On the route to a spatio-temporal geoscience information system, an appropriate data model for geo-objects in space and time has been developed. In this model, geo-objects are represented as sequences of geometries and properties with continuous evo...
The analysis of temporal series—in particular, analysis of multisensor data—is a complex problem. It depends on the application domain, the way the data have to be used, and sensors available, among other factors. Various models, algorithms, and tech...
The unprecedented development of Internet of Things (IoT) technology produces humongous amounts of spatio-temporal sensing data with various geometry types. However, processing such datasets is often challenging due to high-dimensional sensor data ge...
Considering the time dimension in big data analytics allows for a more complete insight into the analyzed phenomena and thus for gaining a competitive advantage on the market. The entrepreneurs also reported the need for temporal big data analytics,...
Current intelligent information systems require complex database approaches managing and monitoring data in a spatio-temporal manner. Many times, the core of the temporal system element is created on the relational platform. In this paper, a summary...
Remotely sensed data, with high spatial and temporal resolutions, can hardly be provided by only one sensor due to the tradeoff in sensor designs that balance spatial resolutions and temporal coverage. However, they are urgently needed for improving...
A temporal model describes processes as a sequence of observable events characterised by distinguishable actions in time. Conformance checking allows these models to determine whether any sequence of temporally ordered and fully-observable events com...
The integration of heterogeneous spatio-temporal datasets presents a critical challenge in geospatial data science, particularly when combining large-scale, passively collected “big” data with precise but sparse “small” data....
The trend to use large amounts of simple sensors as opposed to a few complex sensors to monitor places and systems creates a need for temporal pattern mining algorithms to work on such data. The methods that try to discover re-usable and interpretabl...
In recent years, there has been tremendous growth in the field of indoor and outdoor positioning sensors continuously producing huge volumes of trajectory data that has been used in many fields such as location-based services or location intelligence...
In many tasks related to an object’s observation or real-time monitoring, the gathering of temporal multimodal data is required. Such data sets are semantically connected as they reflect different aspects of the same object. However, data sets...
In the geometric data model for spatio-temporal data, introduced by Chomicki and Revesz [1], spatio-temporal data are modelled as a finite collection of triangles that are transformed by time-dependent affinities of the plane. To facilitate querying...
Multi-source spatio-temporal data analysis is an important task in the development of smart cities. However, traditional data analysis methods cannot adapt to the growth rate of massive multi-source spatio-temporal data and explain the practical sign...
To address the problem of overlooking target movement characteristics and historical activity patterns in conventional path estimation methods, we propose a method based on the principle of multi-source spatio-temporal data fusion. It integrates opti...
In crop and livestock management within the framework of precision agriculture, scenarios full of sensors and devices are deployed, involving the generation of a large volume of data. Some solutions require rapid data exchange for action or anomaly d...
In this paper, we present an implementation of a research data management system that features structured data storage for spatio-temporal experimental data (environmental perception and navigation in the framework of autonomous driving), including m...
In recent years, the proliferation of health data sources due to computer technologies has prompted the use of visualization techniques to tackle epidemiological challenges. However, existing reviews lack a specific focus on the spatial and temporal...
The spatial–temporal correlation is an important feature of sensor data in wireless sensor networks (WSNs). Most of the existing works based on the spatial–temporal correlation can be divided into two parts: redundancy reduction and anomaly detection...
Identifying abnormal process operation with spatial-temporal data remains an important and challenging work in many practical situations. Although spatial-temporal data identification has been extensively studied in some domains, such as public healt...
In recent years, with the widespread use of location-aware handheld devices and the development of wireless networks, trajectory data have shown a trend of rapid growth in data volume and coverage, which has led to the prosperous development of locat...
The transmission and storage of weather radar products will be an important problem for future weather radar applications. The aim of this work is to provide a solution for real-time transmission of weather radar data and efficient data storage. By u...
This paper presents a novel approach to a complex process of electrical capacitance tomography (ECT) measurement data analysis. ECT is frequently employed for non-invasive monitoring of industrial process phenomena. Proposed methodology is based on t...
Every day large quantities of spatio-temporal data are captured, whether by Web-based companies for social data mining or by other industries for a variety of applications ranging from disaster relief to marine data analysis. Making sense of all this...
The primary objective of this study is to explore spatio-temporal effects of the built environment on station-based travel distances through large-scale data processing. Previous studies mainly used global models in the causal analysis, but spatial a...
Forest fires have frequently occurred and caused great harm to people’s lives. Many researchers use machine learning techniques to predict forest fires by considering spatio-temporal data features. However, it is difficult to efficiently obtain...
Spatio-temporal data fusion refers to the technique of combining high temporal resolution from coarse satellite images and high spatial resolution from fine satellite images. However, data availability remains a major limitation in algorithm developm...
Wireless sensor networks (WSNs) have various applications which include zone surveillance, environmental monitoring, event tracking where the operation mode is long term. WSNs are characterized by low-powered and battery-operated sensor devices with...
Benefiting from recent advantages in location-aware technologies, movement data are becoming ubiquitous. Hence, numerous research topics with respect to movement data have been undertaken. Yet, the research of dynamic interactions in movement data is...
This paper introduces a dual-strategy model based on temporal transformation and fuzzy theory, and designs a partitioned mining algorithm for periodic frequent patterns in large-scale event data (3P-TFT). The model reconstructs original event data th...
In this paper, we explore spatio-temporal clusters using massive floating car data from a complex network perspective. We analyzed over 85 million taxicab GPS points (floating car data) collected in Wuhan, Hubei, China. Low-speed and stop points were...
Environmental data visualization tools are used for data validation, maintenance, and assessment of status and trends of environmental variables. However, currently available visualization tools do not meet all the requirements needed for efficient d...
Movement ecology is a field which places movement as a basis for understanding animal behavior. To realize this concept, ecologists rely on data collection technologies providing spatio-temporal data in order to analyze movement. Recently, wireless s...
Sensor networks generate substantial amounts of frequently updated, highly dynamic data that are transmitted as packets in a data stream. The high frequency and continuous unbound nature of data streams leads to challenges when deriving knowledge fro...
Satellite data is of high importance for ocean environment monitoring and protection. However, due to the missing values in satellite data, caused by various force majeure factors such as cloud cover, bad weather and sensor failure, the quality of sa...
Volunteered geographic information (VGI) has great potential to reveal spatial and temporal dynamics of geographic phenomena. However, a variety of potential biases in VGI are recognized, many of which root from volunteer data contribution activities...
Asset management is a process that deals with numerous types of data, including spatial and temporal data. Such an occurrence is attributed to the proliferation of information sources. However, the lack of a comprehensive asset data model that encomp...
The multi-granularity temporal knowledge graph question-answering model consists of two core tasks: question information extraction and knowledge graph embedding representation. Existing studies typically compute the relevance score between the quest...
Remote sensing can provide spatio-temporal continuous Earth observation data and is becoming the main data source for spatial and temporal analysis. Remote sensing data have been widely used in applications such as meteorological monitoring, forest i...
Multivariate time-series data with a contextual spatial attribute have extensive use for finding anomalous patterns in a wide variety of application domains such as earth science, hurricane tracking, fraud, and disease outbreak detection. In most set...
Remote sensing provides rich sources of data for the monitoring of land surface dynamics. However, single-sensor systems are constrained from providing spatially high-resolution images with high revisit frequency due to the inherent sensor design lim...
Stakeholders’ Participation (SP) aims to involve stakeholders in decision-making processes about significant choices affecting their organizations, cities, or communities. Stakeholders’ participation is maintained through SP processes (SP...
Humans learn from a lot of information sources to make decisions. Once this information is learned in the brain, spatio-temporal associations are made, connecting all these sources (variables) in space and time represented as brain connectivity. In r...
The COVID-19 pandemic is the first major pandemic of the digital age and has been characterised by unprecedented public consumption of spatial and temporal disease data, which can enable greater transparency and accountability of governments to the p...
Three-dimensional ocean observation is the foundation for accurately predicting ocean information. Although ocean observation sensor arrays can obtain internal data, their deployment is difficult, costly, and prone to component failures and environme...
With the proliferation of mobile devices, the amount of social media users and online news articles are rapidly increasing, and text information online is accumulating as big data. As spatio-temporal information becomes more important, research on ex...
Task-oriented scene data in big data and cloud environments of a smart city that must be time-critically processed are dynamic and associated with increasing complexities and heterogeneities. Existing hybrid tree-based external indexing methods are i...
In the arid grasslands of northern China, unreasonable grazing methods can reduce the water content and species numbers of grassland vegetation. This project uses solar-powered GPS collars to obtain track data for sheep grazing. In order to eliminate...
Conventional drivers of liking analysis was extended with a time dimension into temporal drivers of liking (TDOL) based on functional data analysis methodology and non-additive models for multiple-attribute time-intensity (MATI) data. The non-additiv...
In this study, we analyzed the spatiotemporal characteristics of pedestrian behavior in street spaces using pedestrian count data—specifically, the number of pedestrians passing in front of infrared sensors installed throughout the downtown are...
Data missing is a common problem in wireless sensor networks. Currently, to ensure the performance of data processing, making imputation for the missing data is the most common method before getting into sensor data analysis. In this paper, the tempo...
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