indexing in spatio-temporal databases by using the technique of overlapping is investigated. Overlapping has been previously applied in various access methods to combine consecutive structure instances into a single structure, without storing identical sub-structures. In this way, space is saved without sacrificing time performance. A new access method, overlapping linear quadtrees is introduced. This structure is able to store consecutive historical raster images, a database of evolving images. Moreover, it can be used to support query processing in such a database. Five such spatio-temporal queries along with the respective algorithms that take advantage of the properties of the new structure are introduced. The new access method was implemented and extensive experimental studies for space efficiency and query processing performance were conducted. A number of results of these experiments are presented. As far as space is concerned, these results indicate that, in the case of similar consecutive images, considerable storage is saved in comparison to independent linear quadtrees. In the case of query processing, the results indicate that the proposed algorithmic approaches outperform the respective straightforward algorithms, in most cases. The region data sets used in experiments were real images of meteorological satellite views and synthetic random images with specified aggregation

AbstractÐWe present a new approach for indexing animated objects and efficiently answering queries about their position in time and space. In particular, we consider an animated movie as a spatiotemporal evolution. A movie is viewed as an ordered sequence of frames, where each frame is a 2D space occupied by the objects that appear in that frame. The queries of interest are range queries of the form, ªfind the objects that appear in area S between frames fi and fjº as well as nearest neighbor queries such as, ªfind the q nearest objects to a given position A between frames fi and fj.º The straightforward approach to index such objects considers the frame sequence as another dimension and uses a 3D access method (such as, an R-Tree or its variants). This, however, assigns long ªlifetimeº intervals to objects that appear through many consecutive frames. Long intervals are difficult to cluster efficiently in a 3D index. Instead, we propose to reduce the problem to a partial-persistence problem. Namely, we use a 2D access method that is made partially persistent. We show that this approach leads to faster query performance while still using storage proportional to the total number of changes in the frame evolution. What differentiates this problem from traditional temporal indexing approaches is that objects are allowed to move and/or change their extent continuously between frames. We present novel methods to approximate such object evolutions. We formulate an optimization problem for which we provide an optimal solution for the case where objects move linearly. Finally, we present an extensive experimental study of the proposed methods. While we concentrate on animated movies, our approach is general and can be applied to other spatiotemporal applications as well. Index TermsÐAccess methods, spatiotemporal databases, animated objects, multimedia. 1

With the rapid increase in the use of inexpensive, location-aware sensors in a variety of new applications, large amounts of time-sequenced location data will soon be accumulated. Efficient indexing techniques for managing these large volumes of trajectory data sets are urgently needed. The key requirements for a good trajectory indexing technique is that it must support both searches and inserts efficiently.

In real-life applications, the objects are both spatial and temporal referenced. The objects which continuously change their location are called moving objects. With the development of wireless communication and positioning technology, it becomes necessary to store and index those objects in database. Due to the complexity of the problem, many pure spatial index structures are unable to index large volume of moving objects in database. In this paper, we propose a whole new idea based on hashing technique. Since it is impossible to re-index all the objects after each time period, we store the objects in buckets. When an object moves within a bucket, the database does not make any change. By using this technique, the number of database update is greatly reduced which makes the index procedure feasible. Then, we extend the previous system structure by introducing a filter layer between the position information collectors and the database. Also four different methods based on the...

The rapid increase in spatio-temporal applications calls for new auxiliary indexing structures. A typical spatio-temporal application is one that tracks the behavior of moving objects through location-aware devices (e.g., GPS). Through the last decade, many spatio-temporal access methods are developed. Spatio-temporal access methods focus on two orthogonal directions: (1) Indexing the past, (2) Indexing the current and predicted future positions. In this short survey, we classify spatio-temporal access methods for each direction based on their underlying structure with a brief discussion of future research directions.

Abstract. The development of a spatiotemporal access method suitable for objects moving on fixed networks is a very attractive challenge due to the great number of real-world spatiotemporal database applications and fleet management systems dealing with this type of objects. In this work, a new indexing technique, named Fixed Network R-Tree (FNR-Tree), is proposed for objects constrained to move on fixed networks in 2-dimensional space. The general idea that describes the FNR-Tree is a forest of 1-dimensional (1D) R-Trees on top of a 2-dimensional (2D) R-Tree. The 2D R-Tree is used to index the spatial data of the network (e.g. roads consisting of line segments), while the 1D R-Trees are used to index the time interval of each object’s movement inside a given link of the network. The performance study, comparing this novel access method with the traditional R-Tree under various datasets and queries, shows that the FNR-Tree outperforms the R-Tree in most cases. 1

The domain of spatiotemporal applications is a treasure trove of new types of data as well as queries. For designing and testing novel data types and access methods that will support these applications, large spatiotemporal datasets are needed. As in many cases it is not possible to obtain real datasets, either they do not exist or they are not accessible, synthetic datasets are governed by artificial parameters rather than by the (sometimes invisible) rules governing real-world behavior. In this work we show how, at least for some cases, one can generate spatiotemporal datasets that simulate real-world behavior. We illustrate example cases and translate them into appropriate calls of GSTD, a spatiotemporal data generator. The generated data is illustrated using two-dimensional snapshot pictures as well as three-dimensional (two spatial plus one temporal dimension) trajectory images.

With the proliferation of mobile computing, the ability to index efficiently the movements of mobile objects becomes important. Objects are typically seen as moving in two-dimensional (x,y) space, which means that their movements across time may be embedded in the three-dimensional (x,y,t) space. Further, the movements are typically represented as trajectories, sequences of connected line segments. In certain cases, movement is restricted, and specifically in this paper, we aim at exploiting that movements occur in transportation networks to reduce the dimensionality of the data. Briefly, the idea is to reduce movements to occur in one spatial dimension. As a consequence, the movement data becomes two-dimensional (x,t). The advantages of considering such lowerdimensional trajectories are the reduced overall size of the data and the lower-dimensional indexing challenge. Since off-the-shelf database management systems typically do not offer higherdimensional indexing, this reduction in dimensionality allows us to use such DBMSes to store and index trajectories. Moreover, we argue that, given the right circumstances, indexing these dimensionality-reduced trajectories can be more efficient than using a three-dimensional index. This hypothesis is verified by an experimental study that incorporates trajectories stemming from real and synthetic road networks.

Position data is expected to play a central role in a wide range of mobile computing applications, including advertising, leisure, safety, security, tourist, and traffic applications. Applications such as these are characterized by large quantities of wirelessly Internetworked, position-aware mobile objects that receive services where the objects ’ position is essential. The movement of an object is captured via sampling, resulting in a trajectory consisting of a sequence of connected line segments for each moving object. This paper presents a technique for querying these trajectories. The technique uses indices for the processing of spatiotemporal range queries on trajectories. If object movement is constrained by the presence of infrastructure, e.g., lakes, park areas, etc., the technique is capable of exploiting this to reduce the range query, the purpose being to obtain better query performance. Specifically, an algorithm is proposed that segments the original range query based on the infrastructure contained in its range. The applicability and limitations of the proposal are assessed via empirical performance studies with varying datasets and parameter settings.

This paper introduces a novel type of query, what we name Spatio-temporal Pattern Queries (STP). Such a query specifies a spatio-temporal pattern as a sequence of distinct spatial predicates where the predicate temporal ordering (exact or relative) matters. STP queries can use various types of spatial predicates (range search, nearest neighbor, etc.) where each such predicate is associated (1) with an exact temporal constraint (a time-instant or a time-interval), or (2) more generally, with a relative order among the other query predicates. Using traditional spatio-temporal index structures for these types of queries would be either inefficient or not an applicable solution. Alternatively, we propose specialized query evaluation algorithms for STP queries With Time. We also present a novel index structure, suitable for STP queries With Order. Finally, we conduct a comprehensive experimental evaluation to show the merits of our techniques. 1