Feature engineering can be performed on time series data making the data easy to manipulate and accessible to business users for analysis according to existing best practices. A computer system can, after receiving time series data related to a device, contextualize the time series data based on business data related to the device from, for example, an enterprise resource planning database. The contextualized data can be windowed by a selected feature based on execution data related to the device from, for example, a manufacturing execution system database. The windowed data can be transformed into summary data using a time series transformation. The summary data can be easily manipulated by, for example, generating genetic maps of the segmented and transformed data for clustering or searching for anomalies and patterns in response to user requests or automatically.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for performing feature engineering of time series data, the method comprising: receiving, at a computer system, time series data related to a device for a period of time, the time series data providing information about the device for the period of time; contextualizing, by the computer system, the time series data based on business data related to the device for the period of time to generate contextualized data; feature windowing, by the computer system, the contextualized data based on execution data related to the device for the period of time to generate a plurality of windows of data; transforming, by the computer system, each of the plurality of windows of data into summary data using a time series transformation to generate a plurality of summary data; and storing, by the computer system, each of the plurality of summary data. 2. The method for performing feature engineering of time series data of claim 1, wherein transforming each of the plurality of windows of data into summary data comprises: generating a plurality of strings by, for each window of data in the plurality of windows of data: normalizing the data in the window of data; smoothing the data in the window of data using piecewise aggregate approximation; and converting the data in the window of data into a string. 3. The method for performing feature engineering of time series data of claim 2, the method further comprising: converting each string of the plurality of strings into a genetic map of a plurality of genetic maps; clustering the plurality of genetic maps to identify pattern similarities; grouping the plurality of genetic maps based on the identified pattern similarities; and displaying the grouped genetic maps to a user. 4. The method for performing feature engineering of time series data of claim 2, the method further comprising: identifying, by the computer system, a pattern that represents an indication of a device failure; building, by the computer system, a predictive model based on the pattern; and sending, by the computer system, an alert based on applying the predictive model to current data. 5. The method for performing feature engineering of time series data of claim 1, the method further comprising: receiving, by the computer system, a selection of a second time series data from a user, the second time series data related to the device for a second period of time; transforming, by the computer system, the second time series data into selected summary data using the time series transformation; searching, by the computer system, the plurality of summary data for a pattern matching at least a portion of the selected summary data; and displaying, by the computer system, summary data from the plurality of summary data having the pattern to the user. 6. The method for performing feature engineering of time series data of claim 5, the method further comprising: predicting, by the computer system, a quality of a second product produced by the device during the second period of time based on a quality of a first product produced by the device during the period of time having the matching pattern. 7. The method for performing feature engineering of time series data of claim 1, wherein the time series transformation used is symbolic aggregate approximation (“SAX”) and each of the plurality of summary data is a string in a plurality of strings. 8. The method for performing feature engineering of time series data of claim 1, wherein the feature windowing is based on one of a time segment, a sliding window, or an event. 9. The method for performing feature engineering of time series data of claim 1, wherein the business data is data from an enterprise resource planning database and the execution data is from a manufacturing execution system database. 10. A system for performing feature engineering of time series data, the system comprising: a processor; and a memory having stored thereon instructions that, when executed by the processor, cause the processor to: receive time series data related to a device for a period of time, the time series data providing information about the device for the period of time; contextualize the time series data based on business data related to the device for the period of time to generate contextualized data; feature window the contextualized data based on execution data related to the device for the period of time to generate a plurality of windows of data; transform each of the plurality of windows of data into summary data using a time series transformation to generate a plurality of summary data; and store each of the plurality of summary data. 11. The system for performing feature engineering of time series data of claim 10, wherein the instructions further comprise instructions that, when executed by the processor, cause the processor to: generate a plurality of strings by, for each window of data in the plurality of windows of data: normalize the data in the window of data; smooth the data in the window of data using piecewise aggregate approximation; and convert the data in the window of data into a string. 12. The system for performing feature engineering of time series data of claim 11, wherein the instructions further comprise instructions that, when executed by the processor, cause the processor to: convert each string of the plurality of strings into a genetic map of a plurality of genetic maps; cluster the plurality of genetic maps to identify pattern similarities; group the plurality of genetic maps based on the identified pattern similarities; and display the grouped genetic maps to a user. 13. The system for performing feature engineering of time series data of claim 11, wherein the instructions further comprise instructions that, when executed by the processor, cause the processor to: identify a pattern that represents an indication of a device failure; build a predictive model based on the pattern; and send an alert based on applying the predictive model to current data. 14. The system for performing feature engineering of time series data of claim 10, wherein the instructions further comprise instructions that, when executed by the processor, cause the processor to: receive a selection of a second time series data from a user, the second time series data related to the device for a second period of time; transform the second time series data into selected summary data using the time series transformation; search the plurality of summary data for a pattern matching at least a portion of the selected summary data; and display summary data from the plurality of summary data having the pattern to the user. 15. The system for performing feature engineering of time series data of claim 14, wherein the instructions further comprise instructions that, when executed by the processor, cause the processor to: predict a quality of a second product produced by the device during the second period of time based on a quality of a first product produced by the device during the period of time having the matching pattern. 16. A processor-readable device, comprising instructions that, when executed by a processor, cause the processor to: receive time series data related to a device for a period of time, the time series data providing information about the device for the period of time; contextualize the time series data based on business data related to the device for the period of time to generate contextualized data; feature window the contextualized data based on execution data related to the device for the period of time to generate a plurality of windows of data; transform each of the plurality of windows of data into summary data using a time series transformation to generate a plurality of summary data; and store each of the plurality of summary data. 17. The processor-readable device of claim 16, further comprising instructions that, when executed by a processor, cause the processor to: generate a plurality of strings by, for each window of data in the plurality of windows of data: normalize the data in the window of data; smooth the data in the window of data using piecewise aggregate approximation; and convert the data in the window of data into a string. 18. The processor-readable device of claim 17, further comprising instructions that, when executed by a processor, cause the processor to: convert each string of the plurality of strings into a genetic map of a plurality of genetic maps; cluster the plurality of genetic maps to identify pattern similarities; group the plurality of genetic maps based on the identified pattern similarities; and display the grouped genetic maps to a user. 19. The processor-readable device of claim 17, further comprising instructions that, when executed by a processor, cause the processor to: identify a pattern that represents an indication of a device failure; build a predictive model based on the pattern; and send an alert based on applying the predictive model to current data. 20. The processor-readable device of claim 16, further comprising instructions that, when executed by a processor, cause the processor to: receive a selection of a second time series data from a user, the second time series data related to the device for a second period of time; transform the second time series data into selected summary data using the time series transformation; search the plurality of summary data for a pattern matching at least a portion of the selected summary data; predict a quality of a second product produced by the device during the second period of time based on a quality of a first product produced by the device during the period of time having the matching pattern; and display summary data from the plurality of summary data having the pattern and the predicted quality of the second product to the user.