Multiplexing Trajectories of Moving Objects

Abstract. Continuously tracking mobility of humans, vehicles or merchandise not only provides streaming, real-time information about their current whereabouts, but can also progressively assemble historical traces, i.e., their evolving trajectories. In this paper, we outline a framework for online detection of groups of moving objects with approximately similar routes over the recent past. Further, we propose an encoding scheme for synthesizing an indicative trajectory that collectively represents movement features pertaining to objects in the same group. Preliminary experimentation with this multiplexing scheme shows encouraging results in terms of both maintenance cost and compression accuracy. Motivation As smartphones and GPS-enabled devices proliferate and location-based services penetrate into the market, managing the bulk of rapidly accumulating traces of objects' movement becomes all the more crucial for monitoring applications. Apart from effective storage and timely response to user requests, data exploration and trend discovery against collections of evolving trajectories seems very challenging. From detection of flocks We have begun developing a stream-based framework for multiplexing trajectories of objects that approximately travel together over a recent time interval. Our perception is that a symbolic encoding for sequences of trajectory segments can offer a rough, yet succinct abstraction of their concurrent evolution. Taking advantage of inherent properties, such as heading, speed and current position, we can continuously report groups of objects with similar motion traces. Then, we may regularly construct an indicative path per detected group, which actually epitomizes spatiotemporal features shared by its participating objects. Overall, such a scheme could be beneficial for: -Data compression: collectively represent traces of multiple objects with a single "delegate" that suitably approximates their common recent movement. -Data discovery: find trends or motion patterns from real-time location feeds. -Data visualization: estimate significance of each multiplexed group of trajectories and illustrate its mutability across time (e.g., on maps)

Maintaining consistent results of continuous queries under diverse window specifications

Continuous queries applied over nonterminating data streams usually specify windows in order to obtain an evolvingd -yet restricted- set of tuples and thus provide timely and incremental results. Although sliding windows get frequently employed in many user requests, additional types like partitioned or landmark windows are also available in stream processing engine

Multi-scale window specification over streaming trajectories

Enormous amounts of positional information are collected by monitoring applications in domains such as fleet management cargo transport wildlife protection etc. With the advent of modern location-based services processing such data mostly focuses on providing real-time response to a variety of user requests in continuous and scalable fashion. An important class of such queries concerns evolving trajectories that continuously trace the streaming locations of moving objects like GPS-equipped vehicles commodities with RFID\u27s people with smartphones etc. In this work we propose an advanced windowing operator that enables online incremental examination of recent motion paths at multiple resolutions for numerous point entities. When applied against incoming positions this window can abstract trajectories at coarser representations towards the past while retaining progressively finer features closer to the present. We explain the semantics of such multi-scale sliding windows through parameterized functions that reflect the sequential nature of trajectories and can effectively capture their spatiotemporal properties. Such window specification goes beyond its usual role for non-blocking processing of multiple concurrent queries. Actually it can offer concrete subsequences from each trajectory thus preserving continuity in time and contiguity in space along the respective segments. Further we suggest language extensions in order to express characteristic spatiotemporal queries using windows. Finally we discuss algorithms for nested maintenance of multi-scale windows and evaluate their efficiency against streaming positional data offering empirical evidence of their benefits to online trajectory processing