Abstract: The disclosed embodiments provide a system for processing data. During operation, the system generates, from a set of traces of an asynchronous workflow, a graph-based representation of the asynchronous workflow. Next, the system uses a set of causal relationships in the asynchronous workflow to update the graph-based representation. The system then analyzes the updated graph-based representation to identify a set of high-latency paths in the asynchronous workflow. Finally, the system uses the set of high-latency paths to output an execution profile for the asynchronous workflow, wherein the execution profile includes a subset of tasks associated with the high-latency paths in the asynchronous workflow.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system obtains a set of latencies for a set of requests in a multi-phase parallel task. Next, the system includes the latencies in a graph-based representation of the multi-phase parallel task. The system then analyzes the graph-based representation to identify a set of high-latency paths in the multi-phase parallel task. Finally, the system uses the set of high-latency paths to output an execution profile for the multi-phase parallel task, wherein the execution profile includes a subset of the requests associated with the high-latency paths.

Abstract: Techniques are described herein for deriving, for each member of a social networking service, a set of metrics representing a measure of the member's intent and interests. For example, a set of member-intent and member-interest scores are derived by detecting which of several applications and services that a particular user interacts with, when the interactions occur, the frequency of the interactions, the particular type of interactions, the nature of the any particular content (e.g., subject matter, topic, etc.) with which the member is interacting, and so forth. The member-intent and member-interest scores are then made available to a wide-variety of applications and services, for example, for use in personalizing various experiences to best suit the intent and interests of each member.

Abstract: Techniques are described herein for deriving, for each member of a social networking service, a set of metrics representing a measure of the member's intent and interests. For example, a set of member-intent and member-interest scores are derived by detecting which of several applications and services that a particular user interacts with, when the interactions occur, the frequency of the interactions, the particular type of interactions, the nature of the any particular content (e.g., subject matter, topic, etc.) with which the member is interacting, and so forth. The member-intent and member-interest scores are then made available to a wide-variety of applications and services, for example, for use in personalizing various experiences to best suit the intent and interests of each member.