Elias Frentzos

Nearest neighbor search on moving object trajectories

With the increasing number of Mobile Location Services (MLS), the need for effective k-NN query processing over historical trajectory data has become the vehicle for data analysis, thus improving existing or even proposing new services. In this paper, we investigate mechanisms to perform NN search on R-tree-like structures storing historical information about moving object trajectories. The proposed branch-and-bound algorithms vary with respect to the type of the query object (stationary or moving point) as well as the type of the query result (continuous or not). We also propose novel metrics to support our search ordering and pruning strategies. Using the implementation of the proposed algorithms on a member of the R-tree family for trajectory data (the TB-tree), we demonstrate their scalability and efficiency through an extensive experimental study using synthetic and real datasets.

Algorithms for Nearest Neighbor Search on Moving Object Trajectories

Nearest Neighbor (NN) search has been in the core of spatial and spatiotemporal database research during the last decade. The literature on NN query processing algorithms so far deals with either stationary or moving query points over static datasets or future (predicted) locations over a set of continuously moving points. With the increasing number of Mobile Location Services (MLS), the need for effective k-NN query processing over historical trajectory data has become the vehicle for data analysis, thus improving existing or even proposing new services. In this paper, we investigate mechanisms to perform NN search on R-tree-like structures storing historical information about moving object trajectories. The proposed (depth-first and best-first) algorithms vary with respect to the type of the query object (stationary or moving point) as well as the type of the query result (historical continuous or not), thus resulting in four types of NN queries. We also propose novel metrics to support our search ordering and pruning strategies. Using the implementation of the proposed algorithms on two members of the R-tree family for trajectory data (namely, the TB-tree and the 3D-R-tree), we demonstrate their scalability and efficiency through an extensive experimental study using large synthetic and real datasets.

Clustering uncertain trajectories

Knowledge discovery in Trajectory Databases (TD) is an emerging field which has recently gained great interest. On the other hand, the inherent presence of uncertainty in TD (e.g., due to GPS errors) has not been taken yet into account during the mining process. In this paper, we study the effect of uncertainty in TD clustering and introduce a three-step approach to deal with it. First, we propose an intuitionistic point vector representation of trajectories that encompasses the underlying uncertainty and introduce an effective distance metric to cope with uncertainty. Second, we devise CenTra, a novel algorithm which tackles the problem of discovering the Centroid Trajectory of a group of movements taking into advantage the local similarity between portions of trajectories. Third, we propose a variant of the Fuzzy C-Means (FCM) clustering algorithm, which embodies CenTra at its update procedure. Finally, we relax the vector representation of the Centroid Trajectories by introducing an algorithm that post-processes them, as such providing these mobility patterns to the analyst with a more intuitive representation. The experimental evaluation over synthetic and real world TD demonstrates the efficiency and effectiveness of our approach.

Building real-world trajectory warehouses

The flow of data generated from low-cost modern sensing technologies and wireless telecommunication devices enables novel research fields related to the management of this new kind of data and the implementation of appropriate analytics for knowledge extraction. In this work, we investigate how the traditional data cube model is adapted to trajectory warehouses in order to transform raw location data into valuable information. In particular, we focus our research on three issues that are critical to trajectory data warehousing: (a) the trajectory reconstruction procedure that takes place when loading a moving object database with sampled location data originated e.g. from GPS recordings, (b) the ETL procedure that feeds a trajectory data warehouse, and (c) the aggregation of cube measures for OLAP purposes. We provide design solutions for all these issues and we test their applicability and efficiency in real world settings.

HERMES: aggregative LBS via a trajectory DB engine

We present HERMES, a prototype system based on a powerful query language for trajectory databases, which enables the support of aggregative Location-Based Services (LBS). The key observation that motivates HERMES is that the more the knowledge in hand about the trajectory of a mobile user, the better the exploitation of the advances in spatio-temporal query processing for providing intelligent LBS. HERMES is fully incorporated into a state-of-the-art Object-Relational DBMS, and its demonstration illustrates its flexibility and usefulness for delivering custom-defined LBS.

Clustering Trajectories of Moving Objects in an Uncertain World

Mining Trajectory Databases (TD) has recently gained great interest due to the popularity of tracking devices. On the other hand, the inherent presence of uncertainty in TD (e.g., due to GPS errors) has not been taken yet into account during the mining process. In this paper, we study the effect of uncertainty in TD clustering and introduce a three-step approach to deal with it. First, we propose an intuitionistic point vector representation of trajectories that encompasses the underlying uncertainty and introduce an effective distance metric to cope with uncertainty. Second, we devise CenTra, a novel algorithm which tackles the problem of discovering the Centroid Trajectory of a group of movements. Third, we propose a variant of the Fuzzy C-Means (FCM) clustering algorithm, which embodies CenTra at its update procedure. The experimental evaluation over real world TD demonstrates the efficiency and effectiveness of our approach.

T-Warehouse: Visual OLAP analysis on trajectory data

Technological advances in sensing technologies and wireless telecommunication devices enable novel research fields related to the management of trajectory data. As it usually happens in the data management world, the challenge after storing the data is the implementation of appropriate analytics for extracting useful knowledge. However, traditional data warehousing systems and techniques were not designed for analyzing trajectory data. Thus, in this work, we demonstrate a framework that transforms the traditional data cube model into a trajectory warehouse. As a proof-of-concept, we implemented T-WAREHOUSE, a system that incorporates all the required steps for Visual Trajectory Data Warehousing, from trajectory reconstruction and ETL processing to Visual OLAP analysis on mobility data.

HERMES: A Trajectory DB Engine for Mobility-Centric Applications

This paper presents HERMES, a prototype DB engine that defines a powerful query language for trajectory databases, which enables the support of mobility-centric applications, such as Location-Based Services LBS. HERMES extends the data definition and manipulation language of Object-Relational DBMS ORDBMS with spatio-temporal semantics and functionality based on advanced spatio-temporal indexing and query processing techniques. Its implementation over two ORDBMS and its utilization in various domains proves the expressive power and applicability of HERMES in different application domains where knowledge regarding mobility data is essential. As a proof-of-concept, in this paper HERMES is applied to a case study related with vehicle traffic analysis, demonstrating its flexibility and usefulness for delivering custom-defined LBS.

On the effect of trajectory compression in spatiotemporal querying

Existing work repeatedly addresses that the ubiquitous positioning devices will start to generate an unprecedented stream of time-stamped positions leading to storage and computation challenges. Hence the need for trajectory compression arises. The goal of this paper is to estimate the effect of compression in spatiotemporal querying; towards this goal, we present an analysis of this effect and provide a model to estimate it in terms of average false hits per query. Then, we propose a method to deal with the models calculation, by incorporating it in the execution of the compression algorithm. Our experimental study shows that this proposal introduces a small overhead in the execution of trajectory compression algorithms, and also verifies the results of the analysis, confirming that our model can be used to provide a good estimation of the effect of trajectory compression in spatiotemporal querying.

Towards a taxonomy of location based services

Location-based services (LBS) constitute an emerging application domain involving spatio-temporal databases. In this paper, i) we propose a classification of LBS, depending on whether the user (query object) and the data objects are moving or not and ii) we provide algorithms for the efficient support of real applications, for every class. We also survey recent work in query processing for the proposed LBS algorithms and sketch open issues for future research.