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 …

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 …

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 …

Meta-learning, a concept from the area of automated machine learning, aims at providing
decision support for data scientists by recommending a suitable setting (a machine learning …

In recent years, the evolving of IoT (Internet of Things) has resulted in the deployment of
massive numbers of sensors in various fields, such as the Energy and Utility (E&U) industry …

Persistent homology has proven itself quite efficient in the topological and qualitative
comparison of filtered topological spaces, when invariance with respect to every …

Routing channels in a field-programmable gate array contain predefined wiring segments of
various lengths. These may be connected to the pins of the gates or joined end-to-end to …

Efficiently finding similar segments or motifs in time series data is a fundamental task that,
due to the ubiquity of these data, is present in a wide range of domains and situations …

Most existing work on sequence databases use correlation (eg, Euclidean distance and
Pearson correlation) as a core function for various analytical tasks. Typically, it requires …