Abstract: In some aspects, computer-implemented methods of identifying patterns in time-series social-media data. In an embodiment, the method includes applying a deep-learning methodology to the time-series social-media data at a plurality of temporal resolutions to identify patterns that may exist at and across ones of the temporal resolutions. A particular deep-learning methodology that can be used is a recursive convolutional Bayesian model (RCBM) utilizing a special convolutional operator. In some aspects, computer-implemented methods of engineering outcome-dynamics of a dynamic system. In an embodiment, the method includes training a generative model using one or more sets of time-series data and solving an optimization problem composed of a likelihood function of the generative model and a score function reflecting a utility of the dynamic system.

Abstract: In some aspects, computer-implemented methods of identifying patterns in time-series social-media data. In an embodiment, the method includes applying a deep-learning methodology to the time-series social-media data at a plurality of temporal resolutions to identify patterns that may exist at and across ones of the temporal resolutions. A particular deep-learning methodology that can be used is a recursive convolutional Bayesian model (RCBM) utilizing a special convolutional operator. In some aspects, computer-implemented methods of engineering outcome-dynamics of a dynamic system. In an embodiment, the method includes training a generative model using one or more sets of time-series data and solving an optimization problem composed of a likelihood function of the generative model and a score function reflecting a utility of the dynamic system.

Abstract: Systems described herein provide structures and functionality for transforming passive analytics systems into systems that can actively modify software behavior based on analytic data to improve software performance relative to configurable goal metrics.

Abstract: Systems described herein provide structures and functionality for transforming passive analytics systems into systems that can actively modify software behavior based on analytic data to improve software performance relative to configurable goal metrics.

Abstract: Systems described herein provide structures and functionality actively modifying software behavior based on analytic data. An example method generally includes receiving session information characterizing interactions between the consumer and a software application; receiving a goal definition specifying how to calculate a goal score based on at least one metric calculable from the session information; grouping the sessions into bins, wherein each bin corresponds to a time interval and includes sessions that have starting times within the time interval; for each session: calculating a current value of a first metric, and determining a current goal score for the session based on the current value for the first metric and the goal definition; calculating a current average goal score for each bin; and rendering a graphical plot of the current average goal scores for the bins against time as partitioned by the bins for display.

Abstract: In some aspects, computer-implemented methods of identifying patterns in time-series social-media data. In an embodiment, the method includes applying a deep-learning methodology to the time-series social-media data at a plurality of temporal resolutions to identify patterns that may exist at and across ones of the temporal resolutions. A particular deep-learning methodology that can be used is a recursive convolutional Bayesian model (RCBM) utilizing a special convolutional operator. In some aspects, computer-implemented methods of engineering outcome-dynamics of a dynamic system. In an embodiment, the method includes training a generative model using one or more sets of time-series data and solving an optimization problem composed of a likelihood function of the generative model and a score function reflecting a utility of the dynamic system.