Abstract: Embodiments of the present disclosure provide to techniques for automatically grouping software applications based on their technical patterns/characteristics (i.e., technical facets) via machine learning (ML) algorithms. For instance, a first set of software applications that exhibit a high prevalence of one or more first technical facets may be grouped into a first category, a second set of software applications that exhibit a high prevalence of one or more second technical facets may be grouped into a second category, and so on. Once grouped into categories, the software applications in a given category may be assessed, analyzed, and/or processed together for various purposes.

Abstract: A system and method of automated driver selection is disclosed. A plurality of driver profiles and a first order are received. Each of the driver profiles includes at least one driver parameter. The first order includes at least one order parameter. A driver score is calculated for each of the plurality of driver profiles for the first order. The driver score is calculated based on the at least one driver parameter and the at least one order parameter. Each driver profile is ranked based on the calculated driver score and a first delivery assignment request is transmitted to a system associated with a first-ranked driver profile. A response is received to the first delivery assignment request.

Abstract: Embodiments of the present disclosure provide to techniques for automatically grouping software applications based on their technical patterns/characteristics (i.e., technical facets) via machine learning (ML) algorithms. For instance, a first set of software applications that exhibit a high prevalence of one or more first technical facets may be grouped into a first category, a second set of software applications that exhibit a high prevalence of one or more second technical facets may be grouped into a second category, and so on. Once grouped into categories, the software applications in a given category may be assessed, analyzed, and/or processed together for various purposes.

Abstract: In some embodiments, a processor accesses a metrics dataset, which includes metrics whose values indicate data network activity. The metrics dataset has segments. Each segment is a respective subset of the data items having a common feature. The processor identifies anomalous segments in the metrics dataset. Each anomalous segment has a segment trend that is different from a trend associated with the larger metrics dataset. The processor generates a data graph that includes nodes, which represent anomalous segments, and edges connecting the nodes. The processor applies weights to the edges. Each weight indicates (i) a similarity between a pair of anomalous segments represented by the nodes connected by the weighted edge and (ii) a relationship between the anomalous segments and the metrics dataset. The processor ranks the anomalous segments based on the applied weights and selects one or more segments with sufficiently high ranks.

Abstract: In some embodiments, a processor accesses a metrics dataset, which includes metrics whose values indicate data network activity. The metrics dataset has segments. Each segment is a respective subset of the data items having a common feature. The processor identifies anomalous segments in the metrics dataset. Each anomalous segment has a segment trend that is different from a trend associated with the larger metrics dataset. The processor generates a data graph that includes nodes, which represent anomalous segments, and edges connecting the nodes. The processor applies weights to the edges. Each weight indicates (i) a similarity between a pair of anomalous segments represented by the nodes connected by the weighted edge and (ii) a relationship between the anomalous segments and the metrics dataset. The processor ranks the anomalous segments based on the applied weights and selects one or more segments with sufficiently high ranks.