document: Combining proactive and reactive predictions for data streams Mining data streams is important in both science and commerce . Two major challenges are ( 1 ) the data may grow without limit so that it is difficult to retain a long history ; and ( 2 ) the underlying concept of the data may change over time . Different from common practice that keeps recent raw data , this paper uses a measure of conceptual equivalence to organize the data history into a history of concepts . Along the journey of concept change , it identifies new concepts as well as re-appearing ones , and learns transition patterns among concepts to help prediction . Different from conventional methodology that passively waits until the concept changes , this paper incorporates proactive and reactive predictions . In a proactive mode , it anticipates what the new concept will be if a future concept change takes place , and prepares prediction strategies in advance . If the anticipation turns out to be correct , a proper prediction model can be launched instantly upon the concept change . If not , it promptly resorts to a reactive mode : adapting a prediction model to the new data . A system RePro is proposed to implement these new ideas . Experiments compare the system with representative existing prediction methods on various benchmark data sets that represent diversified scenarios of concept change . Empirical evidence demonstrates that the proposed methodology is an effective and efficient solution to prediction for data streams .
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