Adaptive similarity search in streaming time series with sliding windows

The challenge in a database of evolving time series is to provide efficient algorithms and access methods for query processing, taking into consideration the fact that the database changes continuously as new data become available. Traditional access methods that continuously update the data are considered inappropriate, due to significant update costs. In this paper, we use the IDC-Index (Incremental DFT Computation – Index), an efficient technique for similarity query processing in streaming time series. The index 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. We focus both on range and nearest-neighbor queries, since both types are frequently used in modern applications. An important characteristic of the proposed approach is its ability to adapt to the update frequency of the data streams. By using a simple heuristic approach...

• Type: Journal
• Year: 2007
• Where: DKE
• Authors: Maria Kontaki, Apostolos N. Papadopoulos, Yannis Manolopoulos
• Keywords: Access Method, DKE 2007, Time Series, Update