Abstract: Disclosed is a system, a method, and/or a device of automatic fitting and/or proposal of prediction models to data entries of a spreadsheet file representative of a larger dataset. In one embodiment, a system for automatic determination of a predictive model for scaled data analysis includes two or more servers that process a spreadsheet file including data from a dataset, each data entry of the spreadsheet file including one or more independent variables in one or more cells and a dependent variable. The system automatically determines the predictive model fits a data entry of the spreadsheet. The system proposes an algorithm in response to a fitting of the predictive model, the algorithm accepting as inputs the one or more independent variables and outputting the dependent variable. The system applies the algorithm against the dataset utilizing parallel processing to generate the dependent variable for each data entry of the dataset.

Abstract: Disclosed are a method, a device, a system and/or a manufacture of machine learning selection and/or application of a data model defined in a spreadsheet. In one embodiment, a method of spreadsheet data analysis utilizing machine learning includes processing a spreadsheet file comprising a formula algorithm to be applied to a dataset, including spreadsheet formulas stored in a first set of one or more cells. Generating from the formula algorithm may be an extrapolated algorithm, expressed in a programming language. The method runs an automatic machine learning process to automatically apply one or more predictive models to the dataset, determines a predictive model of the one or more predictive models fits the dataset, and modifies the extrapolated algorithm in response to an application of the one or more predictive models to the dataset to result in a modified extrapolated algorithm.

Abstract: Disclosed are a method, a device, a system and/or a manufacture of machine learning selection and/or application of a data model defined in a spreadsheet. In one embodiment, a method of spreadsheet data analysis utilizing machine learning includes processing a spreadsheet file comprising a formula algorithm to be applied to a dataset, including spreadsheet formulas stored in a first set of one or more cells. Generating from the formula algorithm may be an extrapolated algorithm, expressed in a programming language. The method runs an automatic machine learning process to automatically apply one or more predictive models to the dataset, determines a predictive model of the one or more predictive models fits the dataset, and modifies the extrapolated algorithm in response to an application of the one or more predictive models to the dataset to result in a modified extrapolated algorithm.

Abstract: Disclosed are a method, a device, a system and/or a manufacture of iterative development and/or scalable deployment of a spreadsheet-based formula algorithm. In one embodiment, a system for scalable application of a data model defined in a spreadsheet to a dataset includes a translation server and an execution server. The translation server receives a spreadsheet file including a formula algorithm. The formula algorithm includes one or more spreadsheet formulas stored in cells. The translation server generates an extrapolated algorithm expressed in a programming language based on the formula algorithm. The execution server receives the extrapolated algorithm and the dataset and verifies calculation independence when applied to a data entry. The extrapolated algorithm is applied against the dataset. An iteration engine may continuously reapply the extrapolated algorithm to update an output data as the dataset evolves and/or receive an update to the formula algorithm and reapply the extrapolated algorithm.

Abstract: Disclosed are a method, a device, a system and/or a manufacture of iterative development and/or scalable deployment of a spreadsheet-based formula algorithm. In one embodiment, a system for scalable application of a data model defined in a spreadsheet to a dataset includes a translation server and an execution server. The translation server receives a spreadsheet file including a formula algorithm. The formula algorithm includes one or more spreadsheet formulas stored in cells. The translation server generates an extrapolated algorithm expressed in a programming language based on the formula algorithm. The execution server receives the extrapolated algorithm and the dataset and verifies calculation independence when applied to a data entry. The extrapolated algorithm is applied against the dataset. An iteration engine may continuously reapply the extrapolated algorithm to update an output data as the dataset evolves and/or receive an update to the formula algorithm and reapply the extrapolated algorithm.

Abstract: Disclosed are a method, a device, a system and/or a manufacture of application of a spreadsheet formula algorithm against a dataset such as a large external data source. In one embodiment, a scalable method of analyzing data includes generating a prototype data through importing a data entry from the dataset and mapping to cells of a spreadsheet file that may be accessible as a software-as-a-service. A data model for analyzing the dataset is defined through a spreadsheet algorithm comprising spreadsheet formulas outputting a dependent variable. The spreadsheet formulas, with one or more independent variables as inputs, are stored in a syntax format permitting independent calculation of the dependent variable. An extrapolated algorithm expressed in a programming language that may include SQL is generated from the formula algorithm and applied against the dataset utilizing parallel processing to generate a value for the dependent variable of each data entry of the dataset.

Abstract: Disclosed are a method, a device, a system and/or a manufacture of application of a spreadsheet formula algorithm against a dataset such as a large external data source. In one embodiment, a scalable method of analyzing data includes generating a prototype data through importing a data entry from the dataset and mapping to cells of a spreadsheet file that may be accessible as a software-as-a-service. A data model for analyzing the dataset is defined through a spreadsheet algorithm comprising spreadsheet formulas outputting a dependent variable. The spreadsheet formulas, with one or more independent variables as inputs, are stored in a syntax format permitting independent calculation of the dependent variable. An extrapolated algorithm expressed in a programming language that may include SQL is generated from the formula algorithm and applied against the dataset utilizing parallel processing to generate a value for the dependent variable of each data entry of the dataset.

Abstract: Disclosed are a method, a device, a system and/or a manufacture of data analysis and prediction of a dataset through algorithm extrapolation from a spreadsheet formula. In one embodiment, a method extracts one or more spreadsheet formulas from one or more cells of a spreadsheet file and assembles a formula algorithm. The formula algorithm accepts a set of data entries comprising one or more independent variables, and outputs a prediction metric as a dependent variable such that each data entry is calculation independent. An extrapolated algorithm expressed in a programming language is generated. A computation block of a dataset is specified and submitted for computation along with the extrapolated algorithm over a network. The dataset comprising two or more data entries usable as an input to the extrapolated algorithm. An output data re-combined from the first output block and one or more additional output blocks is received.

Abstract: Disclosed are a method, a device, a system and/or a manufacture of data analysis and prediction of a dataset through algorithm extrapolation from a spreadsheet formula. In one embodiment, a method extracts one or more spreadsheet formulas from one or more cells of a spreadsheet file and assembles a formula algorithm. The formula algorithm accepts a set of data entries comprising one or more independent variables, and outputs a prediction metric as a dependent variable such that each data entry is calculation independent. An extrapolated algorithm expressed in a programming language is generated. A computation block of a dataset is specified and submitted for computation along with the extrapolated algorithm over a network. The dataset comprising two or more data entries usable as an input to the extrapolated algorithm. An output data re-combined from the first output block and one or more additional output blocks is received.