In one embodiment, a method includes receiving a data stream at an analytics device, applying at the analytics device, continuous streaming queries to the data stream to build a plurality of models simultaneously for a plurality of time windows, each of the models comprising an incremental machine learning algorithm with parameters optimized for one of the time windows, validating the models in parallel using real-time data at the analytics device, selecting at least one of the models based on a comparison of validation results for the models, and applying the selected model to the real-time data to generate a data prediction at the analytics device. An apparatus and logic are also disclosed herein.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method comprising: receiving a data stream at an analytics device; applying at the analytics device, continuous streaming queries to the data stream to build a plurality of models simultaneously for a plurality of time windows, each of said plurality of models comprising an incremental machine learning algorithm with parameters optimized for one of said plurality of time windows; validating said plurality of models in parallel using real-time data at the analytics device; selecting at least one of said plurality of models based on a comparison of validation results for said plurality of models; and applying said at least one selected model to said real-time data to generate a data prediction at the analytics device. 2. The method of claim 1 further comprising dynamically modifying said plurality of models as conditions change over time. 3. The method of claim 1 wherein the analytics device comprises a stream database. 4. The method of claim 1 wherein said plurality of models are built utilizing UDFs/UDAs (User Defined Functions/User Defined Aggregates). 5. The method of claim 1 further comprising ranking said plurality of models based on said comparison of validation results. 6. The method of claim 5 wherein selecting comprises selecting high ranked models and combining said high ranked models for use in generating said data prediction. 7. The method of claim 1 further comprising continuously updating said plurality of models based on said real-time data. 8. The method of claim 1 wherein UDFs/UDAs (User Defined Functions/User Defined Aggregates) are used to validate said plurality of models and generate said data prediction. 9. The method of claim 1 wherein each of said plurality of time windows covers a plurality of said models. 10. The method of claim 9 wherein selecting at least one of said plurality of models comprises selecting a set of models and generating a final predictive model from said set of models. 11. An apparatus comprising: a model distributor operable to process data streams according to continuous streaming queries; a modeler operable to build a plurality of models simultaneously for a plurality of time windows, each of said plurality of models comprising an incremental machine learning algorithm with parameters optimized for one of said plurality of time windows; a model validator operable to validate said plurality of models using real-time data and select at least one of said plurality of models based on a comparison of validation results for said plurality of models; and a model predictor operable to apply said at least one selected model to said real-time data to generate a data prediction. 12. The apparatus of claim 11 further comprising a stream database operable to process said real-time data and memory for storing said processed data. 13. The apparatus of claim 11 wherein the modeler is further operable to dynamically modify said plurality of models as conditions change over time. 14. The apparatus of claim 11 wherein said plurality of models are built utilizing UDFs/UDAs (User Defined Functions/User Defined Aggregates). 15. The apparatus of claim 11 wherein the model validator is further operable to rank said plurality of models based on said comparison of validation results. 16. Logic encoded on one or more non-transitory computer readable media for execution and when executed operable to: process a data stream; apply continuous streaming queries to the data stream to build a plurality of models simultaneously for a plurality of time windows, each of said plurality of models comprising an incremental machine learning algorithm with parameters optimized for one of said plurality of time windows; validate said plurality of models using real-time data; select at least one of said plurality of models based on a comparison of validation results for said plurality of models; and apply said at least one selected model to said real-time data to generate a data prediction at the analytic device. 17. The logic of claim 16 further operable to dynamically modify said plurality of models based on said real-time data. 18. The logic of claim 16 further operable to rank said plurality of models based on said comparison of validation results. 19. The logic of claim 16 wherein said plurality of models are built utilizing UDFs/UDAs (User Defined Functions/User Defined Aggregates). 20. The logic of claim 16 wherein each of said plurality of time windows covers a plurality of models.