Spatial Temporal Query Research Articles

A pattern accumulated compression method for trajectories constrained by urban road networks

In an edge computing framework, a moving object in an urban road network can collect both its own and others’ trajectories to answer spatial–temporal queries. How to store the ever increasing amount of trajectories with limited storage space is a great challenge. We observe that the traces of moving objects in urban road networks usually exhibit great repetition in terms of spatial and velocity distributions. So we propose two strategies, Pattern Accumulated Compression based on Average Velocity (PAC-AV) and Pattern Accumulated Compression based on Representative Velocity (PAC-RV), to compress trajectories online on the mobile devices. The former mainly exploits the path pattern to compress trajectories while the latter exploits both the path pattern and the velocity pattern to compress trajectories. Both PAC-AV and PAC-RV compress the trajectories by two stages. First, we extract the spatial as well as velocity components according to a velocity deviation threshold. Second, we compress the components into a series of identifiers leveraging a Path Pattern Dictionary (PPD) and a Velocity Pattern Dictionaries (VPD). Our method not only greatly cuts down the storage space but also directly supports the velocity-related queries on the compressed trajectories. Experimental results demonstrate the effectiveness and efficiency of our method.

An Improvement for Spatial-Temporal Queries of ATMGRAPH

An Improvement for Spatial-Temporal Queries of ATMGRAPH

A Literature Review of Location-Aware Computing Policies

Location-aware computing systems implementation has the challenge of maintaining its consistency due to frequent disconnections in the mobile environment. Many other exciting issues associated with this domain provide a fertile ground for many mobile database researchers to tackle. This work contributes by taking up-to-date literature on current location-aware mobile data management practices. The paper includes taxonomy for caching, map-matching, and location privacy protection with spatial-temporal queries, unlike other review papers, which only address a specific aspect of location-dependent data management. First, an in-depth analysis of state-of-the-art and classified literature is presented, taking into account the domain of application models and approaches for evaluation. Secondly, it provides a theoretical and analytical investigation of location-conscious computing approaches available in the literature, examining the merit and demerit of each method and the evolving processes between them. Finally, the paper highlighted the outstanding opportunities for future research directions.

PRESS

Location data becomes more and more important. In this paper, we focus on the trajectory data, and propose a new framework, namely PRESS ( <u>P</u>aralleled <u>R</u>oad-Network-Based Trajectory Compr<u>ess</u>ion ), to effectively compress trajectory data under road network constraints. Different from existing work, PRESS proposes a novel representation for trajectories to separate the spatial representation of a trajectory from the temporal representation, and proposes a Hybrid Spatial Compression (HSC) algorithm and error Bounded Temporal Compression (BTC) algorithm to compress the spatial and temporal information of trajectories respectively. PRESS also supports common spatial-temporal queries without fully decompressing the data. Through an extensive experimental study on real trajectory dataset, PRESS significantly outperforms existing approaches in terms of saving storage cost of trajectory data with bounded errors.

Spatio-Temporal Query Processing Method in Wireless Sensor Networks

In wireless sensor networks, observers are interested in spatio-temporal information monitored by sensors. Observers are not interested in sensor itself or massive irrelevant readings from sensors. They often issue spatio-temporal queries such as Which events did happen in region R from 10:00 to 12:00?. Since battery supply of sensors is limited, energy-efficient spatio-temporal query processing in sensor networks has become an important research problem. This paper presents a spatio-temporal query processing algorithm based on data-centric storage. The energy consumption of sensors in three storage strategies, namely external storage, local storage and data-centric storage, is analyzed and compared in this paper. The paper also studies the influence of the probability of an event occurring, node density, number of event types, number of queries, temporal window size and spatial area size in spatial-temporal query on energy consumption. Analytical and experimental results show that in most cases the spatio-temporal query processing algorithm proposed in this paper can save more energy than those algorithms based on the external storage and local storage strategies.

Knowledge-based image retrieval with spatial and temporal constructs

A knowledge-based approach to retrieve medical images by feature and content with spatial and temporal constructs is developed. Selected objects of interest in an image are segmented and contours are generated. Features and content are extracted and stored in a database. Knowledge about image features can be expressed as a type abstraction hierarchy (TAH), the high-level nodes of which represent the most general concepts. Traversing TAH nodes allows approximate matching by feature and content if an exact match is not available. TAHs can be generated automatically by clustering algorithms based on feature values in the databases and hence are scalable to large collections of image features. Since TAHs are generated based on user classes and applications, they are context- and user-sensitive. A knowledge-based semantic image model is proposed to represent the various aspects of an image object's characteristics. The model provides a mechanism for accessing and processing spatial, evolutionary and temporal queries. A knowledge-based spatial temporal query language (KSTL) has been developed that extends ODMG's OQL and supports approximate matching of features and content, conceptual terms and temporal logic predicates. Further, a visual query language has been developed that accepts point-click-and-drag visual iconic input on the screen that is then translated into KSTL. User models are introduced to provide default parameter values for specifying query conditions. We have implemented the KMeD (Knowledge-based Medical Database) system using these concepts.