Efficient Retrieval of Similar Time Sequences Using DFT

cient Retrieval of Similar Time Sequences Using DFTDavood

We propose an improvement of the known DFT-based indexing technique for fast retrieval of similar time sequences. We use the last few Fourier coee-cients in the distance computation without storing them in the index since every coeecient at the end is the complex conjugate of a coeecient at the beginning and as strong as its counterpart. We show analytically that this observation can accelerate...

Physical Database Design for Efficient Time-Series Similarity Search

Similarity search in time-series databases finds such data sequences whose changing patterns are similar to that of a query sequence. For efficient processing, it normally employs a multi-dimensional index. In order to alleviate the well-known dimensionality curse, the previous methods for similarity search apply the Discrete Fourier Transform (DFT) to data sequences, and take only the first tw...

Optimal Construction of Multi-Dimensional Indexes in Time-Series Databases: A Physical Database Design Approach

Similarity search in time-series databases is an operation that finds such data sequences whose changing patterns are similar to that of a query sequence. Typically, it hires the multi-dimensional index for its efficient processing. In order to alleviate the dimensionality curse, a problem in high-dimensional cases, the previous methods for similarity search apply the Discrete Fourier Transform...

Efficient Retrieval of Similar Time Sequences Under Time Warping

A Survey of Recent Methods for Efficient Retrieval of Similar Time Sequences

Time sequences occur in many applications, ranging from science and technology to business and entertainment. In many of these applications, searching through large, unstructured databases based on sample sequences is often desirable. Such similaritybased retrieval has attracted a great deal of attention in recent years. Although several different approaches have appeared, most are based on the...