Capturing the temporal constraints of gradual patterns

TL;DR

This paper introduces a novel ant colony optimization method for gradual pattern mining, extends it to include fuzzy-temporal patterns with approximate time lags, and proposes a cloud-based data crossing model for large-scale IoT time-series data analysis.

Contribution

It presents a new ant colony optimization approach for gradual pattern mining, extends techniques to fuzzy-temporal patterns, and offers a cloud platform integration for IoT data analysis.

Findings

Effective extraction of gradual patterns with temporal lags.

Successful implementation of the ant colony optimization technique.

Feasibility of cloud-based integration for large-scale IoT data.

Abstract

Gradual pattern mining allows for extraction of attribute correlations through gradual rules such as: "the more X, the more Y". Such correlations are useful in identifying and isolating relationships among the attributes that may not be obvious through quick scans on a data set. For instance, a researcher may apply gradual pattern mining to determine which attributes of a data set exhibit unfamiliar correlations in order to isolate them for deeper exploration or analysis. In this work, we propose an ant colony optimization technique which uses a popular probabilistic approach that mimics the behavior biological ants as they search for the shortest path to find food in order to solve combinatorial problems. In our second contribution, we extend an existing gradual pattern mining technique to allow for extraction of gradual patterns together with an approximated temporal lag between the affected gradual item sets. Such a pattern is referred to as a fuzzy-temporal gradual pattern and it may take the form: "the more X, the more Y, almost 3 months later". In our third contribution, we propose a data crossing model that allows for integration of mostly gradual pattern mining algorithm implementations into a Cloud platform. This contribution is motivated by the proliferation of IoT applications in almost every area of our society and this comes with provision of large-scale time-series data from different sources.