Abstract: An embodiment may involve: (i) obtaining a set of data records that include respective pluralities of tuples characterizing operation of communication sessions in a network and that identify hardware or software components related to the network that were involved in the communication sessions, (ii) determining degrees to which signatures in the pluralities of tuples are associated with communication problems in the network; (iii) identifying, from the degrees, a subset of the signatures most associated with the communication problems; (iv) grouping specific pairs from the subset of the signatures into equivalence classes based on co-occurrence of signatures of the specific pairs within the data records; (v) generating and pruning a dependency graph between the equivalence classes; (vi) from the equivalence classes remaining in the dependency graph, selecting a subset of the hardware or software components related to the network that are candidates for involvement with the communication problems.

Abstract: An embodiment may involve: (i) obtaining a set of data records that include respective pluralities of tuples characterizing operation of communication sessions in a network and that identify hardware or software components related to the network that were involved in the communication sessions, (ii) determining degrees to which signatures in the pluralities of tuples are associated with communication problems in the network; (iii) identifying, from the degrees, a subset of the signatures most associated with the communication problems; (iv) grouping specific pairs from the subset of the signatures into equivalence classes based on co-occurrence of signatures of the specific pairs within the data records; (v) generating and pruning a dependency graph between the equivalence classes; (vi) from the equivalence classes remaining in the dependency graph, selecting a subset of the hardware or software components related to the network that are candidates for involvement with the communication problems.

Abstract: An embodiment may involve: (i) obtaining a set of data records that include respective pluralities of tuples characterizing operation of communication sessions in a network and that identify hardware or software components related to the network that were involved in the communication sessions, (ii) determining degrees to which signatures in the pluralities of tuples are associated with communication problems in the network; (iii) identifying, from the degrees, a subset of the signatures most associated with the communication problems; (iv) grouping specific pairs from the subset of the signatures into equivalence classes based on co-occurrence of signatures of the specific pairs within the data records; (v) generating and pruning a dependency graph between the equivalence classes; (vi) from the equivalence classes remaining in the dependency graph, selecting a subset of the hardware or software components related to the network that are candidates for involvement with the communication problems.

Abstract: An embodiment may involve obtaining a set of data records including features characterizing operational aspects of a communication network. Each data record may include a feature vector and performance metrics of the communication network. Each feature vector may include a multiple elements corresponding to feature-value pairs. A first statistical analysis may be applied to the set of data records and their performance metrics to identify major contributors to degraded network performance. A second statistical analysis may be applied to identify elements that negatively influence the major contributors, and to discriminate between additive effects and incompatibilities as the source of negative influence. For each major contributor, a hierarchical dependency tree may be constructed with the major contributor as the root node and influencer elements as other nodes.

Abstract: An embodiment may involve obtaining a set of data records including features characterizing operational aspects of a communication network. Each data record may include a feature vector and performance metrics of the communication network. Each feature vector may include a multiple elements corresponding to feature-value pairs. A first statistical analysis may be applied to the set of data records and their performance metrics to identify major contributors to degraded network performance. A second statistical analysis may be applied to identify elements that negatively influence the major contributors, and to discriminate between additive effects and incompatibilities as the source of negative influence. For each major contributor, a hierarchical dependency tree may be constructed with the major contributor as the root node and influencer elements as other nodes.

Abstract: An embodiment may involve: (i) obtaining a set of data records that include respective pluralities of tuples characterizing operation of communication sessions in a network and that identify hardware or software components related to the network that were involved in the communication sessions, (ii) determining degrees to which signatures in the pluralities of tuples are associated with communication problems in the network; (iii) identifying, from the degrees, a subset of the signatures most associated with the communication problems; (iv) grouping specific pairs from the subset of the signatures into equivalence classes based on co-occurrence of signatures of the specific pairs within the data records; (v) generating and pruning a dependency graph between the equivalence classes; (vi) from the equivalence classes remaining in the dependency graph, selecting a subset of the hardware or software components related to the network that are candidates for involvement with the communication problems.