We discuss a proposal for the implementation of the model management operator ModelGen, which translates schemas from one model to another, for example from object-oriented to SQL or from SQL to XML Schema Descriptions. The operator can be used to generate database wrappers (e.g., object-oriented or XML to relational), default user interfaces (e.g., relational to forms), or default database schemas from other representations. The approach translates schemas from a model to another, within a predefined, but large and extensible, set of models: given a source schema S expressed in a source model, and a target model TM, it generates a schema S expressed in TM that is "equivalent" to S. A wide family of models is handled by using a metamodel in which models can be succinctly and precisely described. The approach expresses the translation as Datalog rules and exposes the source and target of the translation in a generic relational dictionary. This makes the translation transparent, easy to customize and model-independent. The proposal includes automatic generation of translations as composition of basic steps.
Abstract. We describe MIDST, an implementation of the model management operator ModelGen, which translates schemas from one model to another, for example from OO to SQL or from SQL to XSD. It extends past approaches by translating database instances, not just their schemas. The operator can be used to generate database wrappers (e.g. OO or XML to relational), default user interfaces (e.g. relational to forms), or default database schemas from other representations. The approach translates both schemas and data: given a source instance I of a schema S expressed in a source model, and a target model TM, it generates a schema S expressed in TM that is "equivalent" to S and an instance I of S "equivalent" to I. A wide family of models is handled by using a metamodel in which models can be succinctly and precisely described. The approach expresses the translation as Datalog rules and exposes the source and target of the translation in a generic relational dictionary. This makes the translation transparent, easy to customize and model-independent.
"Most of the recent approaches to keyword search employ graph structured representation of data. Answers to queries are generally sub-structures of the graph, containing one or more keywords. While finding the nodes matching keywords is relatively easy, determining the connections between such nodes is a complex problem requiring on-the-fly time consuming graph exploration. Current techniques suffer from poorly performing worst case scenario or from indexing schemes that provide little support to the discovery of connections between nodes. In this paper, we present an indexing scheme for RDF that exposes the structural characteristics of the graph, its paths and the information on the reachability of nodes. This knowledge is exploited to expedite the retrieval of the sub-structures representing the query results. In addition, the index is organized to facilitate maintenance operations as the dataset evolves. Experimental results demonstrates the feasibility of our index that significantly improves the query execution performance.
There is a large amount of health information available for any patient to address his/her health concerns. The freely available health datasets include community health data at the national, state, and community level, readily accessible and downloadable. These datasets can help to assess and improve healthcare performance, as well as help to modify health-related policies. There are also patient-generated datasets, accessible through social media, on the conditions, treatments, or side effects that individual patients experience. Clinicians and healthcare providers may benefit from being aware of national health trends and individual healthcare experiences that are relevant to their current patients. The available open health datasets vary from structured to highly unstructured. Due to this variability, an information seeker has to spend time visiting many, possibly irrelevant, Websites, and has to select information from each and integrate it into a coherent mental model. In this paper, we discuss an approach to integrating these openly available health data sources and presenting them to be easily understandable by physicians, healthcare staff, and patients. Through linked data principles and Semantic Web technologies we construct a generic model that integrates diverse open health data sources. The integration model is then used as the basis for developing a set of analytics as part of a system called ‘Social InfoButtons’, providing awareness of both community and patient health issues as well as healthcare trends that may shed light on a specific patient care situation. The prototype system provides patients, public health officials, and healthcare specialists with a unified view of health-related information from both official scientific sources and social networks, and provides the capability of exploring the current data along multiple dimensions, such as time and geographical location.
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