Recently, order-preserving pattern (OPP) mining, a new sequential pattern mining method,
has been proposed to mine frequent relative orders in a time series. Although frequent …

The aim of sequential pattern mining (SPM) is to discover potentially useful information from
a given sequence. Although various SPM methods have been investigated, most of these …

Recently, order-preserving pattern (OPP) mining has been proposed to discover some
patterns, which can be seen as trend changes in time series. Although existing OPP mining …

This paper explores recent achievements and novel challenges of the annoying privacy-
preserving big data stream mining problem, which consists in applying mining algorithms to …

Order-preserving pattern (OPP) mining is a type of sequential pattern mining method in
which a group of ranks of time series is used to represent an OPP. This approach can …

Mining frequent itemsets from transactional data streams has become very essential in
today's world with many applications such as stock market analysis, retail chain analysis …

In many applications, data objects can be represented as sets. For example, in video on-
demand and social network services, the user data consists of a set of movies that have …

Intervals have been generated in many applications (eg, temporal databases), and they are
often associated with weights, such as prices. This paper addresses the problem of …

Complex event monitoring is an important problem in data streams that has drawn much
attention. Most previous work assumes that the user knows and provides a complex event …

The frequent pattern mining over data streams is a very important problem for many
applications. However, many researches investigate a single stream in which every …