Disclosed herein is a computer architecture and software that is configured to modify data intake operation at an asset-monitoring system based on a predictive model. In accordance with the present disclosure, the asset-monitoring system may execute a predictive model that outputs an indicator of whether at least one event from a group of events (e.g., a failure event) is likely to occur at a given asset within a given period of time in the future. Based on the output of this predictive model, the asset-monitoring system may modify one or more operating parameters for ingesting data from the given asset, such as a storage location for the ingested data, a set of data variables from the asset that are ingested, and/or a rate at which data from the asset is ingested.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computing system comprising: a data intake system; at least one processor; a non-transitory computer-readable medium; and program instructions stored on the non-transitory computer-readable medium that are executable by the at least one processor to cause the computing system to: operate in a first mode in which the computing system ingests a first set of data variables received from a given asset of a plurality of assets; while operating in the first mode, (a) receive operating data from the given asset, (b) ingest the first set of data variables from the received operating data, and (c) based on at least a portion of the ingested data, execute a predictive model that outputs an indicator of whether at least one event from a group of events is likely to occur at the given asset within a given period of time in the future; make a determination that the indicator satisfies a threshold condition; in response to the determination, transition from operating in the first mode to operating in a second mode in which the computing system ingests a second set of data variables received from the given asset, wherein the first set of data variables and the second set of data variables differ by at least one data variable; and while operating in the second mode, (a) receive operating data from the given asset and (b) ingest the second set of data variables from the received operating data. 2. The computing system of claim 1, wherein each data variable in the first and second sets of data variables comprises an output of a respective sensor at the given asset. 3. The computing system of claim 1, wherein the group of events comprises a group of one or more failures. 4. The computing system of claim 3, wherein the predictive model outputs a health metric indicating whether at least one failure from the group of failures is likely to occur at the given asset within the given period of time in the future. 5. The computing system of claim 4, wherein the predictive model is defined based on historical operating data comprising (i) historical abnormal-condition data associated with one or more failures that occurred at the plurality of assets in the past and (ii) historical sensor data indicating operating conditions of the plurality of assets in the past. 6. The computing system of claim 1, wherein the second set of data variables includes one or more data variables that are not included in the first set of data variables. 7. The computing system of claim 6, wherein the second set of data variables includes each data variable in the first set of data variables. 8. The computing system of claim 1, wherein the indicator of whether at least one event from the group of events is likely to occur at the given asset within the given period of time in the future comprises a probability that no event from the group of events is likely to occur at the given asset within the given period of time in the future, and wherein determination that the indicator satisfies the threshold condition comprises a determination that the probability is at or below a threshold value. 9. The computing system of claim 1, wherein the indicator of whether at least one event from the group of events is likely to occur at the given asset within the given period of time in the future comprises an a probability of at least one event from the group of events occurring at the given asset within the given period of time in the future, and wherein determination that the indicator satisfies the threshold condition comprises a determination that the probability is at or above a threshold value. 10. A non-transitory computer-readable medium having instructions stored thereon that are executable to cause a computing system to: operate in a first mode in which the computing system ingests a first set of data variables received from a given asset of a plurality of assets; while operating in the first mode, (a) receive operating data from the given asset, (b) ingest the first set of data variables from the received operating data, and (c) based on at least a portion of the ingested data, execute a predictive model that outputs an indicator of whether at least one event from a group of events is likely to occur at the given asset within a given period of time in the future; make a determination that the indicator satisfies a threshold condition; in response to the determination, transition from operating in the first mode to operating in a second mode in which the computing system ingests a second set of data variables received from the given asset, wherein the first set of data variables and the second set of data variables differ by at least one data variable; and while operating in the second mode, (a) receive operating data from the given asset and (b) ingest the second set of data variables from the received operating data. 11. The non-transitory computer-readable medium of claim 10, wherein each data variable in the first and second sets of data variables comprises an output of a respective sensor at the given asset. 12. The non-transitory computer-readable medium of claim 10, wherein the group of events comprises a group of one or more failures, and wherein the predictive model outputs a health metric indicating whether at least one failure from the group of failures is likely to occur at the given asset within the given period of time in the future. 13. The non-transitory computer-readable medium of claim 12, wherein the predictive model is defined based on historical operating data comprising (i) historical abnormal-condition data associated with one or more failures that occurred at the plurality of assets in the past and (ii) historical sensor data indicating operating conditions of the plurality of assets in the past. 14. The non-transitory computer-readable medium of claim 10, wherein the second set of data variables includes one or more data variables that are not included in the first set of data variables. 15. The non-transitory computer-readable medium of claim 14, wherein the second set of data variables includes each data variable in the first set of data variables. 16. A computer-implemented method comprising: a computing system operating in a first mode in which the computing system ingests a first set of data variables received from a given asset of a plurality of assets; while operating in the first mode, the computing system (a) receiving operating data from the given asset, (b) ingesting the first set of data variables from the received operating data, and (c) based on at least a portion of the ingested data, executing a predictive model that outputs an indicator of whether at least one event from a group of events is likely to occur at the given asset within a given period of time in the future; the computing system making a determination that the indicator satisfies a threshold condition; in response to the determination, the computing system transitioning from operating in the first mode to operating in a second mode in which the computing system ingests a second set of data variables received from the given asset, wherein the first set of data variables and the second set of data variables differ by at least one data variable; and while operating in the second mode, the computing system (a) receiving operating data from the given asset and (b) ingesting the second set of data variables from the received operating data. 17. The computer-implemented method of claim 16, wherein each data variable in the first and second sets of data variables comprises an output of a respective sensor at the given asset. 18. The computer-implemented method of claim 16, wherein the group of events comprises a group of one or more failures, and wherein the predictive model outputs a health metric indicating whether at least one failure from the group of failures is likely to occur at the given asset within the given period of time in the future. 19. The computer-implemented method of claim 18, wherein the predictive model is defined based on historical operating data comprising (i) historical abnormal-condition data associated with one or more failures that occurred at the plurality of assets in the past and (ii) historical sensor data indicating operating conditions of the plurality of assets in the past. 20. The computer-implemented method of claim 16, wherein the second set of data variables includes one or more data variables that are not included in the first set of data variables.