A taxonomy for moving object queries in spatial databases

Highlights

• This study explains the variety of queries in moving objects databases.

• The movement patterns and the new raised queries of the moving objects databases.

• This taxonomy shows the targeted queries of the current moving objects data structures.

Abstract

In the past decade, many works have focused on the development of moving object database indexing and querying. Most of those works have concentrated on the common spatial queries which are used with static objects as well. However, moving objects have different features from static objects which may lead to a variety of queries. Therefore, it is important to understand the full spectrum of moving object queries, even before starting to build an index structure for such objects. The aim of this paper is to provide a complete picture of the capabilities of moving object queries. Thus motivated, in this paper we propose a taxonomy of moving object queries, comprising five perspectives: (i) Location perspective, (ii) Motion perspective, (iii) Object perspective, (vi) Temporal perspective and (v) Patterns perspective. These give an overall view of what moving object queries are about. In this work, each perspective is described and examples are given.

Introduction

Moving objects are objects (points) that change their locations (geometric attributes) over time  [1], which requires a higher update frequency. For instance, taxis traveling around the cities are considered as moving objects because they frequently change their locations over time, which requires a high level of updating in the taxi centers’ databases. Other examples are cars, aircraft, ships, mobile phone users, armies, individuals and many more. Moreover, tracking these moving objects is essential for big data applications. Therefore, the indexing of moving objects plays a critical role in query processing, and our aim is to present a complete study regarding the capabilities of querying moving objects.

In addition, the storage and manipulation of moving objects will be based on spatial information representing static geographical objects alongside temporal information. There is an important difference between the indexing and querying of moving objects and that of static objects. With static objects, spatial data structures basically assume that the objects are constant unless conspicuously updated, whereas moving objects require frequent updates of the locations. Fig. 1 illustrates the difference between moving objects and static objects. Fig. 1(a) shows that the locations of static objects are constant unless conspicuously altered (e.g. change address). On the other hand, Fig. 1(b) shows that the locations of moving objects are constantly updated. Moreover, Fig. 1(b) shows the new features of the moving objects such as direction, velocity and movement patterns, which do not exist for static objects.

With the development of tracking and positioning systems, such as GPS and WI-FI, the correct recording of locations has become available which allows the querying of the moving objects. A large number of moving object applications have different perspectives regarding the querying of the moving objects  [2], [3]. Besides the typical types of queries such as point queries, range queries and k-nearest neighbor queries which are widely used in static object applications, querying moving objects has many other dimensions. For example, the moving objects change their locations with time; therefore, tracking moving objects at certain times (temporal queries) is essential in security applications. Moreover, tracking moving objects that entered or left a certain area (topological queries), is another unique perspective of moving objects querying. These queries illustrate the different vectors of the moving objects which do not exist in static object applications.

In addition, different researches have been developed to analyse objects which change their spatial location over time in order to track and monitor cars, trains, and ships  [4], [5], [6], [7]. Most of these researches on moving objects have concentrated on the common spatio-temporal queries with a lack of attention being paid to the variety of queries about moving objects. In our work, we concentrated on building a taxonomy that assists to achieve a better understanding of the moving object queries in order to build data structures for moving objects. The main goal is to disclose the variety of possible queries about moving objects in spatio-temporal databases.

In this paper, we present a taxonomy for moving object queries. The queries about moving objects can be performed in different environments (underlying structures) which include the Euclidean space, spatial road network and cellular space. Therefore, we explain the moving objects’ environments in Section  2. Then Section  3 explains the moving objects query taxonomy from various perspectives. First is the Location perspective, which includes common spatial queries such as $K$ nearest neighbors (KNNs), range queries and others. Second is the Motion perspective, which covers direction, velocity, distance and displacement queries. Third is the Object perspective, which includes the type status queries and the form status queries. Fourth is the Temporal perspective which includes the trajectory, timestamped, inside, disjoint, meet, equal, contain, overlap and period queries. Last we explain the Patterns perspective, where the moving objects are using undefined movement or predefined movement patterns which include many patterns such as spatial movement patterns and temporal movement patterns. Each perspective is explained with illustrated examples. Section  4 summarizes our taxonomy with targeted queries in most of the current moving object data structures.