Trajectory Detection and Summarization over Surveillance Data Streams Full text

Kostas Patroumpas, Eva Chondrodima, Nikos Pelekis, Yannis Theodoridis
Big Data Analytics for Time-Critical Mobility Forecasting, Vouros G. et al. (eds), Springer Nature Switzerland AG
Abstract. In this chapter, we present Synopses Generator, a stream-based processing framework that can provide online summarized representations of trajectories specifically for sailing vessels and flying aircraft. Assuming that surveillance data monitoring their locations over a large geographical area is available in a streaming fashion, this novel methodology drops any predictable positions (along trajectory segments of “normal” motion characteristics) with minimal loss in accuracy. Effectively, it can keep only those positions conveying salient mobility events (annotated as stop, change in speed, heading, or altitude, etc.), identified when the mobility pattern of a given vessel or aircraft changes significantly. Moreover, this framework specifies parametrized conditions for detecting such mobility features, as well as suitable heuristics that can eliminate inherent noise and can provide succinct trajectory synopses in one pass over the incoming streaming positions. A prototype implementation on top of Apache Flink and Kafka has been set up in modern cluster infrastructures to enable parallelization of the trajectory summarization process against such big mobility data. A comprehensive experimental evaluation has been conducted against various surveillance data in the maritime and aviation domain, and offers concrete evidence of its timeliness, scalability, and compression efficiency, with tolerable concessions to the quality of resulting trajectory approximations. The resulting compressed trajectories can be particularly useful in efficient online or offline post-processing (e.g., mobility analytics, statistics, pattern mining, etc.) while also facilitating their comparison irrespectively of differing update frequencies.