A system and method for detecting behavior of a computing platform that includes obtaining platform data; for each data motif identifiers in a set data motif identifiers, performing data motif detection on data in an associated timescale, wherein a first data motif identifier operates on data in a first timescale, wherein a second data motif identifier operates on data in a second timescale, wherein the first timescale and second timescale are different; in a neural network model, synthesizing platform data anomaly detection with at least a set of features inputs from data motif detection of the set of motif identifiers; and signaling if a platform data anomaly is detected through the neural network model.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system for monitoring usage of a computing platform comprising: data ingester that collects time-based data of a computing platform, wherein the time-based data includes data of a plurality of data types; a set of motif identifier modules, wherein each motif identifier is configured to apply a machine learning process within one timescale of the data and output a motif signal, the set of motif identifier modules comprising a raw motif identifier module that operates on data in a base timescale, at least one motif identifier module that operates on data from a substantially weekly timescale, at least one motif identifier that operates on data from a substantially yearly timescale, a set of data samplers, wherein a data sampler of a one timescale couples a sampled data output to at least one motif identifier of the same the one timescale, and a neural network model that includes feature inputs of at least one layer coupled to the motif signal outputs, a combined motif layer, and including at least one output signal of the operational status of the computing platform. 2. A method for detecting behavior of a computing platform comprising: obtaining platform data; for each data motif identifiers in a set data motif identifiers, performing data motif detection on data in an associated timescale, wherein a first data motif identifier operates on data in a first timescale, wherein a second data motif identifier operates on data in a second timescale, wherein the first timescale and second timescale are different; in a neural network model, synthesizing platform data anomaly detection with at least a set of features inputs from data motif detection of the set of motif identifiers; and signaling if a platform data anomaly is detected through the neural network model. 3. The method of claim 2, further comprising for at least a subset of data motif identifiers, sampling the platform data according to the timescale of an associated data motif identifier, wherein the associated data motif identifier operates on the sampled collected data. 4. The method of claim 3, wherein the first timescale is substantially an hour-based timescale, wherein the second timescale is substantially a day-based timescale, wherein a third data motif identifier operates on data in a substantially week-based timescale, wherein a fourth motif identifier operates on data in substantially a month-based timescale and wherein a fifth motif identifier operates on data in substantially a year-based timescale. 5. The method of claim 2, wherein a third data motif identifier operates on data in a first timescale, wherein the data set of the third data motif identifier is different from the data operated on by the first data motif. 6. The method of claim 2, wherein synthesizing platform data anomaly detection further comprises combining output of multiple data motif identifiers in features of higher layer of the neural network model. 7. The method of claim 2, further comprising training the neural network model. 8. The method of claim 7, further comprising collecting new data within a new timescale associated with a new data motif identifier, and performing data motif detection on data of the new timescale when the new data motif identifier is trained. 9. The method of claim 2, wherein obtaining platform data comprises collecting event records and resource usage from within the computing platform. 10. The method of claim 9, wherein collecting event records and resource usage is scoped to events and usage scoped to an account of the computing platform. 11. The method of claim 2, wherein obtaining platform data comprises accessing data of an external computing platform. 12. The method for predicting data comprising: obtaining platform data; for each data motif identifiers in a set data motif identifiers, performing data motif detection on data in an associated time scale, wherein a first data motif identifier operates on data in a first timescale, wherein a second data motif identifier operates on data in a second timescale, wherein the first timescale and second timescale are different; in a neural network model, synthesizing platform data motif prediction with at least a set of features inputs from data motif detection of the set of motif identifiers; and signaling a platform data prediction generated through the neural network model. 13. The method of claim 12, further comprising, for at least a subset of data motif identifiers, sampling the platform data according to the timescale of an associated data motif identifier, wherein the associated data motif identifier operates on the sampled collected data. 14. The method of claim 13, wherein the first timescale is substantially an hour-based timescale, wherein the second timescale is substantially a day-based timescale, wherein a third data motif identifier operates on data in a substantially week-based timescale, and wherein a fourth motif identifier operates on data in substantially a year-based timescale. 15. The method of claim 12, wherein a third data motif identifier operates on data in a first timescale, wherein the data set of the third data motif identifier is different from the data operated on by the first data motif. 16. The method of claim 12, wherein synthesizing platform data anomaly detection further comprises combining output of multiple data motif identifiers in features of higher layer of the neural network model. 17. The method of claim 16, further comprising collecting new data within a new timescale associated with a new data motif identifier, and performing data motif detection on data of the new timescale when the new data motif identifier is trained. 18. The method of claim 12, wherein obtaining platform data comprises collecting event records and resource usage from within the computing platform. 19. The method of claim 12, wherein collecting event records and resource usage is scoped to events and usage scoped to an account of the computing platform.