The Mathworks, Inc.

Thomas Lane

Mary Ann Branch Freeman

Richard Amos

Jocelyn Martin

Penelope Anderson

Peter C Perkins

Michael Procopio

Wei Wang

Rakesh Kumar

James Stewart

Lucio Andrade-Cetto

Marcelo Marazzi

Marjorie Berman

Tara Scott

Aaditya Kalsi

Amy Kidd

Yashwanth Annapureddy

Anandan Rangasamy

Nicholas Haddad

Edric Mark Ellis

Aaron J Powers

Danil Kirsanov

Gautam V Pendse

Kari Sortland

Benjamin Tordoff

According to some embodiments, a method for making input data available for processing by one or more processors comprises storing one or more parameters, wherein the one or more parameters comprise information identifying a location of the input data; and creating a datastore object using the one or more parameters, wherein the datastore object interfaces the input data and includes a read method for reading a chunk, the chunk being a subset of the input data, and having a size that does not exceed a memory size assigned to the one or more processors. According to some embodiments, the one or more parameters further comprise one or more of a type of the input data; a format of the input data; an offset for reading from the input data; a size of the chunk; a condition for determining the chunk; and a query for deriving the input data.

Datastore mechanism for managing out-of-memory data

A scheduler is provided for tall algorithms that include control flow statements that are dependent on tall computations. Thunks may be defined for the tall algorithms and the boundaries or limits of the thunks may be determined by places where tall variables are required to be gathered. The scheduler executes the tall algorithms by calling individually the thunks from the tall algorithms and pausing when a gather operation is required. The scheduler collectively gathers the tall variables from different tall algorithms by using an optimizer to combine I/O operations for the tall algorithms. The scheduler may put back the gathered variables into the context of each tall algorithm, and the scheduler may resume with the next thunk for each tall algorithm. This process may be repeated until all tall algorithms are finished.

Scheduler for tall-gathering algorithms that include control flow statements

A device may include one or more processors. The device may receive program code that identifies operations to be performed with regard to a data set to determine an output value. The device may identify a plurality of read operations corresponding to the operations. The plurality of read operations may be performed to obtain respective inputs of the operations. The device may generate a first data structure that identifies the operations and the plurality of read operations. The device may identify two or more operations, of the operations, that receive a particular input. The device may generate a second data structure that includes a combined operation. The combined operation may be generated based on the two or more operations, and may be associated with fewer read operations than the two or more operations. The device may determine the output value based on the second data structure.

Big data read-write reduction

According to some embodiments, a method for processing input data comprises creating a MapReducer object corresponding to a MapReduce environment; and receiving, by a MapReduce interface, a plurality of input parameters comprising the input data; a mapper function; a reducer function; and the MapReducer object; and using the MapReduce interface to process the input data by one or more processors in the MapReduce environment using the mapper function and the reducer function. According to some embodiments, the method further comprises creating a second MapReducer object, wherein the second MapReducer object corresponds to a second MapReduce environment; receiving, by the MapReduce interface, the second MapReducer object in place of the first MapReducer object; and utilizing the MapReduce interface to process the input data by the one or more processors in the second MapReduce environment using the mapper function and the reducer function.

Unified mapreduce framework for large-scale data processing

A method for modeling a set of observed data comprises selecting a reference model and an alternative model as possible descriptions of the set of observed data, and storing an index function for measuring fit of models to data. The method further includes performing, by one or more processors, a simulated threshold-fitting for a first of the two models, deriving an initial simulated index for the second model for fitting the second model to the simulated data, and deriving an initial boundary for simulated index difference including calculating a difference between the threshold-fit simulated index for the first model and the initial simulated index for the second model. The method further includes determining, based on a comparison, whether to update a counter used in calculating a simulated p-value, and selecting, based on the simulated p-value, one of the reference and alternative models for modeling the set of observed data.

Methods and systems for early stop simulated likelihood ratio test

An adaptable graphical user interface is provided. The interface may include a setup region to display a group of available solvers to a user and to display parameters for at least one of the group of solvers to the user. The interface may include an options region to display a first group of active options to a user without requiring a user input for the first group of active options, where the first group of active options is determined based on a selected one of the group of solvers. The options region may further display a second group of active options to the user when another one of the solvers is selected by the user, where the second group of active options differs from the first group of active options.

User configured optimizer

A computer-implemented method for a computing environment employing a matrix-based mathematical programming language includes: generating a heterogeneous dataset in the computing environment, where the heterogeneous dataset embodies a plurality of variables for a plurality of subjects, where each element of a respective variable has a same data type, where the variables comprise at least two data types; accessing contents of the heterogeneous dataset with a single command in the computing environment, where the single command is a brace command, a dot command, or a parentheses command; and providing in the computing environment a portion of the heterogeneous dataset based on the single command.

Computer-implemented processing of heterogeneous data

Computer-implemented processing of hetrogeneous data

A method and graphical user interface (GUI) are provided. The method includes generating a file of executable code in a dynamically typed programming language that regenerates computations and results of a graphical user interface (GUI). The GUI includes a data GUI for importing data, viewing data, generating data and generating exclusion sets for data, a fitting GUI for fitting models to data, a plotting GUI for controlling the plotting of data and fitted models, and a display window for previewing a plot.

Generated code from graphical user interface

Growing community of inventors

Carlisle, MA, United States of America

Thomas Lane