Tibco Software Inc.

Cloud Software Group, Inc.

Becton Dickinson and Company Limited

Michael A O'Connell

Daniel Jay Rope

Thomas A Hill

David M Katz

Timothy M Wiles

Giovanni Parmigiani

David J Turner

Merlise Clyde

Jags Ramnarayan

Venkata Jagannath Yellapragada

Gaia Valeria Paolini

Tun-Chieh Hsu

A system for identifying information in high dimensional, low latency streaming data having dynamically evolving data patterns. The system processes, continuously and in real-time, the streaming data. Processing includes filtering the data based on event data to identify diagnostic data points by comparing the event data with an experimental design matrix and performing a modeling operation using the identified diagnostic data points in order to identify efficiently any current and emerging patterns of relationships between at least one outcome variable and predictor variables. The at least one a-priori, pre-designed experimental design matrix is generated based on combinations of the predictor variables and at least one outcome variable. The experimental design matrix is also generated based on at least one of main effects, limitations, constraints, and interaction effects of the predictor variables and combinations.

Adaptive filtering and modeling via adaptive experimental designs to identify emerging data patterns from large volume, high dimensional, high velocity streaming data

A system for generating algorithmic models comprising a function module to generate a desirability function, an automated machine learning module, and a UI module. The desirability function defines a single desirability value based on an algorithmic model accuracy criteria, criteria for algorithmic model quality, criteria for model fidelity, and criteria for the benefits and cost of model deployment. Specific hard and soft constraints regarding these and other user-defined criteria can also be specified by the user. The automated machine learning module generates an algorithmic model by training the algorithmic model against a model data set, identifying the model with the greatest desirability with respect to all criteria as combined via the desirability function. The UI module generates a user interface to display the overall desirability as well as all model criteria configured by the user. The displayed criteria and desirability are selectable and definable.

Multi-method system for optimal predictive model selection

An apparatus for estimating analytics and interactive exploration of big data, stored and/or streaming, using approximate query processing is presented. The apparatus comprises a model constructor and a sampler. The model constructor identifies important predictors variables in big data using feature selection, predictor variables, and outcome variables and partitions the important predictor variables into one or more stratifications based either the identified interactions or identified relationships. The sampler generates a subset of data by querying the big data using a query constructed based on at least one stratification. The subset of data can be fed into an analytics generator. The analytics generator generates analytics data for the outcome variables based on the subset of data and an analytics algorithm and a visualization, e.g. an interactive visualization, comprising the outcome variables, the important predictor variables, the stratification, the subset of data, and the analytics data.

System for rapid interactive exploration of big data

A system and method for analyzing samples, such as biological samples, to accurately and effectively determine the susceptibility of the samples to antimicrobial materials, so as to determine minimum inhibitory concentration (MIC) values for the respective samples and antimicrobial materials. At each of a plurality of time intervals, the system and method directs a plurality of different analyzing light wavelengths, such as red, green and blue wavelengths, onto each of a plurality of sample wells, and detects a respective resultant light wavelength emanating from the respective sample wells for each of the analyzing light wavelengths. The system and method uses resultant light wavelengths to generate at least two growth indicator characteristic curves representing, for example, the redox state and turbidity characteristics of the sample wells. The system then uses the redox state and turbidity characteristics of sample wells containing the same antimicrobial material to determine the MIC value for that material in relation to the sample contained in those wells.

System and method for analyzing antibiotic susceptibility of biological samples using redox and turbidity measurments to ascertain minimum inhibitory concentrations (MICs)

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Durham, NC, United States of America

Michael A O'Connell