Pattern Identification in Time-Series Social Media Data, and Output-Dynamics Engineering for a Dynamic System Having One or More Multi-Scale Time-Series Data Sets

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. A result of the solution is an influence indicator corresponding to intervention dynamics that can be applied to the dynamic system to influence outcome dynamics of the system.

What is claimed is: 
     
         1 . A computer-implemented method of engineering outcome dynamics of a dynamic system that includes one or more multi-scale time-series data sets, the computer-implemented method comprising:
 training a generative model using each of the one or more multi-scale time-series data sets;   providing an optimization problem composed of a likelihood function of the generative model and a score function that reflects a utility of the dynamic system;   solving the optimization problem so as to determine an influence indicator indicating an influence scheme for influencing the outcome dynamics; and   providing the influence indicator to an outcome-dynamics influencing system.   
     
     
         2 . The computer-implemented method according to  claim 1 , wherein the generative model comprises a recursive convolutional Bayesian model (RCBM). 
     
     
         3 . The computer-implemented method according to  claim 2 , wherein the RCBM uses a convolutional operator that carries out a scale-and-copy task. 
     
     
         4 . The computer-implemented method according to  claim 1 , wherein the computer-implemented method is configured for a particular application, and the score function is selected based on the particular application. 
     
     
         5 . The computer-implemented method according to  claim 4 , wherein the score function is a pattern-matching score function. 
     
     
         6 . The computer-implemented method according to  claim 4 , wherein the score function is a profit-maximization score function. 
     
     
         7 . The computer-implemented method according to  claim 4 , wherein the particular application comprises a social-media marketing campaign. 
     
     
         8 . The computer-implemented method according to  claim 1 , wherein the particular application comprises event-alert notification. 
     
     
         9 . The computer-implemented method according to  claim 1 , wherein the one or more multi-scale time-series data sets includes a social-media data set. 
     
     
         10 . The computer-implemented method according to  claim 9 , wherein the social-media data set comprises a social-media data stream. 
     
     
         11 . The computer-implemented method according to  claim 1 , wherein the one or more multi-scale time-series data sets includes video data. 
     
     
         12 . A machine-readable storage medium containing computer-executable instructions for performing a method of engineering outcome dynamics of a dynamic system that includes one or more multi-scale time-series data sets, the computer-implemented method comprising:
 training a generative model using each of the one or more multi-scale time-series data sets;   providing an optimization problem composed of a likelihood function of the generative model and a score function that reflects a utility of the dynamic system;   solving the optimization problem so as to determine an influence indicator indicating an influence scheme for influencing the outcome dynamics; and   providing the influence indicator to an outcome-dynamics influencing system.   
     
     
         13 . The machine-readable storage medium of  claim 12 , wherein the generative model comprises a recursive convolutional Bayesian model (RCBM). 
     
     
         14 . The machine-readable storage medium of  claim 13 , wherein the RCBM uses a convolutional operator that carries out a scale-and-copy task. 
     
     
         15 . The machine-readable storage medium of  claim 12 , wherein the computer-implemented method is configured for a particular application, and the score function is selected based on the particular application. 
     
     
         16 . The machine-readable storage medium of  claim 15 , wherein the score function is a pattern-matching score function. 
     
     
         17 . The machine-readable storage medium of  claim 15 , wherein the score function is a profit-maximization score function. 
     
     
         18 . The machine-readable storage medium of  claim 15 , wherein the particular application comprises a social-media marketing campaign. 
     
     
         19 . The machine-readable storage medium of  claim 12 , wherein the particular application comprises event-alert notification. 
     
     
         20 . The machine-readable storage medium of  claim 12 , wherein the one or more multi-scale time-series data sets includes a social-media data set.