In almost every scientific field, measurements are performed over time. These observations
lead to a collection of organized data called time series. The purpose of time-series data …

Search operations in databases require special support at the physical level. This is true for
conventional databases as well as spatial databases, where typical search operations …

At SIGMOD 2015, an article was presented with the title “DBSCAN Revisited: Mis-Claim, Un-
Fixability, and Approximation” that won the conference's best paper award. In this technical …

The problem of indexing time series has attracted much interest. Most algorithms used to
index time series utilize the Euclidean distance or some variation thereof. However, it has …

The parallel explosions of interest in streaming data, and data mining of time series have
had surprisingly little intersection. This is in spite of the fact that time series data are typically …

This paper deals with finding outliers (exceptions) in large, multidimensional datasets. The
identification of outliers can lead to the discovery of truly unexpected knowledge in areas …

Many high level representations of time series have been proposed for data mining,
including Fourier transforms, wavelets, eigenwaves, piecewise polynomial models, etc …

The problem of similarity search in large time series databases has attracted much attention
recently. It is a non-trivial problem because of the inherent high dimensionality of the data …

This paper deals with finding outliers (exceptions) in large, multidimensional datasets. The
identification of outliers can lead to the discovery of truly unexpected knowledge in areas …

For similarity search in high-dimensional vector spaces (or 'HDVSs'), researchers have
proposed a number of new methods (or adaptations of existing methods) based, in the main …