Efficient Discovering of Top-K Sequential Patterns in Event-Based Spatio-Temporal Data

TL;DR

This paper addresses the challenge of efficiently discovering the top-K most significant sequential patterns in event-based spatio-temporal data, where setting a significance threshold is difficult, by proposing a new approach focused on top-K pattern discovery.

Contribution

It introduces a novel method for discovering the most important sequential patterns without relying on significance thresholds, tailored for complex spatio-temporal datasets.

Findings

Proposed an effective algorithm for Top-K pattern discovery.

Demonstrated improved efficiency over existing threshold-based methods.

Validated approach on real spatio-temporal datasets.

Abstract

We consider the problem of discovering sequential patterns from event-based spatio-temporal data. The dataset is described by a set of event types and their instances. Based on the given dataset, the task is to discover all significant sequential patterns denoting some attraction relation between event types occurring in a pattern. Already proposed algorithms discover all significant sequential patterns based on the significance threshold, which minimal value is given by an expert. Due to the nature of described data and complexity of discovered patterns, it may be very difficult to provide reasonable value of significance threshold. We consider the problem of effective discovering of K most important patterns in a given dataset (that is discovering of Top-K patterns).