In recent years, rapid advances in location-acquisition technologies have led to large amounts of time-stamped location data. Positioning technologies like Global Positioning System (GPS)-based, communication network-based (e.g., 4G or Wi-Fi), and proximity-based (e.g., Radio Frequency Identification) systems enable the tracking of various moving objects, such as vehicles and people. A trajectory is represented by a series of chronologically ordered sampling points. Each sampling point contains a spatial information, which is represented by a multidimensional coordinate in a geographical space, and a temporal information, which is represented by a timestamp. Trajectory data is the foundation for a wide spectrum of services driven and improved by trajectory data mining. By analyzing the movement behavior of individuals or groups of moving objects in large-scale trajectory data, improvements in various fields of applications could be achieved.
However, it is a challenging task to manage, store, and process trajectory data. Based on the characteristics of spatio-temporal trajectory data, there exist four key challenges: the data volume, the high update rate (data velocity), the query latency of analytical queries, and the inherent inaccuracy of the data. For these reasons, it is a nontrivial task to manage and store vast amounts of these data, which are rapidly accumulated. Especially, if we consider hybrid transactional and analytical workloads (so-called HTAP or mixed workloads), which are challenging concerning space and time complexity.
The scope of this topic is the analysis and evaluation of different trajectory compression techniques for columnar in-memory databases.
Contact: Keven Richly