This dissertation challenges an unstudied area in moving objects database domains; predicting (long-term) future locations of moving objects. Moving object prediction enables us to provide a wide range of applications, such as traffic prediction, pre-detection of an aircraft collision, and reporting attractive gas prices for drivers along their routes ahead. Nevertheless, existing location prediction techniques are limited to support such applications since they are generally capable only of short-term predictions. In the real world, many objects exhibit typical movement patterns. This pattern information is able to serve as an important background to tackle the limitations of the existing prediction methods. We aims at offering foundations of pattern-aware prediction for moving objects, rendering more precise prediction results. Specifically, this thesis focuses on three parts. The first part of the thesis studies the problem of predicting future locations of moving objects in Euclidean space. We introduce a novel prediction approach, termed the hybrid prediction model, which utilizes not only the current motion of an object, but also the object's trajectory patterns for prediction. We define, mine, and index the trajectory patterns with a novel access method for efficient query processing. We then propose two different query processing techniques along given query time, i.e., for near future and for distant future. The second part covers the prediction problem for moving objects in network space. We formulate a network mobility model that offers a concise representation of mobility statistics extracted from massive collections of historical objects trajectories. This model captures turning patterns of the objects at junctions, at the granularity of individual objects as well as globally. Based on the model, we develop three different algorithms for predicting the future path of a mobile user moving in a road network, named the PathPredictors. The third part of the thesis extends the prediction problem for a single object to that for multiple objects. We introduce a convoy query that retrieves all groups of objects, i.e., convoys, from the objects' historical trajectories, each convoy consists of objects that have traveled together for some time; thus they may also move together in the future. We then propose three efficient algorithms for the convoy discovery, called the CuTS family, that adopt line simplification methods for reducing the size of the trajectories, permitting efficient query processing. For each part, we demonstrate comprehensive experimental results of our proposals, which show significantly improved accuracies for moving object prediction compared with state-of-the-art methods, while also facilitating efficient query processing.

This dissertation challenges an unstudied area in moving objects database domains; predicting (long-term) future locations of moving objects. Moving object prediction enables us to provide a wide range of applications, such as traffic prediction, pre-detection of an aircraft collision, and reporting attractive gas prices for drivers along their routes ahead. Nevertheless, existing location prediction techniques are limited to support such applications since they are generally capable only of short-term predictions. In the real world, many objects exhibit typical movement patterns. This pattern information is able to serve as an important background to tackle the limitations of the existing prediction methods. We aims at offering foundations of pattern-aware prediction for moving objects, rendering more precise prediction results. Specifically, this thesis focuses on three parts. The first part of the thesis studies the problem of predicting future locations of moving objects in Euclidean space. We introduce a novel prediction approach, termed the hybrid prediction model, which utilizes not only the current motion of an object, but also the object's trajectory patterns for prediction. We define, mine, and index the trajectory patterns with a novel access method for efficient query processing. We then propose two different query processing techniques along given query time, i.e., for near future and for distant future. The second part covers the prediction problem for moving objects in network space. We formulate a network mobility model that offers a concise representation of mobility statistics extracted from massive collections of historical objects trajectories. This model captures turning patterns of the objects at junctions, at the granularity of individual objects as well as globally. Based on the model, we develop three different algorithms for predicting the future path of a mobile user moving in a road network, named the PathPredictors. The third part of the thesis extends the prediction problem for a single object to that for multiple objects. We introduce a convoy query that retrieves all groups of objects, i.e., convoys, from the objects' historical trajectories, each convoy consists of objects that have traveled together for some time; thus they may also move together in the future. We then propose three efficient algorithms for the convoy discovery, called the CuTS family, that adopt line simplification methods for reducing the size of the trajectories, permitting efficient query processing. For each part, we demonstrate comprehensive experimental results of our proposals, which show significantly improved accuracies for moving object prediction compared with state-of-the-art methods, while also facilitating efficient query processing.

With the development of wireless communications and mobile computing technologies, the applications of moving objects have been developed in many topics, for example, traffic monitoring, mobile E-Commerce, Navigation System, and Geographic Information System. The feature of the moving objects is that objects change their locations continuously. Conventional spatial databases can not support to store the moving objects efficiently, because the databases must be updated frequently. Therefore, it is important to index moving objects for efficiently answering queries about moving objects. Among the spatial indexing methods for predicting current and future data, the approach of parametric spatial access methods has been applied largely, since it needs little memory space to preserve parametric rectangles, and it still provides good performance, so it is adopted generally. The methods of this approach include the TPR-tree, the TPR*-tree, the Bx-tree, and the Bxr-tree. Among those methods, the Bxr-tree improves CPU performance of TPR-tree by expanding query region first, and improves I/O performance of the Bxr-tree by expanding the data blocks additionally. However, the query process of the B$^x_r$-tree is too rough such that it costs too much CPU and I/O time to check the useless data. Therefore, in this thesis, we propose a new data structure and a new query processing method named Adjustable Expanded Index (AEI), to improve the disadvantages of the Bxr-tree. In our method, we let each block records the maximum and minimum speeds of each of eight directions, instead of only the maximum speed of each of four directions in the Bxr-tree method. Based on the data structure, the query region can be expanded in each of eight directions individually, instead of being expanded in each of four directions once in the Bxr-tree method. Moreover, in our AEI method, the data blocks can be expanded according to the direction toward the query region, instead of being expanded in four directions in the Bxr-tree method. In this way, the query process of AEI checks less number of data blocks because it considers the minimum speed of each of eight directions. Furthermore, the objects are divided into four groups in AEI according to their directions, while the Bxr-tree method does not. Only the objects moving to query region will be checked in the query process of AEI. Therefore, we can reduce more number of retrieved data blocks and the number of I/O operations in our method than the Bxr-tree. From our simulation, we show that the query process of the AEI method is more efficient than that of the Bxr-tree in term of the average numbers of retrieved data blocks and I/O operations.