Abstract: An apparatus includes a communications component to receive a specified variable and one or more specified criteria to select a final clustered representation of a network, the specified criteria including a maximum degree of loss of information for the specified variable for the final clustered representation; and an iterative collapse component to perform iteration(s) of deriving the final clustered representation.

Abstract: One or more embodiments may include techniques to identify anomalies based on computer-generated results. Moreover, embodiments may include applying scenario rules to data to detect scenario violations and grouping the scenario violations into scenario clusters based on similar behavior performed by entities indicated by similarity metrics. embodiments include determining predictive ability values for each of the scenario clusters, ranking the scenario clusters based on the predictive ability values, and removing scenario clusters having predictive ability values below a threshold. In embodiments combinations of scenario clusters may be generated from the set of scenario clusters and the combinations of scenario clusters may be evaluated for effectiveness. Embodiments include generating scores for entities of the combinations of scenario clusters deemed effective, and provide results indicating whether one or more of the entities committed an anomaly based on the scores for each of the entities.

Abstract: One or more embodiments may include techniques to identify anomalies based on computer-generated results. Moreover, embodiments may include applying scenario rules to data to detect scenario violations and grouping the scenario violations into scenario clusters based on similar behavior performed by entities indicated by similarity metrics. embodiments include determining predictive ability values for each of the scenario clusters, ranking the scenario clusters based on the predictive ability values, and removing scenario clusters having predictive ability values below a threshold. In embodiments combinations of scenario clusters may be generated from the set of scenario clusters and the combinations of scenario clusters may be evaluated for effectiveness. Embodiments include generating scores for entities of the combinations of scenario clusters deemed effective, and provide results indicating whether one or more of the entities committed an anomaly based on the scores for each of the entities.

Abstract: A computer-implemented method of determining actions outside of a norm is provided. The method comprises: generating an actor state vector and a peer group state vector, wherein the actor state vector identifies a characteristic for an actor in each of a plurality of categories and the peer group state vector identifies a characteristic for a peer group in each of the plurality of categories, transforming the actor state vector into a first sampled wave series representation using a first wave series transformation, transforming the population state vector into a second sampled wave series representation using a second wave series transformation, and filtering the first sampled wave series representation and the second sampled wave series representation to identify a deviation of the first wave series representation from the second wave series representation in a phase or a magnitude.

Abstract: An apparatus includes a communications component to receive a specified variable and one or more specified criteria to select a final clustered representation of a network, the specified criteria including a maximum degree of loss of information for the specified variable for the final clustered representation; and an iterative collapse component to perform iteration(s) of deriving the final clustered representation.

Abstract: Systems and methods are provided for automated generation of a customized software product. A system includes a computer-readable medium encoded with a project parameters data structure, where the project parameters data structure includes a plurality of project requirement records, and a project prototype. One or more data processors are configured to process a plurality of initial characteristics for the customized software product, populate the project parameters data structure at least based on the initial characteristics, and generate the project prototype based on the project parameters data structure. The one or more data processors are further configured to output a requirements matrix data structure at least based on the project parameters data structure and the project prototype and to generate the customized software product at least based on the requirements matrix data structure and the project prototype.

Abstract: A computer-implemented method of determining actions outside of a norm is provided. The method comprises: generating an actor state vector and a peer group state vector, wherein the actor state vector identifies a characteristic for an actor in each of a plurality of categories and the peer group state vector identifies a characteristic for a peer group in each of the plurality of categories, transforming the actor state vector into a first sampled wave series representation using a first wave series transformation, transforming the population state vector into a second sampled wave series representation using a second wave series transformation, and filtering the first sampled wave series representation and the second sampled wave series representation to identify a deviation of the first wave series representation from the second wave series representation in a phase or a magnitude.

Abstract: Systems and methods are provided for automated generation of a customized software product. A system includes a computer-readable medium encoded with a project parameters data structure, where the project parameters data structure includes a plurality of project requirement records, and a project prototype. One or more data processors are configured to process a plurality of initial characteristics for the customized software product, populate the project parameters data structure at least based on the initial characteristics, and generate the project prototype based on the project parameters data structure. The one or more data processors are further configured to output a requirements matrix data structure at least based on the project parameters data structure and the project prototype and to generate the customized software product at least based on the requirements matrix data structure and the project prototype.