Abstract: In one example, a system can receive information about a tabular data structure in a memory including a set of data and a first memory allocation. The system can determine a type of the tabular data structure, the type selected from among two types including a native type and a non-native type. The system can, in response to the type being the native type, identify a first proxy data table usable as a proxy for the tabular data structure that shares the first memory allocation. The system can receive a first indication to access the set of data from application code. The system can issue one or more first read commands to the first proxy data table to cause the set of data to be read from the tabular data structure.

Abstract: In one example, a system can receive, from application code including an analysis operation performed on a set of data, an indication to access the set of data included in a tabular data structure using an application programming interface (API), in which the tabular data structure is associated with a memory allocation and a type. The system can determine that the type of the tabular data structure is the native type, the native type characterizing data structures that are accessed using a first programming language and a second programming language. The system can identify a proxy data table that shares the memory allocation, the proxy data table accessed using the API based on the second programming language. The system can issue one or more read commands to the proxy data table to cause the set of data to be read from the tabular data structure.

Abstract: In one example, a system can receive information about a tabular data structure in a memory including a set of data and a first memory allocation. The system can determine a type of the tabular data structure, the type selected from among two types including a native type and a non-native type. The system can, in response to the type being the native type, identify a first proxy data table usable as a proxy for the tabular data structure that shares the first memory allocation. The system can receive a first indication to access the set of data from application code. The system can issue one or more first read commands to the first proxy data table to cause the set of data to be read from the tabular data structure.

Abstract: In one example, a system can receive, from application code including an analysis operation performed on a set of data, an indication to access the set of data included in a tabular data structure using an application programming interface (API), in which the tabular data structure is associated with a memory allocation and a type. The system can determine that the type of the tabular data structure is the native type, the native type characterizing data structures that are accessed using a first programming language and a second programming language. The system can identify a proxy data table that shares the memory allocation, the proxy data table accessed using the API based on the second programming language. The system can issue one or more read commands to the proxy data table to cause the set of data to be read from the tabular data structure.

Abstract: In one example, a system can receive information about a data structure including a set of data entries. The system can generate a proxy data table including a set of columns. The system can use a data access layer to generate a mapping from the data entries to the columns. The system can receive an input to cause an operation to be performed on the data structure by performing the operation on the data structure. Generating a result can involve issuing read commands to the data access layer to perform the operation on the data structure such that the data access layer obtains the associated data entries and provides them as responses to the read commands by performing a translation between the data entries and the columns based on the mapping. The system can then output the result of the operation.

Abstract: Parameter values in source code can be automatically validated using the techniques described herein. For example, a system can receive source code that includes a call to an action. The action can have a parameter that is set to a selected value in the source code. The parameter can be defined in definition data. The system can also receive a file that separate from the source code and includes metadata for the parameter. The system can extract the metadata from the file and modify the definition data to include the metadata. The system can then execute a validation process on the selected value for the parameter. The validation process can involve retrieving the metadata from the modified definition data, evaluating the selected value using the metadata to determine whether the selected value is invalid, and if it is invalid, outputting an error notification indicating that the selected value is invalid.

Abstract: A method of converting data to tree data is provided. A first node memory structure that includes a first value indicator, a first counter value, and a first observation indicator is initialized for a first variable. The first value indicator is initialized with a first value of the first variable selected from first observation data, and the first observation indicator is initialized with a first indicator that indicates the first observation data. The first value of the first variable is compared to a second value of the first variable. The first counter value included in the first node memory structure is incremented when the first value of the first variable matches the second value of the first variable. Corresponding values of second observation data are compared to the identified values from first observation data when the first value of the first variable matches the second value of the first variable. A next observation is read from the data when the identified values match the corresponding values.

Abstract: A method of creating a contingency table is provided. Whether or not a variable level list exists for a second variable in tree data is determined. When the variable level list exists for the second variable in the tree data, a first node memory structure is determined for the second variable from the variable level list, a first value of a first variable is determined using a first observation indicator and the tree data, and a first counter value is added to the contingency table in association with the first value of the first variable and a first value of the second variable. The first node memory structure includes the first value indicator, the first counter value, and the first observation indicator. The first value indicator indicates a first value of the second variable.

Abstract: Computer-implemented systems and methods are provided for predicting outputs. Global output fractions associated with an object are approximated. Outputs for a group are predicted based upon a cyclical aspect component and a movement prediction. An output prediction is calculated based upon the predicted outputs for a related object group and the approximated global output fraction for a particular object.

Abstract: A method of converting data to tree data is provided. A first node memory structure that includes a first value indicator, a first counter value, and a first observation indicator is initialized for a first variable. The first value indicator is initialized with a first value of the first variable selected from first observation data, and the first observation indicator is initialized with a first indicator that indicates the first observation data. The first value of the first variable is compared to a second value of the first variable. The first counter value included in the first node memory structure is incremented when the first value of the first variable matches the second value of the first variable. Corresponding values of second observation data are compared to the identified values from first observation data when the first value of the first variable matches the second value of the first variable. A next observation is read from the data when the identified values match the corresponding values.

Abstract: A method of creating a contingency table is provided. Whether or not a variable level list exists for a second variable in tree data is determined. When the variable level list exists for the second variable in the tree data, a first node memory structure is determined for the second variable from the variable level list, a first value of a first variable is determined using a first observation indicator and the tree data, and a first counter value is added to the contingency table in association with the first value of the first variable and a first value of the second variable. The first node memory structure includes the first value indicator, the first counter value, and the first observation indicator. The first value indicator indicates a first value of the second variable.

Abstract: A method of selecting a one-class support vector machine (SVM) model for incremental response modeling is provided. Exposure group data generated from first responses by an exposure group receiving a request to respond is received. Control group data generated from second responses by a control group not receiving the request to respond is received. A response is either positive or negative. A one-class SVM model is defined using the positive responses in the control group data and an upper bound parameter value. The defined one-class SVM model is executed with the identified positive responses from the exposure group data. An error value is determined based on execution of the defined one-class SVM model. A final one-class SVM model is selected by validating the defined one-class SVM model using the determined error value.

Abstract: Computer-implemented systems and methods are provided to perform accuracy analysis with respect to forecasting models, wherein the forecasting models provide predictions based upon a pool of production data. As an example, a forecast accuracy monitoring system is provided to monitor the accuracy of the forecasting models over time based upon the pool of production data. A forecast model construction system builds and rebuilds the forecasting models based upon the pool of production data.

Abstract: Computer-implemented systems and methods are provided for forecasting product sales. Market shares associated with a product are estimated. Sales for a share group are forecast based upon a seasonality component and a trend prediction. A product sales forecast is calculated based upon the forecasted sales for a share group and the estimated product market share.

Abstract: Computer-implemented systems and methods are provided to perform accuracy analysis with respect to forecasting models, wherein the forecasting models provide predictions based upon a pool of production data. As an example, a forecast accuracy monitoring system is provided to monitor the accuracy of the forecasting models over time based upon the pool of production data. A forecast model construction system builds and rebuilds the forecasting models based upon the pool of production data.