Abstract: The following relates generally to diagnosing problems with websites. In some embodiments, a webpage interaction processor receives a list of potential user experience problems. The webpage interaction processor then extracts click data from the website, and processes the extracted click data into grams. Subsequently, an analytics engine is trained based on the processed click data. The trained analytics engine may then diagnose the problem of the website with a potential user experience problem from the received list of potential user experience problems. In some embodiments, the process is entirely automated.

Abstract: Techniques for automatically detecting pair-wise interaction effects among a large number of variables are provided. An example method includes obtaining a data set including data related to a target variable and each of a plurality of variables upon which the target variable depends; grouping the data related to each variable, of the plurality of variables, into a pre-determined number of groups of grouped variable values; analyzing the grouped variable values related to each variable as compared to the grouped variable values related to each other variable, of the plurality of variables, in order to determine a grouped variable interaction score for each pair of variables, of the plurality of variables; and identifying a pre-determined number of pairs of variables having the highest interaction scores, based on the grouped variable interaction score for each pair of variables.

Abstract: Techniques for automatically identifying frequently traveled routes are provided. An example method includes obtaining telematics data associated with routes traveled by one or more drivers; mapping the telematics data associated with each route to respective step functions representative of each of the plurality of routes; comparing, the step functions representative of each of the routes to one another in order to determine a distance between each pair of step functions, of the plurality of step functions; clustering each of the routes into one or more clusters, with each cluster including one or more routes associated with step functions having distances less than a threshold distance from one another; and identifying one or more frequently traveled routes associated with the one or more drivers based on the one or more clusters, with each frequently traveled route being included in a cluster including greater than a threshold number of routes.

Abstract: Techniques for detecting interactions between predictor variables in a statistical model are provided. The techniques include identifying predictor variables for a dependent variable; and for each pair of predictor variables: obtaining a dataset including (i) first predictor variable values, (ii) second predictor variable values, and (iii) dependent variable values associated with each pair of a first predictor variable value and a second predictor variable value; generating a three-dimensional graph based on the dataset, wherein each point of the three-dimensional graph includes a first coordinate value associated with a first predictor variable, a second coordinate value associated with a second predictor variable, and a third coordinate value associated with a dependent variable outcome; and analyzing the three-dimensional graph to determine a measure of spatial randomness associated with the three-dimensional graph.

Abstract: Systems and methods are described for preparing unstructured data for machine learning analysis. An example method may include: receiving data representing a plurality of processes; analyzing the data to identify, for each process of the plurality of processes, a time-ordered sequence of events that occurred during the process; generating a plurality of emoji sequences by, for each process of the plurality of processes, generating an emoji sequence, each emoji in the emoji sequence representing an event of the events that occurred during the process, and the emoji sequence ordered in accordance with the time-ordered sequence; generating a plurality of feature vectors corresponding to the respective plurality of emoji sequences; and applying a machine learning technique to the plurality of feature vectors.

Abstract: The following relates generally to diagnosing problems with websites. In some embodiments, a webpage interaction processor receives a list of potential user experience problems. The webpage interaction processor then extracts click data from the website, and processes the extracted click data into grams. Subsequently, an analytics engine is trained based on the processed click data. The trained analytics engine may then diagnose the problem of the website with a potential user experience problem from the received list of potential user experience problems. In some embodiments, the process is entirely automated.

Abstract: The following relates generally to diagnosing problems with websites. In some embodiments, a webpage interaction processor receives a list of potential user experience problems. The webpage interaction processor then extracts click data from the website, and processes the extracted click data into grams. Subsequently, an analytics engine is trained based on the processed click data. The trained analytics engine may then diagnose the problem of the website with a potential user experience problem from the received list of potential user experience problems. In some embodiments, the process is entirely automated.