International Business Machines Corporation

University of Florida Research Foundation, Incorporated

Microsoft Technology Licensing, LLC

William Marsh Rice University

Fei Xu

Peter Haas

Eugene Jon Shekita

Kevin Scott Beyer

Vuk Ercegovac

Ravindranath Jampani

Luis L Perez

Mingxi Wu

Christopher M Jermaine

Jingren Zhou

Hiren Shantilal Patel

Edward John Triou, Jr

Subramanian Arumugam

Chistopher Matthew Jermaine

Systems and associated methods for highly parallel processing of parameterized simulations are described. Embodiments permit processing of stochastic data-intensive simulations in a highly parallel fashion in order to distribute the intensive workload. Embodiments utilize methods of seeding records in a database with a source of pseudo-random numbers, such as a compressed seed for a pseudo-random number generator, such that seeded records may be processed independently in a highly parallel fashion. Thus, embodiments provide systems and associated methods facilitating quicker data-intensive simulation by enabling highly parallel asynchronous simulations.

Systems and methods for highly parallel processing of parameterized simulations

Embodiments of the present invention allow multiple data streams to be analyzed as a single data set. The single data set may be described as a stream set herein. The multiple streams that are included in the stream set may be specified through a user script or query. For example, a query may be used to gather all streams created within a date range. The query could include one or more filters to gather certain information from the data streams or to exclude certain data streams that otherwise are in the query's range. A stream may be an unstructured byte stream of data. The stream may be created by append-only writing to the end of the stream. The stream could also be a structured stream that includes metadata that defines column structure and affinity/clustering information.

Analyzing multiple data streams as a single data object

According to one embodiment of the present invention, a method for managing uncertain data is provided. The method includes specifying data uncertainty using at least one variable generation (VG) function. The VG function generates pseudorandom samples of uncertain data values. A random database based on the VG function is specified and multiple Monte Carlo instantiations of the random database are generated. Using a Monte Carlo method, a query is repeatedly executed over the multiple Monte Carlo instantiations to output a Monte Carlo method result and associated query-results. The Monte Carlo method result may then be used to estimate statistical properties of a probability distribution of the query-result.

Managing uncertain data using Monte Carlo techniques

A risk analysis system and method are provided. The system includes an analyzer for analyzing database instances by executing a query on each database instance and selecting a cutoff value. The analyzer also discards the sets of uncertainty data that yield query-result values below the cutoff value and retains the database instances that yield query-result values above the cutoff value as elite sets. The system also includes a cloner to replicate the elite sets, and a sampler to modify the elite sets so that each elite set is mutually statistically independent while still yielding query-result values above the cutoff value.

Risk analysis for data-intensive stochastic models

According to one embodiment of the present invention, a method for managing uncertain data is provided. The method includes specifying data uncertainty using at least one variable generation (VG) function, wherein the VG function generates pseudorandom samples of uncertain data values. A random database based on the VG function is specified. and multiple Monte Carlo instantiations of the random database are generated. Using a Monte Carlo method, a query is repeatedly executed over the multiple Monte Carlo instantiations to output a Monte Carlo method result and associated query-results. The Monte Carlo method result may then be used to estimate statistical properties of a probability distribution of the query-result.

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Fei Xu