Abstract: Various embodiments described herein support or provide for anomaly detection in time series data (e.g., comprising measurement values for a metric of a monitored system) based on one or more incomplete matrix profiles.

Abstract: Various embodiments described herein support or provide for alert monitoring of data (e.g., metric data) based on one or more recommended attribute values (e.g. dimension values), which can facilitate generation of alerts for the data based on detected anomalies. In particular, an embodiment can determine one or more recommended attribute values associated with data being analyzed for anomalies (e.g., metric data), select (or facilitate selection of) one or more of the recommended attribute values, configure alert monitoring for the data based on one or more selected attribute values, and enable the alert monitoring. Once enabled, the alert monitoring can trigger an alert in response to detecting one or more anomalies in the metric data associated with the one or more selected attribute values.

Abstract: This disclosure is directed to large-scale graph processing to determine second-degree connections for members of a social network. A social graph is duplicated into two graphs, where each of the two graphs are partitioned into various partitions. The partitions are each sorted according to a predetermined key selected from each of the graphs. The partitions are then assigned logical Work Units, where a first set of Work Units are determined from a first graph and second set of Work Units are determined from a second graph. The Work Units are determined to be asymmetrical such that the partitions of the first set of Work Units are assigned differently than the partitions of the second set of Work Units. One set of Work Units are loaded in-memory and another set of Work Units are streamed to a mapping module process, which determines the second-degree connections from the sets of Work Units.

Abstract: This disclosure is directed to large-scale graph processing to determine second-degree connections for members of a social network. A social graph is duplicated into two graphs, where each of the two graphs are partitioned into various partitions. The partitions are each sorted according to a predetermined key selected from each of the graphs. The partitions are then assigned logical Work Units, where a first set of Work Units are determined from a first graph and second set of Work Units are determined from a second graph. The Work Units are determined to be asymmetrical such that the partitions of the first set of Work Units are assigned differently than the partitions of the second set of Work Units. One set of Work Units are loaded in-memory and another set of Work Units are streamed to a mapping module process, which determines the second-degree connections from the sets of Work Units.