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We live in an age of rapid technological development. The Internet already affects our lives in many ways. Indeed, we continue to depend more, and more intrinsically, on the Internet, which is increasingly becoming a fundamental piece of societal infrastructure, just as water supply, electricity grids, and transportation networks have been for a long time. But while these other infrastructures are relatively static, the Internet is undergoing swift and fundamental change: Notably, the Internet is going mobile. The world has some 6.7 billion humans, 4 billion mobile phones, and 1.7 billion Internet users. The two most populous continents, Asia and Africa, have relatively low Internet penetration and hold the greatest potentials for growth. Their mobile phone users by far outnumber their Internet users, and the numbers are growing rapidly. China and India are each gaining about half a dozen million new phone users per month. Users across the globe as a whole increasingly embrace mobile Internet devices, with smart phone sales are starting to outnumber PC sales. Indeed, these and other facts suggest that the Internet stands to gain a substantial mobile component. This mega trend towards “mobile” is enabled by rapid and continuing advances in key technology areas such as mobile communication, consumer electronics, g- positioning, and computing. In short, this is the backdrop for this very timely book on moving objects by Xiaofeng Meng and Jidong Chen.



Moving Objects Management Models


Chapter 1. Introduction

Advances in computer and telecommunication technologies have made mobile computing a reality. In a mobile computing environment, users can access information through wireless connections regardless of their physical location. In the last decade, this new kind of computing paradigm has gained great development and posed new challenges to databases. Mobile data management has attracted considerable attention. Moving objects databases that include the management of location information, has become an enabling technology for many location-based services applications. In this chapter, we introduce some background of moving objects management, including mobile computing and positioning technology, and then describe some applications in location-based services and mobile data management. Finally, we present the main content of moving objects databases technologies.
Xiaofeng Meng, Jidong Chen

Chapter 2. Moving Objects Modeling

Location modeling is the foundation for moving objects databases. Existing database management systems are not well equipped to handle continuously changing data, such as the position of moving objects. The reason for this is that in traditional databases, data is assumed to be constant unless it is explicitly modified. This is unsatisfactory for MOD since locations of moving objects are continuously changing. In this chapter, we introduce a few underlying location modeling methods and propose a new graph of cellular automata (GCA) model to integrate the traffic movement features into the model of moving objects and the underlying spatial network.
Jidong Chen, Xiaofeng Meng

Chapter 3. Moving Objects Updating

In moving objects applications, large numbers of locations can be sampled by sensors or GPS periodically, then sent from moving clients to the server and stored in a database. Therefore, continuously maintaining in a database the current locations of moving objects by using a tracking technique becomes very important. The key issue is minimizing the number of updates, while providing precise locations for query results. In this chapter, we will introduce some underlying location update methods. Then, we describe two location update strategies in detail, which can improve the performance. One is the proactive location update strategy, which predicts the movement of moving objects to lower the update frequency; the other is the group location update strategy, which groups the objects to minimize the total number of objects reporting their locations.
Jidong Chen, Xiaofeng Meng

Chapter 4. Moving Objects Indexing

For querying large amounts of moving objects, a key problem is to create efficient indexing structures that make it possible to effectively answer various types of queries. Traditional spatial indexing approaches cannot be used because the locations of moving objects are highly dynamic, which leads to frequent updates of index structures, which in turn will cause huge overheads. In this chapter, we first introduce a few of the underlying spatial index structures including the R-tree, Grid File, and Quad-tree. Then, we propose the indexing methods for moving objects in Euclidean space and in spatial networks. Finally, we describe techniques that index the past, present, and anticipated future positions of moving objects.
Jidong Chen, Xiaofeng Meng

Moving Objects Management Techniques


Chapter 5. Moving Objects Basic Querying

Once we build the model and index for moving objects, we can answer the queries for moving objects. There are many types of queries in moving objects databases such as the nearest neighbor (NN) query, range query, and density query. In this chapter, we will introduce the basic querying types for moving objects according to spatial predicates, temporal predicates, and moving spaces. Though there are many techniques to support moving objects queries, most of the existing studies consider Euclidean spaces, where the distance between two objects is determined solely by their relative position in space. However, in practice, objects can usually move only on a pre-defined set of trajectories as specified by the underlying network. Hence we will introduce how to answer range queries and NN queries for moving objects in a spatial network, which is based on the work of Papadias in [11].
Xiaofeng Meng, Jidong Chen

Chapter 6. Moving Objects Advanced Querying

So far, we have introduced the basic querying for moving objects. There are still some advanced querying for moving objects. It is more difficult to deal with these queries. In this chapter, we introduce a few advanced queries, especially similar trajectory queries and density queries for moving objects. The goal of similar trajectory queries is to find the moving patterns in the trajectories of moving objects, while density queries are to efficiently find dense areas with high concentration of moving objects. We will discuss how to process both the snapshot and continuous density queries in this chapter.
Jidong Chen, Xiaofeng Meng

Chapter 7. Trajectory Prediction of Moving Objects

The trajectory prediction is an important part for the management of moving objects. For example, it can be used to improve the performance of the location update strategy and to support the predictive index and queries. In this chapter, we first review some linear prediction methods and analyze their problem in handling moving objects in spatial networks, and then present our simulation-based prediction methods: Fast-Slow Bounds Prediction and Time-Segment Prediction.
Jidong Chen, Xiaofeng Meng

Chapter 8. Uncertainty of Moving Objects

One of the key research issues with moving objects databases is the uncertainty management. The uncertainty management for moving objects has been well studied recently, with many models and algorithms proposed. In this chapter, we analyze the uncertainty of moving objects in spatial networks and introduce an uncertain trajectory model and an index framework, the uncertain trajectory based Rtree (UTR-tree), for indexing the fully uncertain trajectories of network-constrained moving objects. Then, we introduce how to process queries on this framework. The content of this chapter is mainly from the work of Ding in [14].
Xiaofeng Meng, Jidong Chen

Moving Objects Management Applications


Chapter 9. Dynamic Transportation Navigation

Miniaturization of computing devices, and advances in wireless communication and sensor technology are some of the forces that are propagating computing from the stationary desktop to the mobile outdoors. Some important classes of new applications that will be enabled by this revolutionary development include intelligent traffic management, location-based services, tourist services, mobile electronic commerce, and digital battlefield. Some existing application classes that will benefit from the development include transportation and air traffic control, weather forecasting, emergency response, mobile resource management, and mobile workforce. Location management, i.e., the management of transient location information, is an enabling technology for all these applications. In this chapter, we present the applications of moving objects management and their functionalities, in particular, the application of dynamic traffic navigation, which is a challenge due to the highly variable traffic state and the requirement of fast, on-line computations.
Xiaofeng Meng, Jidong Chen

Chapter 10. Dynamic Transportation Networks

In this chapter, another application, a new moving objects database system, moving objects on dynamic transportation networks (MODTN), is proposed. In the MODTN system, moving objects are modeled as moving graph points that move only within predefined transportation networks. To express general events of the system, such as traffic jams, temporary constructions, insertion and deletion of junctions or routes, the underlying transportation networks are modeled as dynamic graphs so that the state and the topology of the graph system at any time instant can be tracked and queried. Besides, to track the location of network constrained moving objects, a location update mechanism is provided, and the corresponding uncertainty management issues are analyzed. The content of this chapter is mainly from the work of Ding in [1].
Xiaofeng Meng, Jidong Chen

Chapter 11. Clustering Analysis of Moving Objects

In many moving objects management applications, realtime data analysis such as clustering analysis is becoming one of the most important requirements. Most spatial clustering algorithms deal with objects in Euclidean space. In many real-life applications, however, the accessibility of spatial objects is constrained by spatial networks (e.g., road networks). It is therefore more realistic to work on clustering objects in a road network. The distance metric in such a setting is redefined by the network distance, which has to be computed by the expensive shortest path distance over the network. The existing methods are not applicable to such cases. Therefore, we use the information of nodes and edges in the network to present two new static clustering algorithms that prune the search space and avoid unnecessary distance computations. In addition, we present the problem of clustering moving objects in spatial networks and propose a unified framework to address it. The goals are to optimize the cost of clustering moving objects and support multiple types of clusters in a single application.
Jidong Chen, Xiaofeng Meng

Chapter 12. Location Privacy

With rapid development of sensor and wireless mobile devices, it is easy to access mobile users’ location information anytime and anywhere. On one hand, LBS is becoming more and more valuable and important. On the other hand, location privacy issues raised by such applications have also gained more attention. However, due to the specificity of location information, traditional privacy-preserving techniques in data publishing cannot be used. In this chapter, we will introduce location privacy, and analyze the challenges of location privacy-preserving, and give a survey of existing work including the system architecture, location anonymity and query processing.
Xiaofeng Meng, Jidong Chen


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