Big data production in industrial Internet of Things (IIoT) is evident due to the massive
deployment of sensors and Internet of Things (IoT) devices. However, big data processing is …

There has been an exponential growth in the number of low‐cost heterogeneous sensor
devices that are connected to the internet in the existing infrastructure of smart cities in the …

We are concerned with the issue of detecting model changes in probability distributions. We
specifically consider the strategies based on the minimum description length (MDL) …

In this paper, we propose a compression-based anomaly detection method for time series
and sequence data using a pattern dictionary. The proposed method is capable of learning …

We address the issue of detecting changes of models that lie behind a data stream. The
model refers to an integer-valued structural information such as the number of free …

The aim of using atypicality is to extract small, rare, unusual and interesting pieces out of big
data. This complements statistics about typical data to give insight into data. In order to find …

This paper proposes a new method for detecting the uncertainty of a latent structure. We
consider the case where the latent structure of dataset changes gradually over time, with the …

We are concerned with the issue of detecting changes and their signs from a data stream.
For example, when given time series of COVID-19 cases in a region, we may raise early …

Dynamic networks are ubiquitous in many domains for modelling evolving graph-structured
data and detecting changes allows us to understand the dynamic of the domain …

Change mining is one of the main subjects of analysis on time-evolving data. Regardless of
the distribution of the changes over the data, often the algorithms return very large sets of …