Telemetry data contextualized across datasets

An apparatus comprising:

• one or more computer readable storage media;
• program instructions stored on the one or more computer readable storage media that, when executed by a processing system, direct the processing system to at least:
• obtain an indication of a data selection defining an error associated with a computer application, wherein the selection is made from datasets that comprise operational event data entries associated with the computer application determined by one or more associated telemetry elements;
• establish a joined dataset based at least on contextually corresponding characteristics derived from the datasets, wherein the contextually corresponding characteristics comprise at least:
• a time of occurrence of a first operational event data entry for a first dataset of the datasets,
• a time of occurrence of a second operational event data entry for a second dataset of the datasets, and
• an inferred correlation between the first dataset and the second dataset, wherein the first dataset includes an identifier of an application that generated the first dataset that is not included in the second dataset, and wherein the inferred correlation is based on deducing that the identifier should also be associated with the second dataset by applying a machine learning model to the second dataset;
• process the joined dataset with a plurality of adaptive functions to correlate data entries of the joined dataset to the data selection, with each of the plurality of adaptive functions configured to produce an associated correlation dataset comprising data entries potentially related to the data selection;
• and
• evaluate each associated correlation dataset to select an output dataset comprising contextual data entries related to the data selection.

A method of operating a telemetry data processing system, the method comprising:

• receiving an indication of an error associated with a computer application that is included among datasets comprising operational events associated with the computer application determined by one or more associated telemetry elements;
• establishing a joined dataset from at least portions of the datasets that include a timeframe of the error associated with the computer application, wherein the timeframe is determined from times of occurrence of a first operational event data entry for a first dataset of the datasets, and a time of occurrence of a second operational event data entry for a second dataset of the datasets;
• inferring a correlation between the first dataset and the second dataset, wherein the first dataset includes an identifier of an application that generated the first dataset that is not included in the second dataset, and wherein the inferred correlation is based on deducing that the identifier should also be associated with the second dataset by applying a machine learning model to the second dataset;
• processing the joined dataset with a plurality of machine learning algorithms to correlate events of the joined dataset to the error associated with the computer application, with each of the plurality of machine learning algorithms configured to produce an associated dataset comprising data entries potentially related to the error associated with the computer application;
• and
• evaluating each associated dataset to select an output dataset comprising contextual data entries accumulated from the datasets that are related to the error associated with the computer application.