the complexity of standardized formats is one crucial aspect affecting the effort of the mapping process. Therefore, a notion of complexity is desirable for both, manual as well as automatic mapping processes. For this reason we develop an initial set of metrics, based on well established metrics for XML Schema, allowing to analyze the complexity of business document standards. Having such metrics at hand allows estimating the complexity and hence the mapping effort of a business document standard, prior to the actual mapping process. We demonstrate the complexity metrics on three different business document standards from the electronic commerce domain. Matching elements of two data schemas or two data instances plays a key role in data warehousing, e-business, or even biochemical applications. In this paper we present a matching algorithm based on a fixpoint computation that is usable across different scenarios. The algorithm takes two graphs (schemas, catalogs, or other data structures) as input, and produces as output a mapping between corresponding nodes of the graphs. Depending on the matching goal, a subset of the mapping is chosen using filters. After our algorithm runs, we expect a human to check and if necessary adjust the results. As a matter of fact, we evaluate the 'accuracy' of the algorithm by counting the number of needed adjustments. We conducted a user study, in which our accuracy metric was used to estimate the labor savings that the users could obtain by utilizing our algorithm to obtain an initial matching. Finally, we illustrate how our matching algorithm is deployed as one of several high-level operators in an implemented testbed for managing information models and mappings. In this paper, we carefully explore the assumptions behind using information capacity equivalence as a measure of correctness for judging transformed schemas in schema integration and translation methodologies. We present a classification of common integration and translation tasks based on their operational goals and derive from them the relative information capacity requirements of the original and transformed schemas. We show that for many tasks, information capacity equivalence of the schemas is not strictly required. Based on this, we present a new definition of correctness that reflects each undertaken task. We then examine existing methodologies and show how anomalies can arise when using those that do not meet the proposed correctness criteria.