Evaluation of Concept Drift in Poisson Big Data Stream using Adaptive Sliding Windows
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Abstract
Involving with big data whose dynamically changes over time is one of the major problems in big data curation. In this research an adaptive sliding window will be presented, as an evaluation with memory and variable length of data stream. Adaptive sliding window concept is to maintain the variable window size in order to carry the latest read data stream arriving in Poisson process from which older ones based upon algorithm rules. We need to involve the change of concepts meaning (i.e. concept drift) which is necessary for data releases and sophisticate data links. The concept drift thus reflects the change of window size and provides statistics update from recent data. Our simulation runs both fixed and continuous data stream so that sliding window is applied to different processing of data curation. In this paper we have proposed Poisson and Random arrival model of data stream which will employ Massive Online Analysis (MOA) for evaluating the concept drift measurements. Stagger stream generator with the Hoeffding bound outperforms and results highest accuracy while Naïve Bayes learner with Gradually Change generator fits Poisson arrival pattern.
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