Detecting Continuous Group Movement Patterns in Densely Populated Areas
Andreas Morgen, Bernhard Seeger · 2025
Today, smart mobile devices are omnipresent and constantly generating massive spatio-temporal data streams.Among the challenging tasks in stream processing is the continuous derivation of higher-level knowledge, such as detecting movement patterns of objects and groups of objects with low latencies.In spatial stream processing, existing approaches to detecting groups are limited in their applicability because they are either designed for processing historical data only or address a simplified problem setting that is not sufficient for detecting continuously connected groups within densely populated areas.To address these deficiencies, we introduce a novel low-latency pattern-matching approach to group detection by combining recent results from complex event processing, spatial query processing, and community discovery.First, we formally introduce an expressive Complex Event Processing (CEP) operator offering continuous connectedness and life cycle management for group detection.Then, we propose efficient algorithms and various optimization strategies to improve throughput and overall latency.The results of our experiments confirm the efficiency of our approach and the positive effects of our optimization strategies.For cases where comparisons are possible, we report the results compared to existing approaches.