NSPRING: Normalization-supported SPRING for subsequence matching on time series streams

Mining sequences and patterns in time series data streams have a tremendous growth of interest in todays world. The rapid progress of data collection and the web technologies yield tremendous growth of flowing data in various complex forms that need to be analyzed on fly. Traditional data mining methods typically that require to process data by scanning it multiple times are infeasible for stream data applications. However, new techniques like SPRING attempts to solve these challenges by identifying sequences of patterns on time series streams, whose time and space complexity are linear. Unfortunately, SPRING does not support normalization. As many researchers accepted that normalization is necessary, so SPRING is not applicable for most data sets. In this paper, we are proposing an approach called NSPRING based on SPRING. NSPRING extends the advantages of SPRING, e.g. low in time and space complexity, while it can support normalization. More interestingly, NSPRING retains similar mining accuracy to SPRING.

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