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 …

The amount of sensory data manifests an explosive growth due to the increasing popularity
of Wireless Sensor Networks (WSNs). The scale of sensory data in many applications has …

Periodicity detection is a crucial step in time series tasks, including monitoring and
forecasting of metrics in many areas, such as IoT applications and self-driving database …

We consider the problem of computing all-pair correlations in a warehouse containing a
large number (eg, tens of thousands) of time-series (or, signals). The problem arises in …

The extraction of energy from renewable sources is rapidly growing. The current pace of
technological development makes it commercially viable to harness energy from sun, wind …

As the volumes of sensor data being accumulated are likely to soar, data compression has
become essential in a wide range of sensor-data applications. This has led to a plethora of …

During the last decades, there is a vast data explosion in bioinformatics. Big data centres are
trying to face this data crisis, reaching high storage capacity levels. Although several …

In order to understand a complex system, we analyze its output or its log data. For example,
we track a system's resource consumption (CPU, memory, message queues of different …

We study the problem of mining and summarizing multiple time series effectively and
efficiently. We propose PLiF, a novel method to discover essential characteristics (" …

Data centres are large energy consumers today, and their consumption is expected to
increase further, driven by the growth in cloud services. The large monetary cost and the …