Abstract: Data is received from a plurality of devices each having a same target part subject to failure. The received data is used to determine, for each of at least a subset of the plurality of devices, a part failure date on which the target part failed in that device. A set of features usable to predict failure of the target part is engineered, the set of features including one or more features that are not based on logged warning or error events. At least a subset of the data is labeled and aggregated over one or more days. The labeled and aggregated data is used to train a machine learning model configured to be used to predict failure of the target part in a device based on recent data from that device, including by computing from the data features corresponding to the programmatically engineered a set of features.

Abstract: Techniques are disclosed to predict medical device failure based on operational log data. Log data associated with a plurality of devices comprising a population of devices each having a same target part subject to failure. For each of at least a subset of the plurality of devices replacement dates on which the target part was replaced in that device are determined. A set of logged event data with prescribed severity is extracted from the log data for said plurality of devices. A subset of the logged event data is identified as being associated with impending failure of the target part. The subset of the logged event data is transformed into a normalized form. The normalized subset of the logged event data is used to generate a failure prediction model to predict failure of the target part in a device based on the current event logs from that device.

Abstract: Techniques are disclosed to parse and store hierarchical session log data for search and analytics. In various embodiments, log data is stored in a memory or other data storage device. A processor coupled to the memory or other data storage device is used to: identify a set of log data associated with a hierarchical session; extract a session attribute from the set of log data; and store the session attribute in a manner that associates the session attribute with the hierarchical session.

Abstract: Techniques are disclosed to parse and store hierarchical session log data for search and analytics. In various embodiments, log data is stored in a memory or other data storage device. A processor coupled to the memory or other data storage device is used to: identify a set of log data associated with a hierarchical session; extract a session attribute from the set of log data; and store the session attribute in a manner that associates the session attribute with the hierarchical session.

Abstract: A message bus-based streaming rules engine is disclosed. In various embodiments, a data analysis system as disclosed herein includes a communication interface configured to receive log data; and a processor coupled to the communication interface and configured to: parse the log data to generate a data unit comprising a data value; and send the data unit via a message bus to a rule actor configured to apply a rule based at least in part on the data value.

Abstract: Techniques to process machine generated log data are disclosed. In various embodiments, a parser definition associated with a set of log data is determined. The parser definition is compiled to create an instance of a parser to parse the set of log data. The parser has a hierarchical structure comprising a plurality of hierarchically related nodes, each of at least a subset of said nodes having associated therewith one or more actors each configured to parse data associated with that node. At least a portion of the set of log data is sent to the parser instance prior to compilation of said parser instance being completed. A first node of the parser instance is configured to receive and parse log data associated with the first node even if compilation of the parser definition has not been completed with respect to a second node of said parser instance.