Spatio-temporal Index Structure Research Articles

A spatiotemporal data model and an index structure for computational time geography

The availability of Spatiotemporal Big Data has provided a golden opportunity for time geographical studies that have long been constrained by the lack of individual-level data. However, how to store, manage, and query a huge number of time geographic entities effectively and efficiently with complex spatiotemporal characteristics and relationships poses a significant challenge to contemporary GIS platforms. In this article, a hierarchical compressed linear reference (CLR) model is proposed to transform network-constrained time geographic entities from three-dimensional (3D) (x, y, t) space into two-dimensional (2D) space. Accordingly, time geographic entities can be represented as 2D spatial entities and stored in a classical spatial database. The proposed CLR model supports a hierarchical linear reference system (LRS) including not only underlying a link-based LRS but also multiple higher-level route-based LRSs. In addition, an LRS-based spatiotemporal index structure is developed to index both time geographic entities and the corresponding hierarchical network. The results of computational experiments on large datasets of space–time paths and prisms show that the proposed hierarchical CLR model is effective at storing and managing time geographic entities in road networks. The developed index structure achieves satisfactory query performance in milliseconds on large datasets of time geographic entities.

Dynamic Study of Intelligent Traffic Behaviour Based on Multiple Traffic Modes

With the rapid development of society, the traffic problem has become increasingly severe, and the traditional methods can no longer effectively solve the current social traffic behaviour problems. Although studies on the dynamics of human traffic behaviour based on traffic modes can effectively reveal the anomalies in traffic behaviour, few studies integrate intelligent traffic behaviour with multiple traffic modes. Based on the numerous traffic data of bike-sharing and ride-hailing in a Chinese city, this paper reveals the dynamic characteristics of various traffic behaviours in the city by combining spatiotemporal characteristics index and urban spatial structure with human traffic behaviour patterns. The experimental results show that the traffic behaviour of the town presents a double logarithmic power-law distribution in time characteristics, and there is a close interdependent dynamic relationship with the city’s spatial structure. The research in this paper can reveal the relationship between bimodal power-law distribution and spatial characteristics in complex systems and help solve social traffic problems effectively in social reality. Further research results can provide practical planning guidance for the behavioural integration of multiple traffic in smart cities.

A Probabilistic Range Query of Moving Objects in Road Network

The range query of moving objects in a road network is widely used in battlefield environment, intelligent transportation systems, and mobile phone location. It has gradually become a research hotspot in the field of spatiotemporal data management. Most of the existing research on range query of moving objects in the road network is based on the premise of dense sampling of the device and complete storage of data. It ignores the uncertainty of discrete location data caused by a blind area of actual devices and limited memory, which cannot guarantee the accuracy of the range query. In this paper, the processing framework of probabilistic range query of moving objects in the road network is constructed. A spatiotemporal index structure is proposed based on the framework. This index can effectively represent time weights and the relationship between road sections. The probabilistic range query algorithm of moving objects in the road network with an uncertain trajectory caused by sampling frequency is designed and implemented. The experiments verify that the method proposed in this paper can improve query efficiency and ensure query accuracy.

Disk-Based Indexing of Recent Trajectories

The plethora of location-aware devices has led to countless location-based services in which huge amounts of spatiotemporal data get created every day. Several applications require efficient processing of queries on the locations of moving objects over time, i.e., the moving object trajectories. This calls for efficient trajectory-based indexing methods that capture both the spatial and temporal dimensions of the data in a way that minimizes the number of disk I/Os required for both updating and querying. Most existing spatiotemporal index structures capture either the current locations of the moving objects or the entire history of the moving objects. Historical spatiotemporal indexing methods require multiple disk I/Os to process new updates and use a discrete trajectory representation that may result in incomplete query results. In this article, we introduce the trails-tree, a disk-based data structure for indexing recent trajectories. The trails-tree requires half the number of disk I/Os needed by other historical spatiotemporal indexing methods for the insertion and querying operations. We give a detailed description of the trails-tree, and we mathematically analyze its performance. Moreover, we present a novel query processing algorithm that ensures the completeness of the query result set. We experimentally verify the performance of the trails-tree using various real and synthetic datasets.

ST-Hadoop: a MapReduce framework for spatio-temporal data

This paper presents ST-Hadoop; the first full-fledged open-source MapReduce framework with a native support for spatio-temporal data. ST-Hadoop is a comprehensive extension to Hadoop and SpatialHadoop that injects spatio-temporal data awareness inside each of their layers, mainly, language, indexing, and operations layers. In the language layer, ST-Hadoop provides built in spatio-temporal data types and operations. In the indexing layer, ST-Hadoop spatiotemporally loads and divides data across computation nodes in Hadoop Distributed File System in a way that mimics spatio-temporal index structures, which result in achieving orders of magnitude better performance than Hadoop and SpatialHadoop when dealing with spatio-temporal data and queries. In the operations layer, ST-Hadoop shipped with support for three fundamental spatio-temporal queries, namely, spatio-temporal range, top-k nearest neighbor, and join queries. Extensibility of ST-Hadoop allows others to extend features and operations easily using similar approaches described in the paper. Extensive experiments conducted on large-scale dataset of size 10 TB that contains over 1 Billion spatio-temporal records, to show that ST-Hadoop achieves orders of magnitude better performance than Hadoop and SpaitalHadoop when dealing with spatio-temporal data and operations. The key idea behind the performance gained in ST-Hadoop is its ability in indexing spatio-temporal data within Hadoop Distributed File System.

Indexing Fuzzy Spatiotemporal Data for Efficient Querying: A Meteorological Application

Spatiotemporal data, in particular fuzzy and complex spatial objects representing geographic entities and relations, is a topic of great importance in geographic information systems and environmental data management systems. For database researchers, modeling and designing a database of fuzzy spatiotemporal data and querying such a database efficiently have been challenging issues due to complex spatial features and uncertainty involved. This paper presents an integrated approach to modeling, indexing, and efficiently querying spatiotemporal data related to fuzzy spatial and complex objects and spatial relations. As our case study, we design and implement a meteorological database application that involves fuzzy spatial and complex objects, and a spatiotemporal index structure, and supports various types of spatial queries including fuzzy spatiotemporal queries. Our implementation is based on an intelligent database system architecture that combines a fuzzy object-oriented database with a fuzzy knowledge base.

SaIL: A Spatial Index Library for Efficient Application Integration

With the proliferation of spatial and spatio-temporal data that are produced everyday by a wide range of applications, Geographic Information Systems (GIS) have to cope with millions of objects with diverse spatial characteristics. Clearly, under these circumstances, substantial performance speed up can be achieved with the use of spatial, spatio-temporal and other multi-dimensional indexing techniques. Due to the increasing research effort on developing new indexing methods, the number of available alternatives is becoming overwhelming, making the task of selecting the most appropriate method for indexing the data according to application needs rather challenging. Therefore, developing a library that can combine a variety of indexing techniques under a common application programming interface can prove to be a valuable tool. In this paper we present SaIL (SpAtial Index Library), an extensible framework that enables easy integration of spatial and spatio-temporal index structures into existing applications. We focus on design issues and elaborate on techniques for making the framework generic enough, so that it can support user defined data types, customizable spatial queries, and a broad range of spatial (and spatio-temporal) index structures, in a way that does not compromise functionality, extensibility and, primarily, ease of use. SaIL is publicly available and has already been successfully utilized for research and commercial applications.

Indexing Moving Objects for Future Position Retrieval on Location-Based Services

Due to the continuous growth of wireless communication technology and mobile equipment, the need for storing and processing data of moving objects arises in a wide range of location-based applications. In this paper, we propose a new spatio-temporal index structure for moving objects, namely the TPKDB-tree, which supports efficient retrieval of future positions and reduces the update cost.

Adaptive index management for future location-based queries

Many location-based applications have arisen in various areas including mobile communications, traffic control and military command and control (C2) systems. And one of the important research issue in these areas is tracking and managing moving objects through spatiotemporal indexing for the efficient location-based services. However, managing exact geometric location information is difficult to be achieved due to continual changes of moving objects.Traditionally spatiotemporal index structures focus on optimizing the node accesses during construction and massive updates that do not refer on-line movement updates. In this paper we propose an indexing framework for future location queries based on space partitioning and the dual transformation. Our method provides a constraint database approach for constructing indexes to improve the efficiency of spatiotemporal query answering. In addition, the performance enhancement is achieved by our cost-based dynamic management algorithm that determines the appropriate index reorganization probabilistically induced by various mobility models and query cost functions. This approach can be applied for the predictive range queries on moving object's trajectories specifically in the mobile communication environments. We evaluate our method and compare the performance with the related spatiotemporal index structures in the simulated environments.