By Jennifer Widom
Active database platforms improve conventional database performance with strong rule-processing features, offering a uniform and effective mechanism for plenty of database approach purposes. between those purposes are integrity constraints, perspectives, authorization, information amassing, tracking and alerting, knowledge-based platforms, specialist platforms, and workflow administration. this important assortment specializes in the main renowned study tasks in energetic database structures. The venture leaders for every prototype method offer designated discussions in their initiatives and the relevance in their effects to the way forward for lively database structures. * A extensive review of present energetic database platforms and the way they are often prolonged and superior * A entire advent to the center themes of the sphere, together with its motivation and heritage * insurance of lively database (trigger) features in advertisement items * dialogue of impending criteria
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Additional resources for Active Database Systems: Triggers and Rules for Advanced Database Processing
For instance, one may want to develop a new service that composes an ontology engine and a rules engine service for creating new semantics-based decision support services. From a clinical perspective, one may want to compose clinical protocol monitoring services and notification services (which monitor the state of a patient and alert physicians if necessary) with appropriate clinical decision support services and medication dosing services to offer sophisticated decision support. Data and Knowledge Integration: This represents the ability to integrate data across different types of clinical and biological data repositories.
Creation data, author, category of knowledge) are stored in a metadata repository. Knowledge such as genotypic-phenotypic associations can be stored in a database, whereas ontologies and rule bases can be stored in specialized repositories managed by ontology engines and rule bases. Tools that support metadata annotation can be used to create metadata annotations that can be stored in the metadata repository. 4 Functional Requirements The conceptual architecture discussed in the earlier section helps us identify crucial functional requirements that need to be supported for enabling translational medicine, discussed next.
These semantics are missing from the XML data model. • The XML data model is strong in capturing positional collections of data since the children of an XML element are textually ordered. It is weak in capturing non-positional collections since these would suggest arc labels, absent in XML, which indicate the role each of the unordered components is playing in the collection. • The XML data model doesn’t support the distinction of various types of tags into classes and properties, as in the case of the RDF data model where there is a clear distinction between resources, classes, properties and values.