Abstract: A time-aware union operator is disclosed for consistent integration of time-aware data, wherein the time-aware union produces a time-aware consistent integrated view of underlying sources according to specified key constraints and policies. The implementation of time-aware union is idempotent, commutative, and associative, thus making it suitable for data integration, and it produces the same integrated outcome, modulo representation of time, regardless of the order in which sources are integrated.

Abstract: A time-aware union operator is disclosed for consistent integration of time-aware data, wherein the time-aware union produces a time-aware consistent integrated view of underlying sources according to specified key constraints and policies. The implementation of time-aware union is idempotent, commutative, and associative, thus making it suitable for data integration, and it produces the same integrated outcome, modulo representation of time, regardless of the order in which sources are integrated.

Abstract: An experiment manager is discussed for the design and execution of numerical experiments in composite simulation models, such as those created using the Smarter Planet Platform for Analysis Simulation of Health (Splash). The experiment manager independently elicits experiment-related information from each contributor of a component model, and uses this information to subsequently assist the creator of a composite model in selecting experimental factors, creating experimental designs based on these factors, and executing the experiments. This functionality permits cross-disciplinary modeling, simulation, sensitivity analysis and optimization in the setting of complex systems.

Abstract: An aspect of combining simulation models includes a processor configured to process information relating to a plurality of simulated models. The processor collects the information to be processed related to the simulated models in at least one memory having metadata relating to the simulated models. The processor has a transformation component generated using a schema mapping tool. The transformation component detects and corrects any incompatibility between a first and a second simulation model by obtaining metadata relating to the first simulation model and the second simulation model from the memory.

Abstract: An aspect of handling a plurality of simulated models includes receiving from a first simulation model a stream of source data and determining data processing compatibility between the the simulation model and a second-simulation model by i) obtaining metadata relating to the first-simulation models from a memory and ii) obtaining metadata relating to the second simulation model from the memory. An aspect also includes generating a transformation component using a schema mapping tool for providing compatibility between the first and second simulation models upon a determination that the compatibility does not exist between the two models. The—transformation component is generated—using the metadata relating to the first and second simulation models. The method also includes generating a—stream of target data by the second simulation model via the transformation component. The second simulation model uses the stream of source data as input.

Abstract: Embodiments relate to a method and computer program product for generating a composite simulated model. A method includes receiving a specification request for generating a set of target time-series data from a set of source time-series data and obtaining specification information relating to the set of source time-series data, obtaining specification information relating to the set of target time-series data, and obtaining the source time-series data. The method also includes comparing the source and target specification information to determine if the set of source time-series data are time-aligned with the set of target time-series data and converting the set of source time-series data to the set of target time-series data upon determination that time alignment is needed.

Abstract: An aspect of synchronization of time between simulation models includes receiving a specification request for generating a set of target time-series data using a set of source time-series data, obtaining specification information relating to the set of source time-series data, obtaining specification information relating to the set of target time-series data and obtaining the source time-series data; comparing the source specification information and the target specification information to determine if the set of source time-series data are time-aligned with the set of target time-series data, and converting the set of source time-series data to the set of target time-series data upon determination that time alignment is needed.

Abstract: Embodiments of the invention relate to correlating schema mappings. In one embodiment, a set of schema mappings over a source schema and a target schema are received. Each of the schema mappings is decomposed into a basic schema mapping. A first set and second set of relations re determined for the source schema and the target schema, respectively. Each relation in the first set of relations is paired to at least one relation in the second set of relations. The pairing forms multiple relation pairs between the first set and second of relations in the form of (T, T?), where T is a source portion of a relation pair and T? is a target portion of the relation pair. A set of basic schema mappings is identified that matches the relation pair. Each basic schema mapping is merged into a single schema mapping.

Abstract: A method and system for processing a schema mapping M from a source schema S to a target schema T by executing program code on a processor of a computer system. A schema mapping M? from T to S is determined via processing the schema mapping M. The schema mapping M includes at least one constraint ?. Each constraint ? consists of a source-to-target tuple-generating dependencies (s-t tgd). The schema mapping M? includes at least one constraint ??. Each constraint ?? consists of a disjunctive tgd with constants and inequalities among the constants. The schema mapping M? is stored in at least one computer usable storage device of the computer system and/or is outputted in at least one output device of the computer system.

Abstract: A method and system for processing a schema mapping M from a source schema S to a target schema T by executing program code on a processor of a computer system. A schema mapping M? from T to S is determined via processing the schema mapping M. The schema mapping M includes at least one constraint ?. Each constraint ? consists of a source-to-target tuple-generating dependencies (s-t tgd). The schema mapping M? includes at least one constraint ??. Each constraint ?? consists of a disjunctive tgd with constants and inequalities among the constants. The schema mapping M? is stored in at least one computer usable storage device of the computer system and/or is outputted in at least one output device of the computer system.

Abstract: A method for generating a schema mapping. A provided mapping M12 relates schema S1 to schema S2. A provided mapping M23 relates schema S2 to schema S3. A mapping M13 is generated from schema S1 to schema S3 as a composition of mappings M12 and M23. Mappings M12, M23, and M13 are each expressed in terms of at least one second-order nested tuple-generating dependency (SO nested tgd). Mapping M13 does not expressly recite any element of schema S2. At least one schema of the schemas S1 and S2 may comprise at least one complex type expression nested inside another complex type expression. Mapping M13 may define the composition of the mappings M12 and M23 with respect to a relationship semantics or a transformation semantics.

Abstract: A method and system for processing a schema mapping M from a source schema S to a target schema T by executing program code on a processor of a computer system. A schema mapping M? from T to S is determined via processing the schema mapping M. The schema mapping M includes at least one constraint ?. Each constraint ? consists of a source-to-target tuple-generating dependencies (s-t tgd). The schema mapping M? includes at least one constraint ??. Each constraint ?? consists of a disjunctive tgd with constants and inequalities among the constants. The schema mapping M? is stored in at least one computer usable storage device of the computer system and/or is outputted in at least one output device of the computer system.

Abstract: A method and system for processing a schema mapping M from a source schema S to a target schema T by executing program code on a processor of a computer system. A schema mapping M? from T to S is determined via processing the schema mapping M. The schema mapping M includes at least one constraint ?. Each constraint ? consists of a source-to-target tuple-generating dependencies (s-t tgd). The schema mapping M? includes at least one constraint ??. Each constraint ?? consists of a disjunctive tgd with constants and inequalities among the constants. The schema mapping M? is stored in at least one computer usable storage device of the computer system and/or is outputted in at least one output device of the computer system.

Abstract: A method for generating a schema mapping. A provided mapping M12 relates schema S1 to schema S2. A provided mapping M23 relates schema S2 to schema S3. A mapping M13 is generated from schema S1 to schema S3 as a composition of mappings M12 and M23. Mappings M12, M23, and M13 are each expressed in terms of at least one second-order nested tuple-generating dependency (SO nested tgd). Mapping M13 does not expressly recite any element of schema S2. At least one schema of the schemas S1 and S2 may comprise at least one complex type expression nested inside another complex type expression. Mapping M13 may define the composition of the mappings M12 and M23 with respect to a relationship semantics or a transformation semantics.

Abstract: A method for converting relational data to XML (eXtensible Markup Language) is provided. The method can use a greedy algorithm to efficiently construct materialized XML views of relational databases. A greedy algorithm designed for XML view definition queries is provided for decomposing a large query into smaller queries and determining which query will run faster without actually running the query.

Abstract: A method for converting relational data to XML (Extensible Markup Language) is provided. The method, sometimes referred to as SilkRoute, provides a general, dynamic and efficient tool for viewing and querying relational data in XML. SilkRoute can express mappings of relational data in XML that conforms to arbitrary public document type definitions. Also, SilkRoute can materialize the fragment of an XML view needed by an application and it can fully exploit the query engine of a relational database management system whenever data items in an XML view need to be materialized.