Discovering Spatially Interesting Patterns in Big Geo-Referenced Sequential Databases

Abstract

Geo referenced time series is a cornerstone in spatiotemporal data analysis, revealing invaluable patterns that drive socio-economic development. Previous studies have modeled these series as transactional databases. However, traditional transactional databases fail to account for the sequential order of items in a series, leading to the incomplete identification of patterns in applications where the sequence order is significant. Motivated by this limitation, this paper introduces a novel data transformation technique that converts geo-referenced time series into a sequential database, preserving the order of items. This approach is advantageous over traditional models because it maintains the temporal sequence, which is crucial for accurate pattern identification. We present a novel model for discovering geo-referenced frequent sequential patterns (GFSPs) within this transformed database. These patterns are termed “interesting” due to their ability to uncover significant temporal and spatial relationships within the data, which are crucial for understanding complex phenomena like traffic congestion. Our method reveals patterns that are not only frequent but also hold practical implications for traffic management and urban planning. Additionally, we introduce a neighborhood-aware exploration technique designed to optimize search space and computational efficiency. Experimental results validate the effectiveness and efficiency of our proposed algorithm. The utility of the identified patterns is demonstrated through a case study focused on frequent congestion patterns in road network data, underscoring the relevance of the sequential database approach.

Keywords

• Geo-referenced frequent sequential patterns
• sequence mining
• spatiotemporal data
• big data analytics
• downward closure property