Abstract: In an approach for extracting geospatial temporal facts and events, a processor receives a set of structured data and a set of unstructured data. A processor extracts a first set of temporal information and a first set of geospatial information from the set of unstructured data. A processor identifies a second set of temporal information and a second set of geospatial information from the set of structured data. A processor determines that the set of structured data and the set of unstructured data are related, based on at least the first set of temporal information, the second set of temporal information, the first set of geospatial information, and the second set of geospatial information. A processor groups the set of structured data and the set of unstructured data into a collective set of data. A processor stores the collective set of data.

Abstract: A method for identifying a resource in a field using historic well data including vertical well logs for the resource and historic horizontal well production data for the resource, the method including extracting a plurality of features from the vertical well logs, performing a spatial interpolation of the plurality of features extracted from the vertical well logs onto coordinates of the horizontal well production data to determine a plurality of interpolated features, and building a model predicting production of the resource in the field by regressing the horizontal well production data onto the interpolated features, wherein the model is displayed as a visualization of the resource production predicted in the field.

Abstract: A population comparison system, method and a computer program product. A stored list of population members, e.g., hydrocarbon reservoirs, includes parameters for corresponding known characteristics and analogous members for each member. A new population member input receives new member descriptions including parameters for each respective new member. A parameter extraction system automatically extracts an estimated value for each missing key parameter, providing a supplemented description. An analogous member selector automatically selects a subset of listed population members as analogous members for each new population member responsive to the supplemented description. The analogous members serve as a basis for uncertainty characterization from the joint parameter distribution and univariate distributions for each parameter.

Abstract: A population comparison system, method and a computer program product therefor. A stored list of population members, e.g., hydrocarbon reservoirs, includes parameters for corresponding known characteristics and analogous members for each member. A new population member input receives new member descriptions including parameters for each respective new member. A parameter extraction system automatically extracts an estimated value for each missing key parameter, providing a supplemented description. An analogous member selector automatically selects a subset of listed population members as analogous members for each new population member responsive to the supplemented description.

Abstract: A population comparison system, method and a computer program product therefor. A stored list of population members, e.g., hydrocarbon reservoirs, characteristics and analogous members is partitioned into lists for each member. A weighting system automatically uses the partitions to determine a weight set (w*) for population member characteristic and a similarity function. The weighting system may include an objective model that iteratively, blindly identifies analogous members for each population member until the identified analogous members match the listed analogous members. An analogous member selector uses the weights set (w*) and similarity function to automatically select analogous listed members for each new population member.

Abstract: A population comparison system, method and a computer program product therefor. A stored list of population members, e.g., hydrocarbon reservoirs, includes parameters for corresponding known characteristics and analogous members for each member. A new population member input receives new member descriptions including parameters for each respective new member. A parameter extraction system automatically extracts an estimated value for each missing key parameter, providing a supplemented description. An analogous member selector automatically selects a subset of listed population members as analogous members for each new population member responsive to the supplemented description.

Abstract: A population comparison system, method and a computer program product. A stored list of population members, e.g., hydrocarbon reservoirs, includes parameters for corresponding known characteristics and analogous members for each member. A new population member input receives new member descriptions including parameters for each respective new member. A parameter extraction system automatically extracts an estimated value for each missing key parameter, providing a supplemented description. An analogous member selector automatically selects a subset of listed population members as analogous members for each new population member responsive to the supplemented description. The analogous members serve as a basis for uncertainty characterization from the joint parameter distribution and univariate distributions for each parameter.

Abstract: A travel routing system, method and program product therefor. A location detector detects a current location. A geographical database provides details of a given area. Selecting a destination causes a route generator to generate routes through the area from the current location. A flood simulator receives meteorological data and determines flooding along the routes. A risk-modeling unit determines the risk to travelers of using each route. Before the risk-modeling unit is deployed, it is trained off-line to model travel risks using incidents in an incident data store and simulated flooding in the vicinity of the incidents.

Abstract: A method and system perform ranking-based evaluations for regression models that are often appropriate for marketing tasks and are more robust to outliers than traditional residual-based performance measures. The output provided by the method and system provides visualization that can offer insights about local model performance and outliers. Several models can be compared to each other to identify the “best” model and, therefore, the “best” model data for the particular marketing task.

Abstract: Methods for multi-class cost-sensitive learning are based on iterative example weighting schemes and solve multi-class cost-sensitive learning problems using a binary classification algorithm. One of the methods works by iteratively applying weighted sampling from an expanded data set, which is obtained by enhancing each example in the original data set with as many data points as there are possible labels for any single instance, using a weighting scheme which gives each labeled example the weight specified as the difference between the average cost on that instance by the averaged hypotheses from the iterations so far and the misclassification cost associated with the label in the labeled example in question. It then calls the component classification algorithm on a modified binary classification problem in which each example is itself already a labeled pair, and its (meta) label is 1 or 0 depending on whether the example weight in the above weighting scheme is positive or negative, respectively.

Abstract: A method and system perform ranking-based evaluations for regression models that are often appropriate for marketing tasks and are more robust to outliers than traditional residual-based performance measures. The output provided by the method and system provides visualization that can offer insights about local model performance and outliers. Several models can be compared to each other to identify the “best” model and, therefore, the “best” model data for the particular marketing task.

Abstract: Methods for multi-class cost-sensitive learning are based on iterative example weighting schemes and solve multi-class cost-sensitive learning problems using a binary classification algorithm. One of the methods works by iteratively applying weighted sampling from an expanded data set, which is obtained by enhancing each example in the original data set with as many data points as there are possible labels for any single instance, using a weighting scheme which gives each labeled example the weight specified as the difference between the average cost on that instance by the averaged hypotheses from the iterations so far and the misclassification cost associated with the label in the labeled example in question. It then calls the component classification algorithm on a modified binary classification problem in which each example is itself already a labeled pair, and its (meta) label is 1 or 0 depending on whether the example weight in the above weighting scheme is positive or negative, respectively.

Abstract: A method and system perform ranking-based evaluations for regression models that are often appropriate for marketing tasks and are more robust to outliers than traditional residual-based performance measures. The output provided by the method and system provides visualization that can offer insights about local model performance and outliers. Several models can be compared to each other to identify the “best” model and, therefore, the “best” model data for the particular marketing task.

Abstract: Methods for multi-class cost-sensitive learning are based on iterative example weighting schemes and solve multi-class cost-sensitive learning problems using a binary classification algorithm. One of the methods works by iteratively applying weighted sampling from an expanded data set, which is obtained by enhancing each example in the original data set with as many data points as there are possible labels for any single instance, using a weighting scheme which gives each labeled example the weight specified as the difference between the average cost on that instance by the averaged hypotheses from the iterations so far and the misclassification cost associated with the label in the labeled example in question. It then calls the component classification algorithm on a modified binary classification problem in which each example is itself already a labeled pair, and its (meta) label is 1 or 0 depending on whether the example weight in the above weighting scheme is positive or negative, respectively.