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Mining frequent patterns over data streams is an interesting problem due to its wide application area. The researchers in this field have been facing two key challenges, namely reduction in runtime and memory usage. In this study, a novel method for efficient mining of frequent patterns over data streams is proposed. The method is based on sliding window model which divides the window into a number of panes. This method provides a new sliding window mechanism by utilizing a set of simple short lists. Each list stores related information about an item in the sliding window. The proposed mechanism dynamically adopts itself with the concept change. This method is empirically evaluated against recently proposed pane based sliding window algorithms. Experimental results on synthetically generated and real life data streams show the superiority of the proposed method with multiple orders of magnitude in terms of runtime and memory usage with respect to other pane based sliding window algorithms.

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