Moving Object Databases Research Articles

On Computing the Time-varying Distance between Moving Bodies

A moving body is a geometry that may translate and rotate over time. Computing the time-varying distance between moving bodies and surrounding static and moving objects is crucial to many application domains including safety at sea, logistics robots, and autonomous vehicles. Not only is it a relevant analytical operation in itself, but also it forms the basis of other operations, such as finding the nearest approach distance between two moving objects. Most moving objects databases represent moving objects using a point representation, and the computed temporal distance is thus inaccurate when working with large moving objects. This article presents an efficient algorithm to compute the temporal distance between a moving body and other static or moving geometries. We extend the idea of the V-Clip and Lin-Canney closest features algorithms of computational geometry to track the temporal evolution of the closest pair of features between two objects during their movement. We also present a working implementation of this algorithm in an open-source moving objects database and show, using a real-world example on AIS data, that this distance operator for moving bodies is only about 1.5 times as slow as the one for moving points while providing significant improvements in correctness and accuracy of the results.

Approximating the evolution of rotating moving regions using Bezier curves

The region interpolation methods proposed in the moving objects databases literature impose restrictions that can have a significant impact on the representation of the evolution of moving regions, in particular, when a rotation occurs between two observations. In this paper, we propose a data model for moving regions that allows moving segments to rotate and change their length during their evolution between two observations and uses quadratic Bezier curves to define the trajectories of their endpoints. This introduces a new class of moving regions called rotating moving regions (rmregions). We present algorithms for operations involving rmregions and we propose a strategy to allow different interpolation methods to be used in the context of moving objects databases by approximating the interpolations they create using rmregions. We demonstrate our strategy using a reference implementation and compare results obtained when using the strategy presented here and the region interpolation methods and the spatiotemporal operations proposed in the state-of-the-art. Experimental results show that our strategy can be used to complement the region interpolation methods proposed in the moving objects databases literature.

User‐centered road network traffic analysis with MobilityDB

AbstractPerformance indicators of road networks are a long‐lasting topic of research. Existing schemes assess network properties such as the average speed on road segments and the queuing time at intersections. The increasing availability of user trajectories, collected mainly using mobile phones with a variety of applications, creates opportunities for developing user‐centered performance indicators. Performing such an analysis on big trajectory data sets remains a challenge for the existing data management systems, because they lack support for spatiotemporal trajectory data. This article presents an end‐to‐end solution, based on MobilityDB, a novel moving object database system that extends PostgreSQL with spatiotemporal data types and functions. A new class of indicators is proposed, focused on the users' experience. The indicators address the network design, the traffic flow, and the driving comfort of the motorists. Furthermore, these indicators are expressed as analytical MobilityDB queries over a big set of real vehicle trajectories.

Analyzing public transport in the city of Buenos Aires with MobilityDB.

The General Transit Feed Specification (GTFS) is a data format widely used to share data about public transportation schedules and associated geographic information. GTFS comes in two versions: GTFS Static describing the planned itineraries and GTFS Realtime describing the actual ones. MobilityDB is a novel and free open-source moving object database, developed as a PostgreSQL and PostGIS extension, that adds spatial and temporal data types along with a large number of functions, that facilitate the analysis of mobility data. Loading GTFS data into MobilityDB is a quite complex task that, nevertheless, must be done in an ad-hoc fashion. This work describes how MobilityDB is used to analyze public transport mobility in the city of Buenos Aires, using both, static and real-time GTFS data for the Buenos Aires public transportation system. Visualizations are also produced to enhance the analysis. To the authors’ knowledge, this is the first attempt to analyze GTFS data with a moving object database.

CPRQ: Cost Prediction for Range Queries in Moving Object Databases

Predicting query cost plays an important role in moving object databases. Accurate predictions help database administrators effectively schedule workloads and achieve optimal resource allocation strategies. There are some works focusing on query cost prediction, but most of them employ analytical methods to obtain an index-based cost prediction model. The accuracy can be seriously challenged as the workload of the database management system becomes more and more complex. Differing from the previous work, this paper proposes a method called CPRQ (Cost Prediction of Range Query) which is based on machine-learning techniques. The proposed method contains four learning models: the polynomial regression model, the decision tree regression model, the random forest regression model, and the KNN (k-Nearest Neighbor) regression model. Using R-squared and MSE (Mean Squared Error) as measurements, we perform an extensive experimental evaluation. The results demonstrate that CPRQ achieves high accuracy and the random forest regression model obtains the best predictive performance (R-squared is 0.9695 and MSE is 0.154).

Spatiotemporal coverage analysis of moving regions

ABSTRACT Moving objects databases are specialized for processing objects that have attributes which change over time. A moving region is a powerful data type in this context, describing a region which changes shape and position over a specified time interval. This data type is, for example, a suitable model for natural phenomena like hurricanes, clouds or forest fires. Several operations exist to process and analyze moving regions in moving objects databases. One useful operation is to determine which areas are covered for which time span during the movement of moving regions to calculate, for example, which areas are affected by a hurricane for a certain period of time. This paper presents a set of operations that can be used to analyze the spatiotemporal coverage behavior of a moving region to determine which regions are covered for shorter or longer than a given time, or which parts are covered for a specified time interval. Furthermore, the coverage duration of a single point and the average coverage duration for a given region can be determined.

A differentially private location generalization approach to guarantee non-uniform privacy in moving objects databases

Recently there has been much interest in moving objects databases because of their applications in many domains, such as location-based services and traffic management. Moving objects databases store and manage information representing changes in the spatial properties of moving objects over time. Meanwhile, privacy protection has been one of the most important concerns in these databases. In this paper, we study this problem by presenting DPLG, a location generalization approach for moving objects databases that preserves the strong guarantee of differential privacy. Our main goal is to guarantee non-uniform privacy for locations with different privacy protection requirements while being scalable for spatial domains with a large number of locations. For this purpose, we use location generalization in such a way that locations with higher privacy protection requirements are generalized to larger ones. Location generalization also has the advantage that it enables DPLG to reduce the number of locations and, thus, to keep the running time and space requirements as reasonable as possible. We also present two post-processing techniques, namely, consistency constraints enforcement and quality improvement, to have consistent query answers and to reduce query errors caused by location generalization. The quality improvement technique divides the noisy count of each generalized location among the reference locations it contains homogeneously or heterogeneously. Extensive experiments demonstrate that, in addition to keeping reasonable the running time and space requirements, DPLG improves the utility of query answers for locations with lower privacy protection requirements in comparison to those with higher privacy protection requirements while satisfying differential privacy.

An Adaptive Parallel Method for Indexing Transportation Moving Objects

Transportation cyber-physical systems are constrained by spatiality and real-time because of their high level of heterogeneity. Therefore, applications like traffic control generally manage moving objects in a single-machine multithreaded manner, whereas suffering from frequent locking operations. To address this problem and improve the throughput of moving object databases, we propose a GPU-accelerated indexing method, based on a grid data structure, combined with quad-trees. We count object movements and decide whether a particular node should be split or be merged on the GPU. In this case, bottlenecked nodes can be translated to quad-tree without interfering with the CPU. Hence, waiting time of other threads caused by locking operations raised by object data updating can be reduced. The method is simple while more adaptive to scenarios where the distribution of moving objects is skewed. It also avoids shortcomings of existing methods with performance bottleneck on the hot area or spending plenty of calculation resources on structure balancing. Experiments suggest that our method shows higher throughput and lower response time than the existing indexing methods. The advantage is even more significant under the skewed distribution of moving objects.

Globular Gaussian Essential Part in Meager Bayesian Knowledge Structure for Nonlinear Deterioration

Moving Object Databases (MOD), although ubiquitous, still call for methods that will be able to understand, search, analyze, and browse their spatiotemporal content. In this paper, we propose a method for trajectory segmentation and sampling based on the representativeness of the (sub) trajectories in the MOD. In order to find the most representative sub trajectories, the following methodology is proposed. First, a novel global voting algorithm is performed, based on local density and trajectory similarity information. This method is applied for each segment of the trajectory, forming a local trajectory descriptor that represents line segment representativeness. The sequence of this descriptor over a trajectory gives the voting signal of the trajectory, where high values correspond to the most representative parts. Then, a novel segmentation algorithm is applied on this signal that automatically estimates the number of partitions and the partition borders, identifying homogenous partitions concerning their representativeness. Finally, a sampling method over the resulting segments yields the most representative sub trajectories in the MOD. Our experimental results in synthetic and real MOD verify the effectiveness of the proposed scheme, also in comparison with other sampling techniques.

MobilityDB

Despite two decades of research in moving object databases and a few research prototypes that have been proposed, there is not yet a mainstream system targeted for industrial use. In this article, we present MobilityDB, a moving object database that extends the type system of PostgreSQL and PostGIS with abstract data types for representing moving object data. The types are fully integrated into the platform to reuse its powerful data management features. Furthermore, MobilityDB builds on existing operations, indexing, aggregation, and optimization framework. This is all made accessible via the SQL query interface.

IMO

Due to the widespread use of GPS-enabled devices such as smartphones, the research field of moving objects databases has been quite active in the past decade. Human movements are recorded, managed and analyzed for a plethora of applications. In this demo, we introduce a toolbox named IMO to simulate and query infected moving objects. This is primarily motivated by COVID-19 virus pandemic recently. We model the spreading behavior to demonstrate when and where people are infected. The protection policy is simulated such that one can see the isolation and self protection effects such as human movement restriction and the wearing of masks. Optimization techniques are developed to enhance the performance, including data storage, data partition and index structures. This is not a standalone software but a toolbox embedded in SECONDO, an open source and extensible database system. To the best of our knowledge, simulating and querying infected objects are not supported in existing moving objects prototype systems. Demo attendees can conveniently pose their queries and adjust parameters in the interface and the system will visualize the result after only a short delay.

Enhancing spatial and temporal utilities in differentially private moving objects database release

The pervasive use of mobile technologies and GPS-equipped vehicles has resulted in a large number of moving objects databases. Privacy protection is one of the most significant challenges related to moving objects databases because of the legal requirements in many application domains. Over the last few years, several differentially private mechanisms have been proposed for moving objects databases. However, most of them aim to answer statistical queries and do not release a differentially private version of a moving objects database. In this paper, we present DP-MODR, a differentially private (DP) mechanism for synthetic moving objects database release (MODR). DP-MODR tries to efficiently and effectively release synthetic trajectories while preserving spatial and temporal utilities. In this way, the released differentially private moving objects database can be used for different purposes as well, including data analysis tasks. DP-MODR keeps some main spatial and temporal properties of original trajectories and defines a new differentially private tree structure to keep the most probable paths with different lengths and different starting points, which are then iteratively joined to generate synthetic trajectories in a bottom-up way. Also, we present an extension of DP-MODR to support moving objects databases whose locations are time-dependent. Extensive experiments on real moving objects datasets using multiple spatial and temporal evaluation measures show that DP-MODR enhances the utility of query answers and better preserves the main spatial and temporal properties of original trajectories in comparison with recent related work.

How meaningful are similarities in deep trajectory representations?

Finding similar trajectories is an important task in moving object databases. However, classical similarity models face several limitations, including scalability and robustness. Recently, an approach named t2vec proposed transforming trajectories into points in a high dimensional vector space, and this transformation approximately keeps distances between trajectories. t2vec overcomes that scalability limitation: Now it is possible to cluster millions of trajectories. However, the semantics of the learned similarity values – and whether they are meaningful – is an open issue. One can ask: How does the configuration of t2vec affect the similarity values of trajectories? Is the notion of similarity in t2vec similar, different, or even superior to existing models? As for any neural-network-based approach, inspecting the network does not help to answer these questions. So the problem we address in this paper is how to assess the meaningfulness of similarity in deep trajectory representations. Our solution is a methodology based on a set of well-defined, systematic experiments. We compare t2vec to classical models in terms of robustness and their semantics of similarity, using two real-world datasets. We give recommendations which model to use in possible application scenarios and use cases. We conclude that using t2vec in combination with classical models may be the best way to identify similar trajectories. Finally, to foster scientific advancement, we give the public access to all trained t2vec models and experiment scripts. To our knowledge, this is the biggest collection of its kind.

A polyhedra-based model for moving regions in databases

ABSTRACT Moving objects databases store and process objects with a focus on their spatiotemporal behaviour. To achieve this, the model of the data must be suitable to efficiently store and process moving objects. Currently, a unit-based model is widely used, where each moving object is divided into one or more time intervals, during which the object behaves uniformly. This model is also used for a data type called moving regions, which resembles moving and shape changing regions as, for example, forest fires or cloud fields. However, this model struggles to support operations like union, difference or intersection of two moving regions; the resulting objects are unnecessarily bloated and uncomfortable to handle because the resulting number of units is generally very high. In this paper, an alternative model for moving regions is proposed, which is based on polyhedra. Furthermore, this work develops an isomorphism between moving regions and polyhedra including all relevant operations, which has the additional advantage that several implementations for those are already readily available; this is demonstrated by a reference implementation using the existing and well-tested Computational Geometry Algorithms Library (CGAL).

A Survey on Trajectory Data Warehouse

Advanced technologies in location acquisition allow us to track the movement of moving objects (people, planes, vehicles, animals, ships, ...) in geographical space. These technologies generate a vast amount of trajectory data (TD). Several applications in different fields can utilize such TD, for example, traffic management control, social behavior analysis, wildlife migrations and movements, ship trajectories, shoppers behavior in a mall, facial nerve trajectory, location-based services and many others. Trajectory data can be mainly handled either with Moving Object Databases (MOD) or Trajectory Data Warehouse (TDW). In this paper, we aim to review existing studies on storing, managing, and analyzing TD using data warehouse technologies. We propose a framework that aims to provide the requirements for building the TDW. Furthermore, we discuss different applications using the TDW and how these applications utilize the TDW. We address some issues with existing TDWs and discuss future work in this field.

Mobility Data Warehouses

The interest in mobility data analysis has grown dramatically with the wide availability of devices that track the position of moving objects. Mobility analysis can be applied, for example, to analyze traffic flows. To support mobility analysis, trajectory data warehousing techniques can be used. Trajectory data warehouses typically include, as measures, segments of trajectories, linked to spatial and non-spatial contextual dimensions. This paper goes beyond this concept, by including, as measures, the trajectories of moving objects at any point in time. In this way, online analytical processing (OLAP) queries, typically including aggregation, can be combined with moving object queries, to express queries like “List the total number of trucks running at less than 2 km from each other more than 50% of its route in the province of Antwerp” in a concise and elegant way. Existing proposals for trajectory data warehouses do not support queries like this, since they are based on either the segmentation of the trajectories, or a pre-aggregation of measures. The solution presented here is implemented using MobilityDB, a moving object database that extends the PostgresSQL database with temporal data types, allowing seamless integration with relational spatial and non-spatial data. This integration leads to the concept of mobility data warehouses. This paper discusses modeling and querying mobility data warehouses, providing a comprehensive collection of queries implemented using PostgresSQL and PostGIS as database backend, extended with the libraries provided by MobilityDB.

Measuring the Geometric and Semantic Similarity of Space–Time Prisms Using Temporal Signatures

Well-established techniques exist for measuring the similarity of space–time paths. These measures support clustering and aggregation of space–time paths as well as moving objects database queries based on similar movement patterns or semantics. Little attention has been paid, however, to the analogous problem of measuring space–time prism (STP) similarity, despite comparable applications. This article presents and evaluates a method for measuring STP similarity through dimensionality reduction that leverages their inherent temporal ordering. The technique sweeps an STP along the time axis and derives one-dimensional temporal signatures based on a measured STP property that captures its geometry or semantics. These temporal signatures can be visualized directly as curves. We can also apply existing space–time path similarity measures to these signatures. To demonstrate the feasibility of this approach, we perform two sets of experiments measuring geometric and semantic similarity among STPs and assess the information within these curves using visualization, Fréchet distances, and clustering techniques. Results suggest that the temporal signature curves capture meaningful similarities and differences among STPs.

HadoopTrajectory: a Hadoop spatiotemporal data processing extension

The recent advances in location tracking technologies and the widespread use of location-aware applications have resulted in big datasets of moving object trajectories. While there exists a couple of research prototypes for moving object databases, there is a lack of systems that can process big spatiotemporal data. This work proposes HadoopTrajectory, a Hadoop extension for spatiotemporal data processing. The extension adds spatiotemporal types and operators to the Hadoop core. These types and operators can be directly used in MapReduce programs, which gives the Hadoop user the possibility to write spatiotemporal data analytics programs. The storage layer of Hadoop, the HDFS, is extended by types to represent trajectory data and their corresponding input and output functions. It is also extended by file splitters and record readers. This enables Hadoop to read big files of moving object trajectories such as vehicle GPS tracks and split them over worker nodes for distributed processing. The storage layer is also extended by spatiotemporal indexes that help filtering the data before splitting it over the worker nodes. Several data access functions are provided so that the MapReduce layer can deal with this data. The MapReduce layer is extended with trajectory processing operators, to compute for instance the length of a trajectory in meters. This paper describes the extension and evaluates it using a synthetic dataset and a real dataset. Comparisons with non-Hadoop systems and with standard Hadoop are given. The extension accounts for about 11,601 lines of Java code.

PDP-SAG: Personalized Privacy Protection in Moving Objects Databases by Combining Differential Privacy and Sensitive Attribute Generalization

Moving objects databases have become an enabling technology for location-based applications. They mostly focus on the storing and processing of data about moving objects. Privacy protection is one of the most important concerns related to such databases. In recent years, some mechanisms have been proposed to answer statistical queries over moving objects databases, while satisfying differential privacy. However, none of them consider the case where a moving objects database contains non-spatiotemporal sensitive attributes other than spatiotemporal attributes. Besides, most of them do not support the personalized privacy protection requirements of different moving objects. In this paper, we address these problems by presenting PDP-SAG, a differentially private mechanism that combines the sensitive attribute generalization with personalized privacy in a unified manner. By this combination, we aim to provide different levels of differential privacy protection for moving objects that have non-spatiotemporal sensitive attributes as well. In this regard, we generalize the sensitive attribute values of trajectory data records based on their privacy descriptor and define a new personalized differentially private tree structure to keep different noisy frequencies for each trajectory according to the generalized sensitive attribute values of trajectory data records passing through that trajectory. We also propose intra- and inter-consistency constraints enforcements to make noisy frequencies consistent with each other. The extensive experiments on synthetic and real datasets verify that PDP-SAG significantly improves the utility of sensitive query answers and provides the required level of privacy protection for each moving object, in comparison to the case when no personalization and generalization are permitted.

Predicting Long-Term Trajectories of Connected Vehicles via the Prefix-Projection Technique

The vehicle location prediction based on their spatial and temporal information is an important and difficult task in many applications. In the last few years, devices, such as connected vehicles, smart phones, GPS navigation systems, and smart home appliances, have amassed the large stores of geographic data. The task of leveraging this data by employing moving objects database techniques to predict spatio-temporal locations in an accurate and efficient fashion, comprising a complete trajectory remains an actively researched area. Existing methods for frequent sequential pattern mining tend to be limited to predicting short-term partial trajectories, at extremely high computational costs. In order to address these limitations, we designed a prefix-projection-based trajectory prediction algorithm called PrefixTP, which contains three essential phases. First, data collection, connected vehicles equipped with sensors comprise a vehicle grid and generate copious amounts of spatio-temporal data, in order to communicate and share traffic information. Second, model training, examining only the prefix subsequences, and projecting only their corresponding postfix subsequences into projected sets. Finally, trajectory matching, recursively finding postfix sequences meeting the requirement of minimum support count, and outputting the most frequent sequential pattern as the most probable trajectory. Fundamentally, PrefixTP supports three trajectory matching strategies which encompass all possibilities of prediction. Extensive experiments were conducted using real world GPS data sets, and the results show, when comparing predicted complete trajectories against partial short-term trajectories with a guarantee of real-time forecasting, that PrefixTP outperforms first-order, second-order Markov models, and Apriori-based trajectory prediction algorithm.