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 …

Time series is an important class of temporal data objects and it can be easily obtained from
scientific and financial applications. A time series is a collection of observations made …

The proliferation of trajectory data in various application domains has inspired tremendous
research efforts to analyze large-scale trajectory data from a variety of aspects. A …

Most time series data mining algorithms use similarity search as a core subroutine, and thus
the time taken for similarity search is the bottleneck for virtually all time series data mining …

The previous decade has brought a remarkable increase of the interest in applications that
deal with querying and mining of time series data. Many of the research efforts in this context …

Clustering is a powerful vehicle to reveal and visualize structure of data. When dealing with
time series, selecting a suitable measure to evaluate the similarities/dissimilarities within the …

Most time series data mining algorithms use similarity search as a core subroutine, and thus
the time taken for similarity search is the bottleneck for virtually all time series data mining …

Temporal data mining deals with the harvesting of useful information from temporal data.
New initiatives in health care and business organizations have increased the importance of …

Advances of the sensor and GPS techniques have motivated the proliferation of trajectory
data in a wide spectrum of applications. Trajectory similarity computation is one of the most …

A novel metric for time series, called Move-Split-Merge (MSM), is proposed. This metric uses
as building blocks three fundamental operations: Move, Split, and Merge, which can be …