Tibco Software Inc.

Other

Cloud Software Group, Inc.

Thomas A Hill

Vladimir S Rastunkov

Daniel Jay Rope

Michael A O'Connell

Pawel Lewicki

George R Butler

David M Katz

Daniel Scott

Steven Hillion

Jags Ramnarayan

Venkata Jagannath Yellapragada

Gaia Valeria Paolini

Tun-Chieh Hsu

Siva Ramalingam

Piotr Smolinski

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 process control tool for processing wide data from automated manufacturing operations. The tool including a feature selector, an analysis server, and a visualization engine. The feature selector receives process input data from at least one manufacturing process application, wherein the process input data includes a plurality of observations and associated variables, converts the received process input data to a stacked format having one row for each variable in each observation, converts identified categorical variables into numerical variables and identified time-series data into fixed numbers of intervals, computes statistics that measure the strengths of relationships between predictor values and an outcome variable, orders, filters, and pivots the predictor values. The analysis server performs at least one operation to identify interactions between predictor values, e.g. using maximum Likelihood computations or predefined searches, in the filtered predictor values. The visualization engine displays the interactions for use in managing the manufacturing operations.

Process control tool for processing big and wide 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, method, and computer-readable medium for performing a variable and interaction detection operation. In certain embodiments, the variable and interaction detection operation detects important variables and as well as interactions among certain important variables in big data. In certain embodiments, the variable and interaction detection operation includes a robust automated process flow (i.e., an analytic recipe) which identifies important variables and their interactions in large data sets.

Detecting important variables and their interactions in big data

A system, method, and computer-readable medium for performing an auto-query construction operation for use with a distributed analytics operation. More specifically, in certain embodiments, the auto-query construction operation provides automatically generates SQL code instructions via an auto-query construction user interface (UI) settings in a computational system, such as the Dell Statistica computational system. The auto-query construction operation allows a user to interact with a common interface to provide query information including decision variables, parameters of an analysis and convergence criteria. The query information provided via the UI is automatically transformed to database queries and subsequent computation system operations. Thus, the user experience remains intact whether the analytics is performed in database or within the computation system.

Auto query construction for in-database predictive analytics

A system, method, and computer-readable medium for performing a distributed analytics operation. The distributed analytics operation uses interface technologies to de-couple an actual data storage technology from an implementation of distributed analytics. Such a distributed analytics operation obviates requirements to deploy specific computer code onto a data storage platform to specifically target that platform for distributed predictive analytics computations.

Method for performing in-database distributed advanced predictive analytics modeling via common queries

A system, method, and computer-readable medium are disclosed performing an optimization operation. The optimization operation optimizes continuous processes by identifying process states associated with specific ranges for a limited subset of control parameter inputs. In certain embodiments, the optimization operation states comprise clear, stable, and robust process states. Such an optimization operation provides a simpler and cost effective means to optimize continuous processes. Additionally, such an optimization operation is applicable more rapidly to a wider range of real-world operational issues as they occur regularly in continuous process scenarios.

Automatic optimization of continuous processes

A system, method, and computer-readable medium for performing in-database operations, comprising: presenting an automation interface to a user, the user interface automation interface enabling a user to select one or more key performance indicators; instantiating an in-database processing operation, the in-database processing operation performing feature selection from a high dimensional parameter space; executing at least one database statement within the storage system to derive a subset of diagnostic parameters from the high dimensional parameter space.

Method for in-database feature selection for high-dimensional inputs

A system, method, and computer-readable medium are disclosed for identifying sources of variation in complex manufacturing processes via a variation identification operation. In certain embodiments, the variation identification operation is performed via a variation identification system. The variation identification operation addresses special class of analytic problems, namely the estimation of variance components and related statistics from very large (big data) hierarchically nested designs of random factors. These types of data structures occur frequently across various industries, and in particular in automated and batch manufacturing where the variability in product quality as measured in final product testing should be related to batches, lots, wafers, suppliers, etc. upstream of the process.

System for rapid identification of sources of variation in complex manufacturing processes

A system, method, and computer-readable medium are disclosed for analysis and characterization of manufacturing information such as process trees or genealogies using graph theory. More specifically, using graph theory to analyze manufacturing information of a manufacturing operation allows for deep analysis of relationships between batches or units in a process tree and their closeness or distance, to identify clusters associated with specific quality characteristics or problems, to identify common antecedents of specifically labeled batches (e.g., problem batches), and/or to detect overall desirable or undesirable characteristics of the process tree (e.g., centrality, etc.).

Graph theory and network analytics and diagnostics for process optimization in manufacturing

A system, method, and computer-readable medium for extracting the samples from big data to extract most information about the relationships of interest between dimensions and variables in the data repository. More specifically, extracting information from large data repositories follows an adaptive process that uses systematic sampling procedures derived from optimal experimental designs to target from a large data set specific observations with information value of interest for the analytic task under consideration. The application of adaptive optimal design to guide exploration of large data repositories provides advantages over known big data technologies.

Adaptive sampling via adaptive optimal experimental designs to extract maximum information from large data repositories

A method for obtaining integral data of the dilution curve obtained when an indicator is injected into a closed system containing a circulating fluid, and as particularly adapted for utilization in blood flow analysis computers. The apparatus is constructed to analyze the characteristic concentration curve in an abbreviated time period to establish the full integral value of the curve. Concentration curve input is integrated through circuitry which detects relative curve change through a Phase I for subsequent summation with a data value summation through a selected portion of the exponential decay curve, that portion identified as Phase II, thereby to predict the remainder of the curve area and provide data output of an integral value for the entire concentration curve above a designated zero or set point level.

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Oklahoma City, OK, United States of America

Thomas A Hill