A mechanism is provided in a stream computing platform for data stream change detection and model swapping. The mechanism builds a model for each input data stream in a stream computing platform. Each tuple of each given input data stream is tagged with a key corresponding to the given input data stream. The mechanism performs an operation on each input data stream using its corresponding model. The mechanism detects a misdirected input data stream, which is tagged with a key that does not correspond to the misdirected input data stream. The mechanism pauses the misdirected input data. stream swaps a model corresponding to the misdirected input data stream with another model corresponding to another paused input data stream.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method, in a data processing system, for data stream change detection and model swapping in a stream computing platform, the method comprising: building a model for each input data stream in a stream computing platform, wherein each tuple of each given input data stream is tagged with a key corresponding to the given input data stream; performing an operation on each input data stream using its corresponding model; detecting a misdirected input data stream, wherein the misdirected input data stream is tagged with a key that does not correspond to the misdirected input data stream; pausing the misdirected input data stream; and swapping a model corresponding to the misdirected input data stream with another model corresponding to another paused input data stream. 2. The method of claim 1, wherein building the model for each input data stream comprises performing machine learning on a predetermined number of initial input tuples to form a machine learning model. 3. The method of claim 2, wherein the machine learning model is a time series forecasting model. 4. The method of claim 3, wherein the time series forecasting model is an ARIMA model or a Holt-Winters model. 5. The method of claim 1, wherein performing an operation on each input data stream comprises performing a time series forecasting operation on each input data stream using its corresponding model. 6. The method of claim 1, wherein detecting the misdirected input data stream comprises: performing error detection on a result of the operation for a given input data stream; and responsive to determining that a number of errors in the results of the operation for the given input data stream exceeds a threshold, determining that the given input data stream is misdirected. 7. The method of claim 1, wherein detecting the misdirected input data stream comprises: responsive to determining the input data stream does not fit its corresponding model, determining that the given input data stream is misdirected. 8. The method of claim 1, further comprising: responsive to pausing the misdirected input data stream, entering the misdirected input data stream in a hash table that lists current paused data streams and their corresponding model parameters. 9. The method of claim 8, wherein swapping the model corresponding to the misdirected input data stream with another model corresponding to another paused input data stream comprises: swapping the model corresponding to the misdirected input data stream with each other model corresponding to a paused input data stream in the hash table; determining which other model is a best fit for the misdirected input data stream; and swapping the model corresponding to the misdirected input data stream with the other model that is the best fit for the misdirected input data stream. 10-20. (canceled)