Abstract: A system, method and computer storage medium are provided for computing analytics on structured data from at least one data source. Statistical estimates are computed using a statistics object. Software is provided which is capable of performing any of the data processing methods selected from the pass, stream and merge methods and performing at least one statistical calculation on data from the data source using the statistics object to compute statistical estimates by at least one method selected from the provided data processing methods.

Abstract: A computer method, apparatus and storage medium is provided for creating quantitative aesthetic graphics from data. The invention utilizes a graph algebra to construct graphs and visually or otherwise represents the graphs as a quantitative aesthetic graphic representation. To create the quantitative aesthetic graphics from data, the data is indexed to form a data set. Thereafter, the data is converted into a variable data structure composed of an index set, a range and a function. The variable data structure is converted into a variable set by using at least one of a blend step, a cross step and a nest step. The variable set is mapped into a set of points and the set of points is mapped into an aesthetic representation.

Abstract: A computer method, apparatus and storage medium is provided for creating quantitative aesthetic graphics from data. The invention utilizes a graph algebra to construct graphs and visually or otherwise represents the graphs as a quantitative aesthetic graphic representation. To create the quantitative aesthetic graphics from data, the data is indexed to form a data set. Thereafter, the data is converted into a variable data structure composed of an index set, a range and a function. The variable data structure is converted into a variable set by using at least one of a blend step, a cross step and a nest step. The variable set is mapped into a set of points and the set of points is mapped into an aesthetic representation.

Abstract: A method and computer system is provided for automatically constructing a time series model for the time series to be forecasted. The constructed model can be either a univariate ARIMA model or a multivariate ARIMA model, depending upon whether predictors, interventions or events are inputted in the system along with the series to be forecasted. The method of constructing a univariate ARIMA model comprises the steps of imputing missing values of the time series inputted; finding the proper transformation for positive time series; determining differencing orders; determining non-seasonal AR and MA orders by pattern detection; building an initial model; estimating and modifying the model iteratively.