Tackling biodiversity information is essentially a distributed effort. Data handled are inherently heterogeneous, being provided by distinct research groups and using different vocabularies. Queries in biodiversity systems require to correlate these data, using many kinds of knowledge on geographic, biologic and ecological issues. Available biodiversity systems can only cope with part of these queries, and end users must perform several manual tasks to derive the desired correlations, because of semantic mismatches among data sources and lack of appropriate operators. This paper presents a solution based on Web services to meet these challenges. It relies on ontologies to retrieve the query contexts and uses the terms of this context to discover suitable sources in data repositories. This approach is being tested using real data, with new services.
This paper describes the development of an open source CASE tool, the ArgoCASEGEO, and its modular architecture. The ArgoCASEGEO allows the geographic database modelling based on the UML-GeoFrame conceptual model that is specific for Geographic Information Systems applications. The data dictionary associated to the modelled schema is stored as a XML/XMI document, aiming its use by other software. The ArgoCASEGEO follows a design methodology based on a reusable collection of analysis patterns. The analysis patterns collection is stored in a data base composing a Catalog. The catalog is attached to the ArgoCASEGEO. Thus, searching for existing analysis patterns will be an easier and efficient task.
Scientific research is producing and consuming large volumes of multimedia data at an ever growing rate. Data annotations are used, among others, to provide context information and enhance content management, making it easier to interpret and share data. However, raw multimedia data often needs to go through complex processing steps before it can be consumed. During these transformation processes, original annotations from the production phase are often discarded or ignored, since their usefulness is usually limited to the first transformation step. New annotations must be made at each step, and associated with the final product, a time consuming task often carried out manually. The task of systematically associating new annotations to the result of each data transformation step is known as annotation propagation. This paper introduces techniques for structuring and propagating annotations, in parallel to the data transformation processes, thereby alleviating the overhead and decreasing the errors introduced by manual annotation. This helps the construction of new annotated multimedia data sets, preserving contextual information. The solution is based on: (i) the notion of semantic annotations; (ii) a set of transformations rules, based on ontological relations; and, (iii) workflows that deal with interrelated processing steps
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