Abstract: A technique for detecting anomalous values in a small set of financial metrics makes use of context data that is determined based upon the characteristics of the target company being evaluated. Context data is selected to represent the historical values of the financial metric for the target company or the simultaneous performance of peer companies. Using the context data, an anomaly score for the financial metric is calculated representing the degree to which the value of the financial metric is an outlier among the context data. This can be done using an exceptional statistical technique. The anomaly score can be used to evaluate the risks associated with business transactions related to the target company.

Abstract: Electrical data processing techniques are described for performing business analysis based on datasets that are incomplete (e.g., contain censored data) and/or based on datasets that are derived from a stage-based business operation. A first technique offsets the effects of error caused by the incomplete dataset by performing a trending operation followed by a de-trending operation. A second technique provides a model containing multiple sub-models, where the output of one sub-model serves as the input to another sub-model in recursive fashion. A third technique determines when a specified event is likely to occur with respect to a given asset by first discriminating whether the event is very unlikely to occur; if the asset does not meet this initial test, it is further processed by a second sub-model, which determines the probability that the specified event will occur for each of a specified series of time intervals.

Abstract: A method for predicting or detecting an event in turbomachinery includes the steps of obtaining operational data from at least one machine and at least one peer machine. The operational data comprises a plurality of performance metrics. A genetic algorithm (GA) analyzes the operational data, and generates a plurality of clauses, which are used to characterize the operational data. The clauses are evaluated as being either “true” or “false”. A fitness function identifies a fitness value for each of the clauses. A perturbation is applied to selected clauses to create additional clauses, which are then added to the clauses group. The steps of applying a fitness function, selecting a plurality of clauses, and applying a perturbation can be repeated until a predetermined fitness value is reached. The selected clauses are then applied to the operational data from the machine to detect or predict a past, present or future event.

Abstract: A method of identifying a set of entities based on a pattern of interest is provided. The method includes identifying a reference entity and identifying one or more alert categories indicative of a pattern of interest in the reference entity over a time period of interest. The method further comprises determining a matching percentage of the pattern of interest exhibited by the reference entity, in one or more entities comprising the set of entities based on the one or more alert categories. The method further comprises identifying one or more of the entities comprising the set of entities that exhibit one or more of the patterns of interest exhibited by the reference entity, based on the matching percentage.

Abstract: A method for predicting or detecting an event in turbomachinery includes the steps of obtaining operational data from at least one machine and at least one peer machine. The operational data comprises a plurality of performance metrics. A genetic algorithm (GA) analyzes the operational data, and generates a plurality of clauses, which are used to characterize the operational data. The clauses are evaluated as being either “true” or “false”. A fitness function identifies a fitness value for each of the clauses. A perturbation is applied to selected clauses to create additional clauses, which are then added to the clauses group. The steps of applying a fitness function, selecting a plurality of clauses, and applying a perturbation can be repeated until a predetermined fitness value is reached. The selected clauses are then applied to the operational data from the machine to detect or predict a past, present or future event.

Abstract: A method for determining whether an operational metric representing the performance of a target machine has an anomalous value is provided. The method includes collecting operational data from at least one machine, and calculating at least one exceptional anomaly score from the obtained operational data.

Abstract: A method for aggregating anomalous values is provided. The method comprises obtaining operational data from at least one machine and calculating at least one exceptional anomaly score from the operational data. The exceptional anomaly scores can then be aggregated to identify acute or chronic anomalous values.

Abstract: A method for monitoring engine performance includes sampling exhaust gas temperature associated with a turbine engine over an interval of operational time of the turbine engine. The method further includes applying a first test to identify statistical outliers on the sampled exhaust gas temperature data and removing identified statistical outliers from the sampled exhaust gas temperature data. Subsequently, the method includes applying a second test to identify step changes in slope of the exhaust gas temperature data and dividing the interval of operational time into one or more segments based upon the identified step changes. Finally, the method includes determining a slope for each segment and combining the segments to obtain a rate of performance deterioration of the turbine engine.

Abstract: A method for predicting the financial health of a business entity is provided. The method comprises generating one or more anomaly scores and one or more multi-dimensional time-varying patterns for one or more financial metrics related to a business entity and analyzing the one or more anomaly scores and the one or more multi-dimensional time-varying patterns for the one or more financial metrics, using a dynamic predictive modeling system. The method then comprises predicting one or more business behavioral patterns related to the business entity based on the step of analyzing and aggregating the one or more predicted business behavioral patterns in a selected manner to predict the financial health of the business entity.

Abstract: A technique is provided for analyzing a dataset. The technique includes generating multivariate parameters to capture statistical patterns over time and/or across dimensions in the dataset, and developing a dynamic model based on the multivariate parameters for analyzing the dataset.

Abstract: A technique for detecting anomalous values in a small set of financial metrics makes use of context data that is determined based upon the characteristics of the target company being evaluated. Context data is selected to represent the historical values of the financial metric for the target company or the simultaneous performance of peer companies. Using the context data, an anomaly score for the financial metric is calculated representing the degree to which the value of the financial metric is an outlier among the context data. This can be done using an exceptional statistical technique. The anomaly score can be used to evaluate the risks associated with business transactions related to the target company.

Abstract: A method and system for detecting business behavioral patterns related to a business entity is provided. The method comprises determining a model for business behavioral patterns in which the likelihood of a particular business behavioral pattern is associated with the occurrence of a qualitative event and a quantitative metric. The method further comprises extracting a first data set from a first data source and a second data set from a second data source. The first data set represents the occurrence of the qualitative event associated with the business entity. The second data set represents the quantitative metric associated with the business entity. Then a first confidence attribute and a first temporal attribute associated with the qualitative event is determined. Similarly, a second confidence attribute and a second temporal attribute associated with the quantitative metric are determined.

Abstract: System, method and computer product to detect behavioral patterns related to the financial health of a business entity. In this invention, financial data and business data that relate to the business entity is extracted from various data sources. The financial data comprises quantitative financial data and/or qualitative financial data. The business data comprises quantitative business data and/or qualitative business data. The quantitative financial and business data is analyzed using a financial anomaly detection technique to detect the behavioral patterns associated with the business entity.

Abstract: Electrical data processing techniques are described for performing business analysis based on datasets that are incomplete (e.g., contain censored data) and/or based on datasets that are derived from a stage-based business operation. A first technique offsets the effects of error caused by the incomplete dataset by performing a trending operation followed by a de-trending operation. A second technique provides a model containing multiple sub-models, where the output of one sub-model serves as the input to another sub-model in recursive fashion. A third technique determines when a specified event is likely to occur with respect to a given asset by first discriminating whether the event is very unlikely to occur; if the asset does not meet this initial test, it is further processed by a second sub-model, which determines the probability that the specified event will occur for each of a specified series of time intervals.

Abstract: A process for developing a model and integrating the model into a business intelligence system includes: (a) defining at least one variable X to serve as an input to the model and at least one output variable Y to serve as an output of the model; (b) assessing whether there is sufficient data of sufficient quality to operate the model in the business intelligence system of the business, and creating a prototype design of the model; (c) further developing the prototype design of the model to produce a final model design, and validating output results provided by the final model design; (d) implementing the final model design to produce an implemented model, and developing an interface that enables a user to interact with the implemented model; and (e) integrating the implemented model and associated interface into the business intelligence system to provide an integrated model, and repetitively monitoring the accuracy of output results provided by the integrated model.

Abstract: Statistical models for quantifying and predicting human perception of scratches on automotive components are disclosed. Such models may be created by utilizing quantitative two-step moving scale surveys. Such surveys employ a continuous scale to model human perception and allow response bias and measurement error in survey data to be evaluated. Once survey data is collected, relationships between the visual perception of the scratches and the measurable optical properties associated with the scratches can be determined. Additionally, relationships between the visual perception of the scratches and the actual physical scratch dimensions can be determined. Thereafter, models for predicting the human perception of such scratches can be created therefrom. Since these models predict the results of such surveys, the need for repeatedly collecting survey data is eliminated. These models may also be used for predicting human perception of other items of interest.

Abstract: An apparatus for estimating DC motor brush wear comprising: a wear element calculator adapted to calculate a plurality of wear elements from at least one environmental variable; a wear element multiplier adapted to multiply the wear elements by respective ones of a plurality of wear coefficients to yield a plurality of weighted wear elements; and a summer adapted to sum the weighted wear elements to yield a brush wear estimate.