Chapter 6: Access Methods and Query Processing Techniques

Pasquale, Adriano Di; Forlizzi, Luca; Jensen, Christian S.; Manolopoulos, Yannis; Nardelli, Enrico; Pfoser, Dieter; Proietti, Guido; Šaltenis, Simonas; Theodoridis, Yannis; Tzouramanis, Theodoros; Vassilakopoulos, Michael

doi:10.1007/978-3-540-45081-8_6

Cited by 8 publications

(1 citation statement)

References 73 publications

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“…Bearing these goals in mind, whenever there are several ways of explaining a concept we use the terminology and notation that is common to these fields rather than that which is more commonly used in spatial databases (e.g., Rigaux et al [2001] and Shekhar and Chawla [2003]). Thus the survey is not necessarily aimed at database researchers (as is for example [Di Pasquale et al 2003]) although we hope that they will also find it useful. Please note also that we are focussing on the representation of spatial objects which means that the objects have extent (e.g., Arman and Aggarwal [1993], Samet [1990aSamet [ , 1990b, Suetens et al [1992]) rather than being merely points.…”

Section: Introductionmentioning

confidence: 99%

Object-based and image-based object representations

Samet

2004

ACM Comput. Surv.

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An overview is presented of object-based and image-based representations of objects by their interiors. The representations are distinguished by the manner in which they can be used to answer two fundamental queries in database applications: (1) Feature query: given an object, determine its constituent cells (i.e., their locations in space). (2) Location query: given a cell (i.e., a location in space), determine the identity of the object (or objects) of which it is a member as well as the remaining constituent cells of the object (or objects). Regardless of the representation that is used, the generation of responses to the feature and location queries is facilitated by building an index (i.e., the result of a sort) either on the objects or on their locations in space, and implementing it using an access structure that correlates the objects with the locations. Assuming the presence of an access structure, implicit (i.e., image-based) representations are described that are good for finding the objects associated with a particular location or cell (i.e., the location query), while requiring that all cells be examined when determining the locations associated with a particular object (i.e., the feature query). In contrast, explicit (i.e., object-based) representations are good for the feature query, while requiring that all objects be examined when trying to respond to the location query. The goal is to be able to answer both types of queries with one representation and without possibly having to examine every cell. Representations are presented that achieve this goal by imposing containment hierarchies on either space (i.e., the cells in the space in which the objects are found), or objects. In the former case, space is aggregated into successively larger-sized chunks (i.e., blocks), while in the latter, objects are aggregated into successively larger groups (in terms of the number of objects that they contain). The former is applicable to image-based interior-based representations of which the space pyramid is an example. The latter is applicable to object-based interior-based representations of which the R-tree is an example. The actual mechanics of many of these representations are demonstrated in the VASCO JAVA applets found at

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Section: Introductionmentioning

confidence: 99%

Object-based and image-based object representations

Samet

2004

ACM Comput. Surv.

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Uncertainty in Spatial Trajectories

Trajcevski

2011

Computing With Spatial Trajectories

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Trajectory Indexing Using Movement Constraints*

Pfoser

Jensen

2005

Geoinformatica

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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; 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 occurs in two-dimensional (x, t) space. The advantages of considering such lower-dimensional trajectories are that the overall size of the data is reduced and that lower-dimensional data is to be indexed. Since off-the-shelf database management systems typically do not offer higher-dimensional indexing, this reduction in dimensionality allows us to use existing 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. A decisive factor here is the fractal dimension of the networkVthe lower, the more efficient is the proposed approach. This hypothesis is verified by an experimental study that incorporates trajectories stemming from real and synthetic road networks.

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Chapter 6: Access Methods and Query Processing Techniques

Cited by 8 publications

References 73 publications

Object-based and image-based object representations

Object-based and image-based object representations

Uncertainty in Spatial Trajectories

Trajectory Indexing Using Movement Constraints*

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