Abstract: Improved techniques for processing large-scale data and various large-scale data applications (e.g., large-scale Data Mining (DM), large-scale data analysis (LSDA)) in computing systems (e.g., Data Information Systems, Database Systems) are disclosed. Redundancy-reduced data (RRDS) can be provided as data that can be used more efficiently by various applications, especially, large-scale data applications. In doing so, at least one assumption about the distribution of a multi-dimensional data set (MDDS) and its corresponding set of responses (Y) can be made in order to reduce the multi-dimensional data set (MDDS). For example, a normal distribution (e.g.

Abstract: Improved techniques for reducing the size of data of the multidimensional data are disclosed. The improved techniques are highly suitable for processing large-scale data and various large-scale data applications (e.g., large-scale Data Mining (DM), large-scale data analysis (LSDA) in computing systems (e.g., Data Information Systems, Database Systems). Redundancy-reduced data can be provided (RRDS) thereby providing data that can be used more efficiently by various applications, especially, large-scale data applications. At least one assumption about the distribution of a multi-dimensional data set (MDDS) and its corresponding set of responses (Y) can be made in order to reduce the multi-dimensional data set (MDDS). It should be noted that the assumption can be made after effectively combining multi-dimensional data set (MDDS) and its corresponding set of responses (Y) so that the set of responses (Y) can also be in considered in effectively reducing the size of the multi-dimensional data set (MDDS).

Abstract: A multi-staged sample and seed machine-learning training technique is presented. A sample proportion of a training data set is fed to a machine-learning algorithm (MLA) for purposes of configuring functions of the MLA to predict an output with a desired degree of accuracy. When iterating the sample proportion, if a deviation in an incrementally produced current accuracy of the MLA does not exceed a threshold, the sampled proportion is increased. This continues until the current degree of accuracy meets or exceeds the desired degree of accuracy, which is an indication that the functions of the MLA are configured as a desired model for producing the predicted output when the MLA is presented with input that may or may not have been associated with the training data set.

Abstract: According to an example, a model is selected from models including an augmented buyer model based on probabilities of conceivable transitions, and each conceivable transition includes a multi-step transition between a first URL and a second URL via at least one intermediate URL of the website. A user is determined to likely be a buyer or a non-buyer based on interaction data and the selected model. The user is presented with an offer that encourages the user to buy from the website upon the determination that the user is a buyer.

Abstract: Regime change in streaming data is detected. The streaming data is sent to a plurality of modules. Each module in the plurality of modules produces an association measure. The association measure is a measure of similarity between the streaming data and regime data associated with the module producing the association measure. A regime change in the streaming data is detected based on values of the association measures from the plurality of modules.

Abstract: Methods, systems, and computer-readable and executable instructions are provided for classifying an electrocardiogram (ECG) signal. Classifying an ECG signal can include analyzing the ECG signal using a stream of pulses generated by a sampler, extracting cardiac pulse features from a timing of the stream of pulses, and classifying the ECG signal based on the extracted cardiac pulse feature.

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: Regime change in streaming data is detected. The streaming data is sent to a plurality of modules. Each module in the plurality of modules produces an association measure. The association measure is a measure of similarity between the streaming data and regime data associated with the module producing the association measure. A regime change in the streaming data is detected based on values of the association measures from the plurality of modules.

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.