Efficient similarity search in streaming time sequences

Query processing in data streams is a very important research direction. The challenge in a database of data streams is to provide efficient algorithms and access methods for query processing, taking into consideration the fact that the database changes continuously as new data arrive. Traditional access methods that continuously update the data are considered inefficient, due to the significant update costs. In this paper we present IDC-Index, an efficient technique for similarity query processing in streaming time sequences, which is based on a multidimensional access method enhanced with a deferred update policy and an incremental computation of the discrete Fourier transform (DFT), which is used as a feature extraction method. The method manages to reduce the number of false alarms examined and therefore achieves high answers/candidates ratio. Moreover, an extensive performance evaluation based on synthetic random walk and real time sequences have shown that the proposed technique outperforms significantly existing approaches for similarity range query processing.