Traminer is a Java library for preprocessing, managing and mining spatial trajectory data. Its aim is to help researchers, developers, practitioners and other non-expert professionals to manipulate various vehicle and pedestrian trajectories more easily without re-implementing most fundamental methods.

The architecture of Traminer consists of three layers and a data reader library, which collectively form a full stack of APIs for trajectory data analysis.

1. Data reader library: More often than not, raw trajectory data collected from business are stored and delivered in a specific data format. In order to make the upstream layers agnostic of the adopted format, this library provides a rich set of data reader classes and methods, which can load trajectory data with various formats and transform them into abstracted trajectory objects. Analogously, a set of readers for digital maps and Point-of-Interest datasets are also provided to load maps and PoI data on disk.

2. Data preprocessing layer: Raw trajectories should go through a series of preprocessing steps before they become suitable for indexing, querying and mining. This layer support most basic trajectory preprocessing functions, as is outlined in below.

• Noise filtering
• Trajectory compression
• Trajectory segmentation (stay point detection)
• Trajectory calibration
• Map matching (with map data)
• Trajectory annotation (with PoI data)

3. Data management layer: This layer deals with a (typically large) collection of trajectory data. It provides implementations of most representative trajectory similarity measures (e.g., LCSS, DTW, ERP, EDR) between two trajectories. It supports a variety of spatio-temporal queries on trajectory data. Indexing and query processing strategies can also be selected according to the storage and computation mode adopted by the business (i.e., disk-based single node, memory-based single node, disk-based cluster, memory-based cluster).

• Trajectory similarity measures
• Spatio-temporal index (platform dependent)
• Spatio-temporal queries (platform dependent)
• Semantic-aware trajectory queries (with PoI data)
• Network-based trajectory queries (with map data)

4. Data mining layer: This layer supports a broad range of data mining tasks for trajectory data, which can be directly used by relevant application developers and business analysts.

• Frequent pattern discovery
• Popular route mining
• Co-traveler discovery
• Clustering
• Outlier/event detection
• Semantic trajectory mining