Abstract: A computing device automatically creates a log record recognizer expression and uses the log record recognizer expression to identify a log record type for a log record to parse the log record. A log record type regular expression is selected from log record type regular expressions and is separated into subexpressions that are normalized and are reassembled into an expression recognizer for each log record type regular expression. The expression recognizer for each is read into a data structure. The recognizer expressions are sorted based on an order associated with an expression operator of each subexpression. A log recognizer expression is created from each read expression recognizer included in the sorted recognizer expressions. A log record type of a log record is identified using the created log recognizer expression. A log record type regular expression is selected. The log record is parsed using the selected log record type regular expression.

Abstract: Techniques to simulate production events are described. Some embodiments are particularly directed to techniques to simulate production events based on randomization across a distribution of production events. In one embodiment, for example, an apparatus may comprise a simulation application operative to simulate one or more commands in a simulated environment using a task hierarchy, the simulation application comprising a configuration component, a command generation component, and an execution component, wherein simulating the one or more commands comprises executing one or more task commands.

Abstract: A computing device updates date values in a read dataset to support data analytics. Outlier and non-outlier date values are identified by, for each date value as a respective date value, reading a predefined number of neighboring date values relative to the respective date value; computing a median value and a median absolute deviation value of the predefined number of neighboring date values; computing a difference between the respective date value and the median value; dividing an absolute value of the difference by the median absolute deviation value to define a deviation value; comparing the deviation value to a threshold deviation value; and, based on the comparison, identifying the respective date value as an outlier or a non-outlier date value. Each identified non-outlier date value is updated with a new date computed using a date offset value. Each updated, identified non-outlier date value is replaced in a date updated dataset.

Abstract: Techniques to simulate production events are described. Some embodiments are particularly directed to techniques to simulate production events based on randomization across a distribution of production events. In one embodiment, for example, an apparatus may comprise a simulation application operative to simulate one or more commands in a simulated environment using a task hierarchy, the simulation application comprising a configuration component, a command generation component, and an execution component, wherein simulating the one or more commands comprises executing one or more task commands.