Traditional distance and density-based anomaly detection techniques are unable to detect
periodic and seasonality related point anomalies which occur commonly in streaming data …

The all-pairs-similarity-search (or similarity join) problem has been extensively studied for
text and a handful of other datatypes. However, surprisingly little progress has been made …

The ubiquity of time series data across almost all human endeavors has produced a great
interest in time series data mining in the last decade. While dozens of classification …

Anomaly detection problems arise in multiple applications, as discussed in the preceding
chapter. such as financial fraud, cyber intrusion, video surveillance, and medical image …

The last decade has seen a flurry of research on all-pairs-similarity-search (or similarity
joins) for text, DNA and a handful of other datatypes, and these systems have been applied …

Many time series data mining problems require subsequence similarity search as a
subroutine. Dozens of similarity/distance measures have been proposed in the last decade …

The problem of anomaly detection on time series is to predict whether a newly observed
time series novel or normal, to a set of training time series. It is very useful in many …

The problem of anomaly detection in time series has recently received much attention.
However, many existing techniques require the user to provide the length of a potential …

Recently, wireless sensor networks (WSNs) have been extensively deployed to monitor
environments. Sensor nodes are susceptible to fault generation due to hardware and …

Anomaly detection in time series is one of the fundamental issues in data mining that
addresses various problems in different domains such as intrusion detection in computer …