Abstract: One embodiment provides a method, including: receiving, at a conversational agent system, an input request from a user; sending, within the conversational agent system, the input request to at least one of a plurality of edge nodes, wherein each of the plurality of edge nodes performs at least one of a plurality of tasks and wherein the plurality of edge nodes are located on a common network and communicate with each other; processing, at the at least one of a plurality of edge nodes, the input request to generate a response by comparing the input request to historical inputs stored in a database corresponding to the at least one of a plurality of edge nodes; and providing, at the conversational agent system, the response to the input request to the user.

Abstract: One embodiment provides a method, including: receiving, at a conversational agent system, an input request from a user; sending, within the conversational agent system, the input request to at least one of a plurality of edge nodes, wherein each of the plurality of edge nodes performs at least one of a plurality of tasks and wherein the plurality of edge nodes are located on a common network and communicate with each other; processing, at the at least one of a plurality of edge nodes, the input request to generate a response by comparing the input request to historical inputs stored in a database corresponding to the at least one of a plurality of edge nodes; and providing, at the conversational agent system, the response to the input request to the user.

Abstract: One embodiment provides a method, including: receiving a plurality of loglines from a system execution log; generating at least one control flow graph by mining (i) a plurality of templates and (ii) a sequence of the plurality of templates defined by an order encountered during execution from the system execution log, wherein the control flow graph identifies flows between templates during execution; mining (i) discriminative parameters, (ii) a position for each of the mined discriminative parameters within a template, and (iii) a value of each of the mined discriminative parameters; annotating edges of the at least one control flow graph with the values of the mined discriminative parameters within the control flow graph; identifying execution traces from the system execution log using the values of the mined discriminative parameters; and detecting system anomalies in the identified execution traces by comparing one of the execution traces to an expected execution.

Abstract: One embodiment provides a method, including: receiving a plurality of loglines from a system execution log; generating at least one control flow graph by mining (i) a plurality of templates and (ii) a sequence of the plurality of templates defined by an order encountered during execution from the system execution log, wherein the control flow graph identifies flows between templates during execution; mining (i) discriminative parameters, (ii) a position for each of the mined discriminative parameters within a template, and (iii) a value of each of the mined discriminative parameters; annotating edges of the at least one control flow graph with the values of the mined discriminative parameters within the control flow graph; identifying execution traces from the system execution log using the values of the mined discriminative parameters; and detecting system anomalies in the identified execution traces by comparing one of the execution traces to an expected execution.