Moving Object Databases Research Articles

An efficient hybridized index technique for moving object database

Indexes are needed in order to index a number of moving object’s positions to provide answers to different types of queries as fast as possible. The most popular types of querying techniques in moving object databases are K-Nearest Neighbor and Rang queries. In KNN, a set of k points of interest that can be reached in a minimum response time are retrieved. For Range Query, all objects whose positions fall within a predefined rectangular or circular range are retrieved. Creating an efficient index for objects’ locations is looked upon as the most critical problem in connection with spatial–temporal data management. Indexes are different based on their structures, query processing, and update performance. In this context, this paper aims to hybridize both tree and grid based structures to enhance update, search, and insert in the index. To achieve this goal, the current paper will discuss the design of the proposed index.

A data model for moving regions of fixed shape in databases

ABSTRACTMoving object databases are designed to store and process spatial and temporal object data. An especially useful moving object type is a moving region, which consists of one or more moving polygons suitable for modeling the spread of forest fires, the movement of clouds, spread of diseases and many other real-world phenomena. Previous implementations usually allow a changing shape of the region during the movement; however, the necessary restrictions on this model result in an inaccurate interpolation of rotating objects. In this paper, we present an alternative approach for moving and rotating regions of fixed shape, called Fixed Moving Regions, which provide a significantly better model for a wide range of applications like modeling the movement of oil tankers, icebergs and other rigid structures. Furthermore, we describe and implement several useful operations on this new object type to enable a database system to solve many real-world problems, as for example collision tests, projections and intersections, much more accurate than with other models. Based on this research, we also implemented a library for easy integration into moving objects database systems, as for example the DBMS Secondo (1) (2) developed at the FernUniversität in Hagen.

Short-Range Prediction of the Zone of Moving Vehicles in Arterial Networks

In many moving object databases, future locations of vehicles in arterial networks are predicted. While most of studies apply the frequent behavior of historical trajectories or vehicles’ recent kinematics as the basis of predictions, consideration of the dynamics of the intersections is mostly neglected. Signalized intersections make vehicles experience different delays, which vary from zero to some minutes based on the traffic state at intersections. In the absence of traffic signal information (red and green times of traffic signal phases, the queue lengths, approaching traffic volume, turning volumes to each intersection leg, etc.), the experienced delays in traffic signals are random variables. In this paper, we model the probability distribution function (PDF) and cumulative distribution function (CDF) of the delay for any point in the arterial networks based on a spatiotemporal model of the queue at the intersection. The probability of the presence of a vehicle in a zone is determined based on the modeled probability function of the delay. A comparison between the results of the proposed method and a well-known kinematic-based method indicates a significant improvement in the precisions of the predictions.

A spatiotemporal algebra in Hadoop for moving objects

Spatiotemporal data represent the real-world objects that move in geographic space over time. The enormous numbers of mobile sensors and location tracking devices continuously produce massive amounts of such data. This leads to the need for scalable spatiotemporal data management systems. Such systems shall be capable of representing spatiotemporal data in persistent storage and in memory. They shall also provide a range of query processing operators that may scale out in a cloud setting. Currently, very few researches have been conducted to meet this requirement. This paper proposes a Hadoop extension with a spatiotemporal algebra. The algebra consists of moving object types added as Hadoop native types, and operators on top of them. The Hadoop file system has been extended to support parameter passing for files that contain spatiotemporal data, and for operators that can be unary or binary. Both the types and operators are accessible for the MapReduce jobs. Such an extension allows users to write Hadoop programs that can perform spatiotemporal analysis. Certain queries may call more than one operator for different jobs and keep these operators running in parallel. This paper describes the design and implementation of this algebra, and evaluates it using a benchmark that is specific to moving object databases.

MoZo: A Moving Zone Based Routing Protocol Using Pure V2V Communication in VANETs

Vehicular Ad-hoc Networks (VANETs) are an emerging field, whereby vehicle-to-vehicle communications can enable many new applications such as safety and entertainment services. Most VANET applications are enabled by different routing protocols. The design of such routing protocols, however, is quite challenging due to the dynamic nature of nodes (vehicles) in VANETs. To exploit the unique characteristics of VANET nodes, we design a moving-zone based architecture in which vehicles collaborate with one another to form dynamic moving zones so as to facilitate information dissemination. We propose a novel approach that introduces moving object modeling and indexing techniques from the theory of large moving object databases into the design of VANET routing protocols. The results of extensive simulation studies carried out on real road maps demonstrate the superiority of our approach compared with both clustering and non-clustering based routing protocols.

On temporal-constrained sub-trajectory cluster analysis

Cluster analysis over Moving Object Databases (MODs) is a challenging research topic that has attracted the attention of the mobility data mining community. In this paper, we study the temporal-constrained sub-trajectory cluster analysis problem, where the aim is to discover clusters of sub-trajectories given an ad-hoc, user-specified temporal constraint within the dataset's lifetime. The problem is challenging because: (a) the time window is not known in advance, instead it is specified at query time, and (b) the MOD is continuously updated with new trajectories. Existing solutions first filter the trajectory database according to the temporal constraint, and then apply a clustering algorithm from scratch on the filtered data. However, this approach is extremely inefficient, when considering explorative data analysis where multiple clustering tasks need to be performed over different temporal subsets of the database, while the database is updated with new trajectories. To address this problem, we propose an incremental and scalable solution to the problem, which is built upon a novel indexing structure, called Representative Trajectory Tree (ReTraTree). ReTraTree acts as an effective spatio-temporal partitioning technique; partitions in ReTraTree correspond to groupings of sub-trajectories, which are incrementally maintained and assigned to representative (sub-)trajectories. Due to the proposed organization of sub-trajectories, the problem under study can be efficiently solved as simply as executing a query operator on ReTraTree, while insertion of new trajectories is supported. Our extensive experimental study performed on real and synthetic datasets shows that our approach outperforms a state-of-the-art in-DBMS solution supported by PostgreSQL by orders of magnitude.

GSMNet: A Hierarchical Graph Model for Moving Objects in Networks

Existing data models for moving objects in networks are often limited by flexibly controlling the granularity of representing networks and the cost of location updates and do not encompass semantic information, such as traffic states, traffic restrictions and social relationships. In this paper, we aim to fill the gap of traditional network-constrained models and propose a hierarchical graph model called the Geo-Social-Moving model for moving objects in Networks (GSMNet) that adopts four graph structures, RouteGraph, SegmentGraph, ObjectGraph and MoveGraph, to represent the underlying networks, trajectories and semantic information in an integrated manner. The bulk of user-defined data types and corresponding operators is proposed to handle moving objects and answer a new class of queries supporting three kinds of conditions: spatial, temporal and semantic information. Then, we develop a prototype system with the native graph database system Neo4Jto implement the proposed GSMNet model. In the experiment, we conduct the performance evaluation using simulated trajectories generated from the BerlinMOD (Berlin Moving Objects Database) benchmark and compare with the mature MOD system Secondo. The results of 17 benchmark queries demonstrate that our proposed GSMNet model has strong potential to reduce time-consuming table join operations an d shows remarkable advantages with regard to representing semantic information and controlling the cost of location updates.

Object-to-group probabilistic distance measure for uncertain data classification

Uncertain objects, where each feature is represented by multiple observations or a given or fitted probability density function, arise in applications such as sensor networks, moving object databases and medical and biological databases. We propose a methodology to classify uncertain objects based on a new probabilistic distance measure between an uncertain object and a group of uncertain objects. This object-to-group probabilistic distance measure is unique in that it accounts separately for the correlations among the features within each class and within each object. We compare the proposed object-to-group classifier to two existing classifiers, namely, the K-Nearest Neighbor classifier on object means (certain-KNN) and the uncertain-naïve Bayes classifier. In addition, we compare the object-to-group classifier to an uncertain K-Nearest Neighbor classifier (uncertain-KNN), also proposed here, that uses existing probabilistic distance measures for object-to-object distances. We illustrate the advantages of the proposed classifiers with both simulated and real data.

On querying and mining semantic-aware mobility timelines

The explosion of available positioning information associated with the inferred or user-declared semantics of the respective locations has contributed in what is called the Big Data era by posing new challenges to the mobility data management and mining research community. Motivated by a series of challenges posed in Pelekis et al. (SIGKDD Explor 15(1):23–32, 2013), in this paper, we present a unified framework for the management and the analysis of LifeSteps, i.e., data objects about human mobility including both (raw) spatio-temporal trajectories and their semantic counterpart. In particular, we provide solutions for developing real-world semantic-aware moving object database and trajectory data warehouse systems and we devise respective query processing algorithms. Our experimental study on realistic synthetic data including synchronized raw (i.e., GPS log) and semantic (i.e., diaries) information verifies the effectiveness and efficiency of the proposed framework.

Simulating Our LifeSteps by Example

During the past few decades, a number of effective methods for indexing, query processing, and knowledge discovery in moving object databases have been proposed. An interesting research direction that has recently emerged handles semantics of movement instead of raw spatio-temporal data. Semantic annotations, such as “stop,” “move,” “at home,” “shopping,” “driving,” and so on, are either declared by the users (e.g., through social network apps) or automatically inferred by some annotation method and are typically presented as textual counterparts along with spatial and temporal information of raw trajectories. It is natural to argue that such “spatio-temporal-textual” sequences, called semantic trajectories, form a realistic representation model of the complex everyday life (hence, mobility) of individuals. Towards handling semantic trajectories of moving objects in Semantic Mobility Databases, the lack of real datasets leads to the need to design realistic simulators. In the context of the above discussion, the goal of this work is to realistically simulate the mobility life of a large-scale population of moving objects in an urban environment. Two simulator variations are presented: the core Hermoupolis simulator is parametric driven (i.e., user-defined parameters tune every movement aspect), whereas the expansion of the former, called Hermoupolis by-example , follows the generate-by-example paradigm and is self-tuned by looking inside a real small (sample) dataset. We stress test our proposal and demonstrate its novel characteristics with respect to related work.

Modeling and Querying Moving Objects with Social Relationships

Current moving-object database (MOD) systems focus on management of movement data, but pay less attention to modelling social relationships between moving objects and spatial-temporal trajectories in an integrated manner. This paper combines moving-object database and social network systems and presents a novel data model called Geo-Social-Moving (GSM) that enables the unified management of trajectories, underlying geographical space and social relationships for mass moving objects. A bulk of user-defined data types and corresponding operators are also proposed to facilitate geo-social queries on moving objects. An implementation framework for the GSM model is proposed, and a prototype system based on native Neo4J is then developed with two real-world data sets from the location-based social network systems. Compared with solutions based on traditional extended relational database management systems characterized by time-consuming table join operations, the proposed GSM model characterized by graph traversal is argued to be more powerful in representing mass moving objects with social relationships, and more efficient and stable for geo-social querying.

Enabling Smart Transportation Systems: A Parallel Spatio-Temporal Database Approach

We are witnessing increasing interests in developing “smart cities” which helps improve the efficiency, reliability, and security of a traditional city. An important aspect of developing smart cities is to enable “smart transportation,” which improves the efficiency, safety, and environmental sustainability of city transportation means. Meanwhile, the increasing use of GPS devices has led to the emergence of big trajectory data that consists of large amounts of historical trajectories and real-time GPS data streams that reflect how the transportation networks are used or being used by moving objects, e.g., vehicles, cyclists, and pedestrians. Such big trajectory data provides a solid data foundation for developing various smart transportation applications, such as congestion avoidance, reducing greenhouse gas emissions, and effective traffic accident response, etc. Instead of proposing yet another specific smart transportation application, we propose the parallel-distributed network-constrained moving objects database (PD-NMOD), a general framework that manages big trajectory data in a scalable manner, which provides an infrastructure that is able to support a wide variety of smart transportation applications and thus benefiting the smart city vision as a whole. The PD-NMOD manages both transportation networks and trajectories in a distributed manner. In addition, the PD-NMOD is designed to support general SQL queries over moving objects and to efficiently process the SQL queries on big trajectory data in parallel. Such design facilitates smart transportation applications to retrieve relevant trajectory data and to conduct statistical analyses. Empirical studies on a large trajectory data set collected from 3,500 taxis in Beijing offer insight into the design properties of the PD-NMOD and offer evidence that the PD-NMOD is efficient and scalable.

Linking movement and environmental data: The need for representation

In several domains, there has been an increasing interest in analysing moving objects due to the recent ubiquity of location tracking devices. These locations are commonly abstracted and stored as trajectories: a finite set of ordered in time geometries. Tracked objects tend to move in a certain environment that influences their movement behaviour. Time dependent environmental data are commonly abstracted and stored in grid/array structures that have different granularities and characteristics compare to the trajectory datasets. Movement analysis requires linking these two data types. However, little consideration has been given to the issues of integration in the moving objects databases that is primarily dealing with trajectory storage and analysis as well as in array databases dealing mainly with the storage and retrieval of grid structures. In this paper, we propose a database model that utilizes abstract data types for combining trajectories and time dependent environmental data. We introduce a set of spatio-temporal operations for interacting with raster data and integrating them with the moving geometries. We demonstrate how the integration operations can be used for manipulation and analysis of moving objects, using trajectories of tropical cyclone and environmental data, using sea surface temperature (SST) for the period from 1980 to 2009 as a case study. Since tropical cyclones generally gain strength over the warmer seas, the proposed operations are used to answer questions about genesis and movement patterns of tropical cyclones, in relation to the changing patterns of the SST.

Robust high-quality interpolation of regions to moving regions

With the rise of moving object databases it is possible to store and process spatial and temporal data, for example geometrical structures together with the information about how these behave over intervals of time. For simple objects like moving points the spatiotemporal development is derived from the start and end position in space and time, which is then linearly interpolated. For moving regions, especially with changing shapes, it is more challenging to obtain the necessary data to represent them. An elegant and intuitive solution is to create an algorithm, which automatically interpolates the moving region from the start and end shape over a specified time interval. Two papers on this topic have been published in the past, each focussing on different aspects of this so-called Region Interpolation Problem. This paper tries to combine the advantages and improve these approaches to provide high-quality interpolations while maintaining robustness even in border cases. This results in the implementation of a library, which can be easily integrated into existing moving objects database systems, as for example the DBMS Secondo developed at the FernUniversitat in Hagen.

Moving Object Databases — Dynamic Flight Route Modification According to Weather Related Incidents

A moving entity requires huge amount of data, thus its database should handle these data (index mechanisms, secondary storage needs, its management of data for multiusers, and so on). To give an object's correct location, database need to be updated frequently. This means capacity gets overflow eventually. So, new data types need to be used to make the system stable. The solution is to unite spatial and temporal data. It's called spatio-temporal data. Also, this type of database is called moving objects databases (MODS). With these type of data it is possible to show plane locations and dynamically change air vehicle's (UAV, plane, military vehicle and so on) route according to weather constantly. In this study Airborne Warning and Control System (AWACS) movement is modeled, this air vehicle's data is queried, and dynamically its route calculated according to weather. Therefore, a safer route can be calculated and planned in today's technologic capabilities.

Network-Matched Trajectory-Based Moving-Object Database: Models and Applications

Tracking and managing the locations of moving objects are essential in modern intelligent transportation systems (ITSs). However, a number of limitations in existing methods make them unsuitable for real-world ITS applications. In particular, Euclidean-based methods are not accurate enough in representing locations and in analyzing traffic, unless the locations are frequently updated. Network-based methods require either digital maps to be installed in moving objects or transmission of prediction policies, which inevitably increase the cost. To solve these problems, we propose a network-matched trajectory-based moving-object database (NMTMOD) mechanism and a traffic flow analysis method using the NMTMOD. In the NMTMOD, the locations of moving objects are tracked through a dense sampling and batch uploading strategy, and a novel edge-centric network-matching method, which is running at the server side, is adopted to efficiently match the densely sampled GPS points to the network. In addition, a deviation-based trajectory optimization method is provided to minimize the trajectory size. Empirical studies with large real trajectory data set offer insight into the design properties of the proposed NMTMOD and suggest that the NMTMOD significantly outperforms other mobile-map free-moving-object database models in terms of precision of both location tracking and network-based traffic flow analysis.

Exploring Dance Movement Data Using Sequence Alignment Methods.

Despite the abundance of research on knowledge discovery from moving object databases, only a limited number of studies have examined the interaction between moving point objects in space over time. This paper describes a novel approach for measuring similarity in the interaction between moving objects. The proposed approach consists of three steps. First, we transform movement data into sequences of successive qualitative relations based on the Qualitative Trajectory Calculus (QTC). Second, sequence alignment methods are applied to measure the similarity between movement sequences. Finally, movement sequences are grouped based on similarity by means of an agglomerative hierarchical clustering method. The applicability of this approach is tested using movement data from samba and tango dancers.

A Self-Adaptive Parameter Selection Trajectory Prediction Approach via Hidden Markov Models

Trajectory prediction of objects in moving objects databases (MODs) has garnered wide support in a variety of applications and is gradually becoming an active research area. The existing trajectory prediction algorithms focus on discovering frequent moving patterns or simulating the mobility of objects via mathematical models. While these models are useful in certain applications, they fall short in describing the position and behavior of moving objects in a network-constraint environment. Aiming to solve this problem, a hidden Markov model (HMM)-based trajectory prediction algorithm is proposed, called Hidden Markov model-based Trajectory Prediction (HMTP). By analyzing the disadvantages of HMTP, a self-adaptive parameter selection algorithm called HMTP $\ast$ is proposed, which captures the parameters necessary for real-world scenarios in terms of objects with dynamically changing speed. In addition, a density-based trajectory partition algorithm is introduced, which helps improve the efficiency of prediction. In order to evaluate the effectiveness and efficiency of the proposed algorithms, extensive experiments were conducted, and the experimental results demonstrate that the effect of critical parameters on the prediction accuracy in the proposed paradigm, with regard to HMTP $\ast$ , can greatly improve the accuracy when compared with HMTP, when subjected to randomly changing speeds. Moreover, it has higher positioning precision than HMTP due to its capability of self-adjustment.

GMOBench: Benchmarking generic moving objects

In real world scenarios, people's movement include several environments rather than one, for example, road network, pavement areas and indoor. This imposes a new challenge for moving objects database that the complete trip needs to be managed by a database system. In the meantime, novel queries regarding different transportation modes should also be supported. Since existing methods are limited to trips in a single environment and do not support queries on moving objects with different transportation modes, new technologies are essentially needed in a database system. In this paper, we introduce a benchmark called GMOBench that aims to evaluate the performance of a database system managing moving objects in different environments. GMOBench is settled in a realistic scenario and is comprised of three components: (1) a data generator with the capability of creating a scalable set of trips representing the complete movement of humans (both indoor and outdoor); (2) a set of carefully designed and benchmark queries; (3) Mode-RTree, an index structure for managing generic moving objects. The generator defines some parameters so that users can control the characteristics of results. We create the benchmark data in such a way that the dataset can mirror important characteristics and real world distributions of human mobility. Efficient access methods and optimization techniques are developed for query processing. In particular, we propose an index structure called Mode-RTree to manage moving objects in different environments. By employing the proposed index, the cost of benchmark queries is greatly reduced. GMOBench is implemented in a real database system to have a practical result. We perform an extensive experimental study on comprehensive datasets to evaluate the performance. The results show that by using the Mode-RTree we achieve significant performance improvement over the baseline method, demonstrating the effectiveness and efficiency of our approaches.

Performance Analysis on Uncertain Data using Decision Tree

Data uncertainty is common in emerging applications, such as sensor networks, moving object databases, medical and biological fields. Data uncertainty can be caused by various factors including measurements precision limitation. Data uncertainty is inherited in various applications due to different reasons such as outdated sources or imprecise measurement and transmission problems. Classification is one of the most popular data mining techniques. Lot of people used decision tree for data classification and it widely used on certain or precise data. However in this paper we applied on uncertain data which is taken from UCI machine learning repository. This paper proposes a decision tree based classification method on uncertain data. We construct decision tree algorithms by including entropy and information gain, considering the uncertain data intervals. We use some pruning techniques that can improve efficiency of the decision tree and our experiment show that it significantly reduce the treeconstruction time.