Clustering spatial data streams for targeted alerting in disaster response

2013 ◽  
Author(s):  
Paras Mehta ◽  
Agnès Voisard ◽  
Sebastian Müller
Keyword(s):  
Data Streams ◽  
Spatial Data ◽  
Disaster Response

Using computer clusters for processing spatial-temporal data streams in data acquisition systems

2018 ◽  
Vol 935 (5) ◽  
pp. 54-63
Author(s):  
A.A. Maiorov ◽  
A.V. Materuhin ◽  
I.N. Kondaurov
Keyword(s):  
Data Acquisition ◽  
Data Streams ◽  
Spatial Data ◽  
High Performance ◽  
Temporal Data ◽  
Cyberphysical Systems ◽  
Software Environment ◽  
Computer Clusters ◽  
Data Acquisition Systems ◽  
Essential Components

Geoinformation technologies are now becoming “end-to-end” technologies of the new digital economy. There is a need for solutions for efficient processing of spatial and spatio-temporal data that could be applied in various sectors of this new economy. Such solutions are necessary, for example, for cyberphysical systems. Essential components of cyberphysical systems are high-performance and easy-scalable data acquisition systems based on smart geosensor networks. This article discusses the problem of choosing a software environment for this kind of systems, provides a review and a comparative analysis of various open source software environments designed for large spatial data and spatial-temporal data streams processing in computer clusters. It is shown that the software framework STARK can be used to process spatial-temporal data streams in spatial-temporal data streams. An extension of the STARK class system based on the type system for spatial-temporal data streams developed by one of the authors of this article is proposed. The models and data representations obtained as a result of the proposed expansion can be used not only for processing spatial-temporal data streams in data acquisition systems based on smart geosensor networks, but also for processing spatial-temporal data streams in various purposes geoinformation systems that use processing data in computer clusters.

Class-based Conditional MaxRS Query in Spatial Data Streams

2017 ◽  
Author(s):  
Mir Imtiaz Mostafiz ◽  
S. M. Farabi Mahmud ◽  
Muhammed Mas-ud Hussain ◽  
Mohammed Eunus Ali ◽  
Goce Trajcevski
Keyword(s):  
Data Streams ◽  
Spatial Data

Mobile Vehicle-Borne Environmental Monitoring Based on Environmental Multi-Sensor Integration

2014 ◽  
Vol 522-524 ◽  
pp. 38-43
Author(s):  
Nan Yang ◽  
Zhen Feng Shao ◽  
Lei Zhang
Keyword(s):  
Environmental Monitoring ◽  
Spatial Data ◽  
Disaster Response ◽  
High Efficiency ◽  
Environmental Parameters ◽  
Development Trend ◽  
Quantitative Model ◽  
Environmental Data ◽  
Multiple Sensors ◽  
Mobile Vehicle

Environmental monitoring is increasingly playing a significant role in such aspects as environment protection, emergency disaster response and rescue, and macro decision-making etc. However, the intrinsic characteristics of complexity and spatial-temporal diversity, multi-scale features and heterogeneity brought from various means of data acquisition make the integration of multi-source data with high-efficiency becomes an international challenge nowadays. In this paper, the design and implementation of a vehicle-borne platform based on Internet of Things for environmental monitoring has been achieved. And then, by merging and matching environmental data and spatial data, more intensive multi-source environmental parameters and information can be obtained to act as meaningful supplementation of fixed environmental monitoring stations. The research of this paper is conductive to the transition of environmental monitoring from static methods to dynamic methods and from a small amount of data-based empirical model to sensor network-based quantitative model. Mobile environmental monitoring platform integrating with multiple sensors that can make environmental monitoring more timely, dynamic, integrated and intelligent will be the beneficial attempt and the development trend.

Supporting Natural Hazards Management With Geospatial Technologies

2019 ◽  
Author(s):  
Diana Mitsova
Keyword(s):  
Decision Making ◽  
Real Time ◽  
Natural Hazards ◽  
Spatial Data ◽  
Disaster Response ◽  
Early Warning Systems ◽  
Unmanned Aircraft ◽  
Time Data ◽  
Geospatial Technologies ◽  
Collaborative Efforts

On a global scale, natural disasters continue to inflict a heavy toll on communities and to pose challenges that either persist or amplify in complexity and scale. There is a need for flexible and adaptive solutions that can bridge collaborative efforts among public agencies, private and nonprofit organizations, and communities. The ability to explore and analyze spatial data, solve problems, visualize, and communicate outcomes to support the collaborative efforts and decision-making processes of a broad range of stakeholders is critical in natural hazards and disaster management. The adoption of geospatial technologies has long been at the core of natural hazards risk assessment, linking existing technologies in GIS (geographic information system) with spatial analytical techniques and modeling. Practice and research have shown that though risk-reduction strategies and the mobilization of disaster-response resources depend on integrating governance into the process of building disaster resilience, the implementation of such strategies is best informed by accurate spatial data acquisition, fast processing, analysis, and integration with other informational resources. In recent years, new and accessible sources and types of data have greatly enhanced the ability of practitioners and researchers to develop approaches that support rapid and efficient disaster response, including forecasting, early warning systems, and damage assessments. Innovations in geospatial technologies, including remote sensing, real-time Web applications, and distributed Web-based GIS services, feature platforms for systematizing and sharing data, maps, applications, and analytics. Distributed GIS offers enormous opportunities to strengthen collaboration and improve communication and efficiency by enabling agencies and end users to connect and interact with remotely located information products, apps, and services. Newer developments in geospatial technologies include real-time data management and unmanned aircraft systems (UAS), which help organizations make rapid assessments and facilitate the decision-making process in disasters.

A Distance-Window Approach for the Continuous Processing of Spatial Data Streams

2020 ◽  
Vol 11 (2) ◽  
pp. 16-30
Author(s):  
Salman Ahmed Shaikh ◽  
Akiyoshi Matono ◽  
Kyoung-Sook Kim
Keyword(s):  
Real Time ◽  
Data Streams ◽  
Spatial Data ◽  
Moving Objects ◽  
Smart Cities ◽  
Continuous Processing ◽  
Law And Order ◽  
Essential Requirement ◽  
Stream Processing Engines ◽  
Window Approach

Real-time and continuous processing of citywide spatial data is an essential requirement of smart cities to guarantee the delivery of basic life necessities to its residents and to maintain law and order. To support real-time continuous processing of data streams, continuous queries (CQs) are used. CQs utilize windows to split the unbounded data streams into finite sets or windows. Existing stream processing engines either support time-based or count-based windows. However, these are not much useful for the spatial streams containing the trajectories of moving objects. Hence, this paper presents a distance-window based approach for the processing of spatial data streams, where the unbounded streams can be split with respect to the trajectory length. Since the window operation involves repeated computation, this work presents two incremental distance-based window approaches to avoid the repetition. A detailed experimental evaluation is presented to prove the effectiveness of the proposed incremental distance-based windows.

Unbounded Spatial Data Stream Query Processing using Spatial Semijoins

2021 ◽  
Vol 15 (02) ◽  
pp. 33-41
Author(s):  
Wendy Osborn
Keyword(s):  
Query Processing ◽  
Data Streams ◽  
Spatial Data ◽  
Data Stream ◽  
Spatial Extent ◽  
Common Region ◽  
Data Set ◽  
Spatial Join ◽  
The Common ◽  
Spatial Data Stream

In this paper, the problem of query processing in spatial data streams is explored, with a focus on the spatial join operation. Although the spatial join has been utilized in many proposed centralized and distributed query processing strategies, for its application to spatial data streams the spatial join operation has received very little attention. One identified limitation with existing strategies is that a bounded region of space (i.e., spatial extent) from which the spatial objects are generated needs to be known in advance. However, this information may not be available. Therefore, two strategies for spatial data stream join processing are proposed where the spatial extent of the spatial object stream is not required to be known in advance. Both strategies estimate the common region that is shared by two or more spatial data streams in order to process the spatial join. An evaluation of both strategies includes a comparison with a recently proposed approach in which the spatial extent of the data set is known. Experimental results show that one of the strategies performs very well at estimating the common region of space using only incoming objects on the spatial data streams. Other limitations of this work are also identified.

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