公开(公告)号：US20250123937A1

公开(公告)日：2025-04-17

申请号：US18989312

申请日：2024-12-20

Applicant: Dynatrace LLC

Inventor： Herwig MOSER ,  Martin CARPELLA ,  Otmar ERTL

IPC: G06F11/34 ,  G06F11/30 ,  G06F16/28

Abstract: Technologies are disclosed for the automated, rule-based generation of models from arbitrary, semi-structured observation data. Context data of received observation data, like data describing the location of on which a phenomenon was observed, is used to identify related observations, to generate entities in a model describing the observed data and to assign observations to model data. Mapping rules may be used for the on-demand generation of models, and different sets of mapping rules may be used to generate different models out of the same observation data for different purposes. Further, observation time data may be used to observer the temporal evolution of the generated model. Possible use cases of the so generated models include the interpretation of observation data that describes unexpected operation conditions in view of the generated model, or to determine how a monitored system reacts on changing conditions, like increased load.

公开(公告)号：US20220358023A1

公开(公告)日：2022-11-10

申请号：US17733105

申请日：2022-04-29

Applicant: Dynatrace LLC

Inventor： Herwig MOSER ,  Martin CARPELLA ,  Otmar ERTL

IPC: G06F11/34 ,  G06F11/30 ,  G06F16/28

Abstract: Technologies are disclosed for the automated, rule-based generation of models from arbitrary, semi-structured observation data. Context data of received observation data, like data describing the location of on which a phenomenon was observed, is used to identify related observations, to generate entities in a model describing the observed data and to assign observations to model data. Mapping rules may be used for the on-demand generation of models, and different sets of mapping rules may be used to generate different models out of the same observation data for different purposes. Further, observation time data may be used to observer the temporal evolution of the generated model. Possible use cases of the so generated models include the interpretation of observation data that describes unexpected operation conditions in view of the generated model, or to determine how a monitored system reacts on changing conditions, like increased load.