International Business Machines Corporation

Patricia G Selinger

Bruce Gilbert Lindsay

Peter Haas

Wang-Chiew Tan

Roger A Reinsch

Melvin R Zimowski

Paul Philip Maglio

Robert Peter Resch

Robert J Grafe

John G Adair

Cheryl Ann Kieliszewski

Daniel J Coyle, Jr

Ronald Mak

Yun Yu Wang

Mir Hamid Pirahesh

Jen-Yao Chung

Ignacio Gabriel Terrizzano

Marisa S Viveros

Richard A Demers

Richard R Sanders

Partha Pratim Bhattacharya

Nicole C Barberis

Robert D Jackson

Dusan Ecimovic

Piyaphol Phoungphol

Michael E Murphy

Robert L Sunday

Lawrence Peter Zaino

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.

System for design and execution of numerical experiments on a composite simulation model

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 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&#x2014;transformation component is generated&#x2014;using the metadata relating to the first and second simulation models. The method also includes generating a&#x2014;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.

Composite simulation modeling and analysis

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.

A method of performing a parallel join operation on a pair of relations R1 and R2 in a system containing P processors organized into Q clusters of P/Q processors each. The system contains disk storage for each cluster, shared by the processors of that cluster, together with a shared intermediate memory (SIM) accessible by all processors. The relations R1 and R2 to be joined are first sorted on the join column. The underlying domain of the join column is then partitioned into P ranges of equal size. Each range is further divided into M subranges of progressively decreasing size to create MP tasks T.sub.m,p, the subranges of a given range being so sized relative to one another that the estimated completion time for task T.sub.m,p is a predetermined fraction that of task T.sub.m-1,p. Tasks T.sub.m,p with larger time estimates are assigned (and the corresponding tuples shipped) to the cluster to which processor p belongs, while tasks with smaller time estimates are assigned to the SIM, which is regarded as a universal cluster (cluster 0). The actual task-to-processor assignments are determined dynamically during the join phase in accordance with the dynamic longest processing time first (DLPT) algorithm. Each processor within a cluster picks its next task at any given decision point to be the one with the largest time estimate which is owned by that cluster or by cluster 0.

Method of performing a parallel relational database query in a

The invention establishes the context in which data exchanged between dissimilar relational database management systems can be mutually understood and preserved, and data conversions can be minimized. The invention accomplishes this by establishing layers of descriptive information which isolate machine characteristics, levels of support software, and user data descriptions. Optimized processing is achieved by processing the different descriptor levels at different times during the development and execution of the database management systems. Minimal descriptive information is exchanged between the cooperating database management systems. Any data conversions that may be necessary are done only by the receiver of the data, and only at the point where it is necessary to have the data represented in the receiver's native format for processing. For sending and receiving systems which match, data conversion is completely avoided, even when the data may have been forwarded through intermediate DBMS systems which do not match. For sending and receiving systems which do not match, data conversion is minimized. The data conversion routines and tables in each system are also minimized by requiring conversion only INTO a system's native format, never FROM its native format into some other format.

Method and combination for minimizing data conversions when data is

Heterogenous database communication system in which communicating

Application programs which are developed and scheduled within a first computing system environment are permitted to access relational data registered at a remote database management system (DBMS) operating in a second computing environment dissimilar to the first computing environment. Access to data through the DBMS from an application execution site remote from the DBMS is supported by a process, logically subordinate to the application program which maps application program data access requests to the DBMS.

Query language execution on heterogeneous database servers using a

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San Jose, CA, United States of America

Patricia G Selinger