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: Systems and methods for automatically decomposing table-structured electronic documents are described. The systems and methods of this invention generally comprise utilizing mathematical relationships, together with textual and positional clues to the mathematical relationships, in a collaborative manner, to derive a mathematical construct of the table-structured document. Embodiments of this invention automatically process a multitude of table-structured documents, thereby eliminating the need for human interaction with such documents in many cases and lowering the costs associated with processing such documents.

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 technique is presented for detecting manipulation in a market. The technique may be used with a web-based market that enables a plurality of traders to transact or to speculate in an item. A record of each transaction between the traders of the market is stored. The transaction records are accessed and processed to identify transactions representative of a potential manipulation of the market. To identify a potential manipulation of the market, the transaction records are processed to identify transactions between a first trading account established by a trader and a second trading account established as an alias trading account. In addition, the transaction records are processed to identify transaction executed as part of a plan of collusion between two or more traders.

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 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 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 visualization technique for directing the attention of analysts to anomalous values of performance measures associated with a target entity is described. A grid of cells is created where each row represents a particular performance metric, and each column a particular time period. For each cell, an anomaly score is calculated associated with the performance metric and time period corresponding to the row and column of the cell. The anomaly score is based on the value of the performance metric for that particular entity for that time period, as well as context data. The context data is selected to represent the historical values of the performance metric for the target entity or the simultaneous performance of peer entities. The anomaly score is calculated using an exceptional statistical technique, and a display characteristic is associated with the value of the anomaly score based upon the range into which the anomaly score falls.

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: 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: Systems and methods for automatically understanding, decomposing, extracting, validating and reformatting unstructured tabular information into intermediate structured representations of the information contained therein are described. No constraints are placed on the origin or format of these documents when originally submitted. Furthermore, no pre-created scripts are required to map the information contained in the submitted documents. The systems and methods of this invention generally comprise obtaining an electronic document, automatically analyzing and understanding the contents of the document, extracting information from the document, categorizing the information, and then creating an intermediate structured representation of the information contained therein. The intermediate structured representations may then be easily converted for use in a myriad of back-end systems.

Abstract: Systems and methods for automatically understanding and decomposing unstructured tabular information are described. No constraints are placed on the origin or format of these documents when originally submitted; the documents may be in an unstructured and/or nonstandard format, and they may be electronic or flat files. The systems and methods of this invention generally comprise obtaining an electronic ASCII-formatted document, analyzing and understanding the contents of the document, and decomposing the information contained in the document, utilizing a variety of algorithms and heuristics to do this. Embodiments of this invention automatically process a multitude of financial documents, thereby eliminating the need for human interaction with such documents in many cases and lowering the costs associated with processing such documents.

Abstract: Systems and methods for automatically decomposing table-structured electronic documents are described. The systems and methods of this invention generally comprise utilizing mathematical relationships, together with textual and positional clues to the mathematical relationships, in a collaborative manner, to derive a mathematical construct of the table-structured document. Embodiments of this invention automatically process a multitude of table-structured documents, thereby eliminating the need for human interaction with such documents in many cases and lowering the costs associated with processing such documents.

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: A digital cockpit allows a cockpit user to “steer” a business in the same manner that a cockpit of an airplane is used to control the airplane. A number of digital cockpit features enable this functionality. For example, the digital cockpit provides an efficient mechanism for providing prompt reporting regarding a business's past behavior, its current behavior, and its projected future behavior. The digital cockpit uses a suite of business models to provide such information. The digital cockpit further includes mechanisms for allowing a user to carry out desired control of the business by making changes to the business's processes and associated systems. Further, the digital cockpit provides a modular design which allows for the efficient plug-in and modification of business models.