Abstract: In one embodiment, the invention provides a system including at least one computing device for enhancing the recovery of heavy oil in an underground, near-surface crude oil extraction environment by performing a method comprising sampling and identifying microbial species (bacteria and/or fungi) that reside in the underground, near-surface crude oil extraction environment; collecting rock and fluid property data from the underground, near-surface crude oil extraction environment; collecting nutrient data from the underground, near-surface crude oil extraction environment; identifying a preferred microbial species from the underground, near-surface crude oil extraction environment that can transform the heavy oil into a lighter oil; identifying a nutrient from the underground, near-surface crude oil extraction environment that promotes a proliferation of the preferred microbial species; and introducing the nutrient into the underground, near-surface crude oil extraction environment.

Abstract: In one embodiment, the invention provides an analytical processing system for enhancing the recovery of heavy oil in an underground, near-surface crude oil extraction environment, comprising: a data collection engine configured for receiving data relating to the underground, near-surface crude oil extraction environment, wherein the data includes in situ microbial species data, oil properties data and in situ nutrient information that are identified as being germane to the underground, near-surface crude oil extraction environment; a data warehouse for storing data collected from the data collection engine; and a data modeling system for analyzing data stored in the data warehouse and facilitating an identity of a preferred microbial species from the underground, near-surface crude oil extraction environment that can transform the heavy oil into a lighter oil.

Abstract: A supercomputer processing system is provided that is configured to execute a plurality of simulations through transaction processing. The supercomputer processing system includes a supercomputer configured to execute a first simulation of the plurality of simulations and generate an output based upon execution of the first simulation, and a transaction hub. The transaction hub includes a relational database configured to store the output of the first simulation, and an application server having a service-oriented architecture (SOA) that supports an event triggering service. The event triggering service is configured to detect the output of the first simulation and automatically trigger the supercomputer to execute a second simulation of the plurality of simulations using the output of the first simulation stored in the relational database.

Abstract: A system and method for interpreting electrocardiogram data. A system is provided that clusters raw electrocardiogram (EKG) data into clusters of EKG data; generates a predictive model for each cluster of EKG data; compares inputted patient EKG data with the clusters of EKG data to identify a matching cluster of EKG data; applies the predictive model associated with the matching cluster of EKG data to the inputted patient EKG data; and outputs diagnostic data.

Abstract: In one embodiment, the invention provides an analytical processing system for enhancing the recovery of heavy oil in an underground, near-surface crude oil extraction environment, comprising: a data collection engine configured for receiving data relating to the underground, near-surface crude oil extraction environment, wherein the data includes in situ microbial species data, oil properties data and in situ nutrient information that are identified as being germane to the underground, near-surface crude oil extraction environment; a data warehouse for storing data collected from the data collection engine; and a data modeling system for analyzing data stored in the data warehouse and facilitating an identity of a preferred microbial species from the underground, near-surface crude oil extraction environment that can transform the heavy oil into a lighter oil.

Abstract: A system and method for enhancing the recovery of heavy oil in an oil extraction environment by feeding nutrients to a preferred microbial species (bacteria and/or fungi). A method is described that includes the steps of: sampling and identifying microbial species that reside in the oil extraction environment; collecting fluid property data from the oil extraction environment; collecting nutrient data from the oil extraction environment; identifying a preferred microbial species from the oil extraction environment that can transform the heavy oil into a lighter oil; identifying a nutrient from the oil extraction environment that promotes a proliferation of the preferred microbial species; and introducing the nutrient into the oil extraction environment.

Abstract: A method and system for predicting customer behavior based on the geography of a data network are provided. Furthermore, a method and system for evaluating the training of a predictive algorithm to determine if the algorithm does not adequately take into consideration the influences of data network geography are also provided. The method and system generate frequency distributions of a customer database data set, training data set and testing data set and compare the frequency distributions of data network geographical characteristics to determine if there are discrepancies. If the discrepancies are above a predetermined tolerance, one or more of the data sets may not be representative of the customer database taking into account data network geographical influences on customer behavior. Thus, recommendations for improving the training data set and/or testing data set are then provided such that the data set is more representative of the data network geographical influences.

Abstract: A supercomputer processing system is provided that is configured to execute a plurality of simulations through transaction processing. The supercomputer processing system includes a supercomputer configured to execute a first simulation of the plurality of simulations and generate an output based upon execution of the first simulation, and a transaction hub. The transaction hub includes a relational database configured to store the output of the first simulation, and an application server having a service-oriented architecture (SOA) that supports an event triggering service. The event triggering service is configured to detect the output of the first simulation and automatically trigger the supercomputer to execute a second simulation of the plurality of simulations using the output of the first simulation stored in the relational database.

Abstract: A method and system for sample data selection to test and train predictive algorithm of customer behavior are provided. The method and system generate frequency distributions of a customer database data set, training data set and testing data set and compare the frequency distributions of geographical characteristics to determine if there are discrepancies. If the discrepancies are above a predetermined tolerance, one or more of the data sets may not be representative of the customer database taking into account geographical influences on customer behavior. Thus, recommendations for improving the training data set and/or testing data set are then provided such that the data set is more representative of the customer database. In this way, “nuggeting” of customers is accounted for in the training and/or testing data sets.

Abstract: An enhanced concern indicator failure prediction system is provided to predict possible product failures with automatic notification of people as well as systems. The system integrates data mining, spatial analysis, linear programming, narrowcasting, data warehousing, visualization, and text mining. As a result, failure conditions, attributes, complaints, locations, consequences, and sequence of events are analyzed using data mining technologies. This data is fed into an optimization module that assesses the efficiency of the failure process such that failures can be assessed as to their priority. These priorities are then used to feed a triggering engine that triggers notification of systems and individuals using narrowcasting technology. This system is one that allows early warning of potential problems to occur and integrates data from call centers, legacy systems, retailers, manufacturers, vendor supplied parts, and transportation of parts.

Abstract: An enhanced concern indicator failure prediction system is provided to predict possible product failures with automatic notification of people as well as systems. The system integrates data mining, spatial analysis, linear programming, narrowcasting, data warehousing, visualization, and text mining. As a result, failure conditions, attributes, complaints, locations, consequences, and sequence of events are analyzed using data mining technologies. This data is fed into an optimization module that assesses the efficiency of the failure process such that failures can be assessed as to their priority. These priorities are then used to feed a triggering engine that triggers notification of systems and individuals using narrowcasting technology. This system is one that allows early warning of potential problems to occur and integrates data from call centers, legacy systems, retailers, manufacturers, vendor supplied parts, and transportation of parts.

Abstract: A method and system for ascertaining the favorable positioning of products in a retail environment is provided. The locations of products within a retail space are determined using a position identifying system, such as a global positioning system, a local positioning system, or an enhanced global positioning system as products are stocked within the retail space. The paths of customers through the retail space are also determined using the position identifying system, and these paths may be sensed and recorded using a device that stores a position identifier broadcast by the position identifying system. Customers may be identified using financial transaction databases or other identifying data. The products chosen for purchase by the customers are identified, and the locations of the chosen products within the retail space are associated with the paths of the customers through the retail space to form a set of spatial relationships.

Abstract: A method and system for predicting customer behavior based on the geography of a data network are provided. Furthermore, a method and system for evaluating the training of a predictive algorithm to determine if the algorithm does not adequately take into consideration the influences of data network geography are also provided. The method and system generate frequency distributions of a customer database data set, training data set and testing data set and compare the frequency distributions of data network geographical characteristics to determine if there are discrepancies. If the discrepancies are above a predetermined tolerance, one or more of the data sets may not be representative of the customer database taking into account data network geographical influences on customer behavior. Thus, recommendations for improving the training data set and/or testing data set are then provided such that the data set is more representative of the data network geographical influences.

Abstract: A method and system for ascertaining warranty issues associated with transportation products is provided. Transportation products are manufactured at a factory, and as the products are made, historical information concerning significant events related to the manufacturing process may be stored. As the transportation product is delivered to a retail outlet, other significant event information may be stored. Locations are determined within a region for warranty claims related to the servicing of selected transportation products. Product history information for the selected transportation products may be retrieved from one or more databases. Data mining algorithms may be employed to generate input data for forming a set of spatial relationships. The locations of products within the region are then associated with the product history information to form a set of spatial relationships.

Abstract: A method and system for sample data selection to test and train predictive algorithm of customer behavior are provided. The method and system generate frequency distributions of a customer database data set, training data set and testing data set and compare the frequency distributions of geographical characteristics to determine if there are discrepancies. If the discrepancies are above a predetermined tolerance, one or more of the data sets may not be representative of the customer database taking into account geographical influences on customer behavior. Thus, recommendations for improving the training data set and/or testing data set are then provided such that the data set is more representative of the customer database. In this way, “nuggeting” of customers is accounted for in the training and/or testing data sets.

Abstract: A method and system for ascertaining relationships between collected geological samples and remotely sensed geological data within a region is provided. The locations of physical samples and the locations of data points associated with remotely sensed data are identified and stored. The locations within the region are determined using a position identifying system, such as the global positioning system. The collected physical samples are analyzed to obtain data values for physical and chemical properties of the collected samples. Data mining algorithms are used to generate input data for the formulation of a set of spatial relationships. The locations of the physical samples within the region are associated with the locations of the remotely sensed data to form a set of spatial relationships.