Bigml, Inc.

J Justin Donaldson

Charles Austin Parker

Francisco J Martin

Adam Ashenfelter

Jose Antonio Ortega

Jos Verwoerd

Poul Petersen

Oscar Rovira

Beatriz Garcia

Antonio Blasco

A system and method enables users to selectively expose and optionally monetize their data resources, for example on a web site. Data assets such as datasets and models can be exposed by the proprietor on a public gallery for use by others. Fees may be charged, for example, per new model, or per prediction using a model. Users may selectively expose public datasets or public models while keeping their raw data private.

Predictive modeling and data analysis in a secure shared system

We describe a high-level computational framework especially well suited to parallel operations on large datasets. In a system in accordance with this framework, there is at least one, and generally several, instances of an architecture deployment as further described. We use the term 'architecture deployment' herein to mean a cooperating group of processes together with the hardware on which the processes are executed. This is not to imply a one-to-one association of any process to particular hardware. To the contrary, as detailed below, an architecture deployment may dynamically spawn another deployment as appropriate, including provisioning needed hardware. The active architecture deployments together form a system that dynamically processes jobs requested by a user-customer, in accordance with customer's monetary budget and other criteria, in a robust and automatically scalable environment.

Evolving parallel system to automatically improve the performance of multiple concurrent tasks on large datasets

Systems and methods are disclosed for building and using decision trees, preferably in a scalable and distributed manner. Our system can be used to create and use classification trees, regression trees, or a combination of regression trees called a gradient boosted regression tree (GBRT). Our system leverages approximate histograms in new ways to process large datasets, or data streams, while limiting inter-process communication bandwidth requirements. Further, in some embodiments, a scalable network of computers or processors is utilized for fast computation of decision trees. Preferably, the network comprises a tree structure of processors, comprising a master node and a plurality of worker nodes or 'workers,' again arranged to limit necessary communications.

Methods for building regression trees in a distributed computing environment

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Corvallis, OR, United States of America

J Justin Donaldson