Continuous Probabilistic Nearest-Neighbor Queries for Uncertain Trajectories

This work addresses the problem of processing continuous nearest neighbor (NN) queries for moving objects trajectories when the exact position of a given object at a particular time instant is not known, but is bounded by an uncertainty region. As has already been observed in the literature, the answers to continuous NN-queries in spatio-temporal settings are time parameterized in the sense that the objects in the answer vary over time. Incorporating uncertainty in the model yields additional attributes that affect the semantics of the answer to this type of queries. In this work, we formalize the impact of uncertainty on the answers to the continuous probabilistic NN-queries, provide a compact structure for their representation and efficient algorithms for constructing that structure. We also identify syntactic constructs for several qualitative variants of continuous probabilistic NN-queries for uncertain trajectories and present efficient algorithms for their processing. 1

PA-Tree: A Parametric Indexing Scheme for Spatio-temporal Trajectories

Abstract. Many new applications involving moving objects require the collec-tion and querying of trajectory data, so efficient indexing methods are needed to support complex spatio-temporal queries on such data. Current work in this domain has used MBRs to approximate trajectories, which fail to capture some basic properties of trajectories, including smoothness and lack of internal area. This mismatch leads to poor pruning when such indices are used. In this work, we revisit the issue of using parametric space indexing for historical trajectory data. We approximate a sequence of movement functions with single continuous polynomial. Since trajectories tend to be smooth, our approximations work well and yield much finer approximation quality than MBRs. We present the PA-tree, a parametric index that uses this new approximation method. Experiments show that PA-tree construction costs are orders of magnitude lower than that of com-peting methods. Further, for spatio-temporal range queries, MBR-based methods require 20%–60 % more I/O than PA-trees with clustered indicies, and 300%– 400 % more I/O than PA-trees with non-clustered indicies.

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

FP-Flock: An algorithm to find frequent flock patterns in spatio-temporal databases

The widespread use of location systems such as GPS and RFID along with the massive use of mobile devices have allowed a significant increase in the availability and access to spatio-temporal databases in recent years. This large amount of data has motivated the development of more efficient techniques to process queries about the behavior of moving objects, like discovering behavior patterns among trajectories of moving objects over a continuous period of time. Several studies have focused on the query patterns that capture the behavior of entities in motion, which are reflected in collaborations such as mobile clusters, convoy queries and flock patterns. In this paper, we provided an algorithm to find clustering patterns, traditionally known as flocks, which is based on a frequent pattern mining approach. Twoalternatives for detecting patterns, both online and offline, are presented. Both alternatives were compared with two algorithms of the same type, Basic Flock Evaluation (BFE) and LCMFlock. The performance and behavior was measured in different datasets, both synthetic and real.Sociedad Argentina de Informática e Investigación Operativ

FP-Flock: An algorithm to find frequent flock patterns in spatio-temporal databases

The widespread use of location systems such as GPS and RFID along with the massive use of mobile devices have allowed a significant increase in the availability and access to spatio-temporal databases in recent years. This large amount of data has motivated the development of more efficient techniques to process queries about the behavior of moving objects, like discovering behavior patterns among trajectories of moving objects over a continuous period of time. Several studies have focused on the query patterns that capture the behavior of entities in motion, which are reflected in collaborations such as mobile clusters, convoy queries and flock patterns. In this paper, we provided an algorithm to find clustering patterns, traditionally known as flocks, which is based on a frequent pattern mining approach. Twoalternatives for detecting patterns, both online and offline, are presented. Both alternatives were compared with two algorithms of the same type, Basic Flock Evaluation (BFE) and LCMFlock. The performance and behavior was measured in different datasets, both synthetic and real.Sociedad Argentina de Informática e Investigación Operativ

FP-Flock: An algorithm to find frequent flock patterns in spatio-temporal databases

The widespread use of location systems such as GPS and RFID along with the massive use of mobile devices have allowed a significant increase in the availability and access to spatio-temporal databases in recent years. This large amount of data has motivated the development of more efficient techniques to process queries about the behavior of moving objects, like discovering behavior patterns among trajectories of moving objects over a continuous period of time. Several studies have focused on the query patterns that capture the behavior of entities in motion, which are reflected in collaborations such as mobile clusters, convoy queries and flock patterns. In this paper, we provided an algorithm to find clustering patterns, traditionally known as flocks, which is based on a frequent pattern mining approach. Twoalternatives for detecting patterns, both online and offline, are presented. Both alternatives were compared with two algorithms of the same type, Basic Flock Evaluation (BFE) and LCMFlock. The performance and behavior was measured in different datasets, both synthetic and real.Sociedad Argentina de Informática e Investigación Operativ