Abstract: Methods, systems, and computer program products are provided for optimizing financial performance. Time series data describing the financial performance generated by corresponding marketing investments is provided to configure an econometric model. Linear coefficients of the econometric model are adjusted in accordance with the qualitative factors received as inputs from experts. The econometric model is transformed into an aggregated non-linear econometric model that includes non-linear factors that cause the financial performance to change at a varying rate as a function of the marketing investments. An allocation of the marketing investments generated by the aggregated non-linear econometric model is adjusted by applying an optimization algorithm to provide an optimized financial performance.

Abstract: A method for compressing an initial dataset may be implemented on a data processing system. The method may include generating a group of bootstrap samples of wavelet coefficients from the initial dataset using a wavelet basis function. An average quantile of the group of bootstrap samples of wavelet coefficients may be determined. The group of wavelet coefficients may be compressed by deleting initial wavelet coefficients having magnitudes less than the coefficient cutoff value equal to the average quantile. The compressed group of wavelet coefficients and the wavelet basis function may be used to approximate the initial dataset.

Abstract: Sensor data is received from one or more sensors. The sensor data is organized within a hierarchy. The sensor data is organized within a hierarchy that is non-dyadic. A processor of a computing device generates a discrete wavelet transform, based on the sensor data and based on the hierarchy of the sensor data, to compress the sensor data. The sensor data, as has been compressed via generation of the discrete wavelet transform, is processed.

Abstract: Methods, systems, and computer program products are provided for quantifying financial impact of marketing investments. Time series data describing the financial performance generated by corresponding marketing investments that are made as a function of time is provided to configure an econometric model. The econometric model, which describes a linear relationship between the financial performance and the corresponding marketing investments, is transformed into an aggregated non-linear econometric model that includes non-linear factors causing the financial performance to change at a varying rate as a function of the marketing investments.

Abstract: Methods, systems, and computer program products are provided for optimizing financial performance. Time series data describing the financial performance generated by corresponding marketing investments is provided to configure an econometric model. Linear coefficients of the econometric model are adjusted in accordance with the qualitative factors received as inputs from experts. The econometric model is transformed into an aggregated non-linear econometric model that includes non-linear factors that cause the financial performance to change at a varying rate as a function of the marketing investments. An allocation of the marketing investments generated by the aggregated non-linear econometric model is adjusted by applying an optimization algorithm to provide an optimized financial performance.

Abstract: Methods, systems, and computer program products are provided for incorporating qualitative factors into an econometric model. Time series data describing the financial performance generated by corresponding marketing investments that are made as a function of time is provided to configure an econometric model. The econometric model includes linear coefficients that define a linear relationship between the financial performance and the corresponding marketing investments. The linear coefficients are adjusted in accordance with the qualitative factors received as inputs from experts, thereby enabling the qualitative factors to be quantified into the econometric model.

Abstract: In a method for generating a query plan for estimating a number of unique entry counts of an attribute in a database, a sample of entries in the database is identified, at least one of a sampling percent and a coefficient of variance of the entries in the sample is identified, and a skewness of the entries in the sample is calculated. In addition, at least one of a plurality of estimators is selected based upon the skewness of the entries and at least one of the sampling percent and the coefficient of variance of the entries in the sample. Moreover, a query plan is generated from the selected at least one of the plurality of estimators. A query optimizer for performing the method is provided.

Abstract: Systems and methods for estimating the number of unique attributes in a database are disclosed that can include generating a first estimate of the unique values of an attribute using a data sample; generating a second estimate of the unique values of the attribute in the data sample; forming a weighted first estimate; forming a weighted second estimate; and combining the weighted first and second estimates to form a final estimate.

Abstract: Systems, methods, media, and other embodiments associated with predicting website user satisfaction based on current actions analyzed in light of combined quantitative and qualitative data are described. One example system embodiment includes logic for acquiring both quantitative website usage data and qualitative website satisfaction data. The example system also includes logic for creating a satisfaction categorization data from the quantitative website usage data and the qualitative website satisfaction data.

Abstract: Systems and methods for estimating the number of unique attributes in a database are disclosed that can include generating a first estimate of the unique values of an attribute using a data sample; generating a second estimate of the unique values of the attribute in the data sample; forming a weighted first estimate; forming a weighted second estimate; and combining the weighted first and second estimates to form a final estimate.

Abstract: A method and system for site path evaluation using web session clustering is provided. The method identifies properties of a plurality of web site traversal paths. Data is acquired from a plurality of sessions corresponding to at least a portion of the plurality of web page traversal paths. Portions of the web site traversal paths are grouped into a unified web page category. The plurality of sessions is clustered into a plurality of web session clusters according to a similarity measure. One of the plurality of web session clusters most closely exhibiting at least one predefined metric is selected for analysis of the propertied of a web page traversal path contained therein. A system includes a plurality of web pages, a monitoring program and a computational process configured to receive data and identify properties of the plurality of traversal paths.

Abstract: One exemplary method comprises a method for grouping a plurality of data elements of a dataset. The method includes clustering the dataset into a plurality of clusters with each of the plurality of clusters including at least one of the plurality of data elements. The method further includes iteratively classifying the plurality of clusters into a plurality of classes of like data elements.