A Data Model and Predicate Logic for Trajectory Data (Extended Version)

TL;DR

This paper introduces a comprehensive data model and predicate logic for trajectory data, effectively capturing their spatio-temporal properties and uncertainty, enabling advanced querying and analysis of increasing trajectory datasets.

Contribution

It presents a novel data model treating trajectories as first-class entities and a predicate logic for querying under uncertainty, advancing trajectory data management.

Findings

The data model fully captures spatio-temporal properties of trajectories.

The predicate logic supports expressive queries under various uncertainty assumptions.

It can represent all spatial and temporal relations from prior research.

Abstract

With recent sensor and tracking technology advances, the volume of available trajectory data is steadily increasing. Consequently, managing and analyzing trajectory data has seen significant interest from the research community. The challenges presented by trajectory data arise from their spatio-temporal nature as well as the uncertainty regarding locations between sampled points. In this paper, we present a data model that treats trajectories as first-class citizens, thus fully capturing their spatio-temporal properties. We also introduce a predicate logic that enable query processing under different uncertainty assumptions. Finally, we show that our predicate logic is expressive enough to capture all spatial and temporal relations put forward by previous work.