scholarly journals AN INTERACTIVE PLATFORM FOR ENVIRONMENTAL SENSORS DATA ANALYSES

2021 ◽  
Vol VIII-4/W1-2021 ◽  
pp. 57-64
Author(s):  
S. Harbola ◽  
V. Coors
Keyword(s):  
Visual Analytics ◽  
Urban System ◽  
Environmental Data ◽  
Human Interaction ◽  
Spatiotemporal Data ◽  
Improve Energy Efficiency ◽  
Depth Sensors ◽  
Environmental Monitoring System ◽  
The Future ◽  
Pm2.5 And Pm10

Abstract. The increased usage of the environmental monitoring system and sensors, installed on a day-to-day basis to explore information and monitor the cities’ environment and pollution conditions, are in demand. Sensor networking advancement with quality and quantity of environmental data has given rise to increasing techniques and methodologies supporting spatiotemporal data interactive visualisation analyses. Moreover, Visualisation (Vis) and Visual Analytics (VA) of spatiotemporal data have become essential for research, policymakers, and industries to improve energy efficiency, environmental management, and cities’ air pollution planning. A platform covering Vis and VA of spatiotemporal data collected from a city helps to portray such techniques’ potential in exploring crucial environmental inside, which is still required. Therefore, this work presents Vis and VA interface for the spatiotemporal data represented in terms of location, including time, and several measured attributes like Particular Matter (PM) PM2.5 and PM10, along with humidity, and wind (speed and direction) to assess the detailed temporal patterns of these parameters in Stuttgart, Germany. The time series are analysed using the unsupervised HDBSCAN clustering on a series of (above mentioned) parameters. Furthermore, with the in-depth sensors nature understanding and trends, Machine Learning (ML) approach called Transformers Network predictor model is integrated, that takes successive time values of parameters as input with sensors’ locations and predict the future dominant (highly measured) values with location in time as the output. The selected parameters variations are compared and analysed in the spatiotemporal frame to provide detailed estimations on how average conditions would change in a region over the time. This work would help to get a better insight into the urban system and enable the sustainable development of cities by improving human interaction with the spatiotemporal data. Hence, the increasing environmental problems for big industrial cities could be alarmed and reduced for the future with proposed work.

scholarly journals GEO-VISUALISATION AND VISUAL ANALYTICS FOR SMART CITIES: A SURVEY

2018 ◽  
Vol XLII-4/W11 ◽  
pp. 11-18
Author(s):  
S. Harbola ◽  
V. Coors
Keyword(s):  
Public Administration ◽  
Spatial Data ◽  
Smart City ◽  
Visual Analytics ◽  
Smart Cities ◽  
Urban System ◽  
Human Interaction ◽  
White Spots ◽  
Data Requirements ◽  
Insight Into

<p><strong>Abstract.</strong> Geo-Visualisation (GV) and Visual Analytics (VA) of geo-spatial data have become a focus of interest for research, industries, government and other organisations for improving the mobility, energy efficiency, waste management and public administration of a smart city. The geo-spatial data requirements, increasing volumes, varying formats and quality standards, present challenges in managing, storing, visualising and analysing the data. A survey covering GV and VA of the geo-spatial data collected from a smart city helps to portray the potential of such techniques, which is still required. Therefore, this survey presents GV and VA techniques for the geo-spatial urban data represented in terms of location, multi-dimensions including time, and several other attributes. Further, the current study provides a comprehensive review of the existing literature related to GV and VA from cities, highlighting the important open white spots for the cities’ geo-spatial data handling in term of visualisation and analytics. This will aid to get a better insight into the urban system and enable sustainable development of the future cities by improving human interaction with the geo-spatial data.</p>

scholarly journals An Ontology-based Visual Analytics for Apple Variety Testing

2021 ◽  
Author(s):  
Ekaterina Chuprikova ◽  
Abraham Mejia Aguilar ◽  
Roberto Monsorno
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Data Integration ◽  
Visual Analytics ◽  
Agricultural Sector ◽  
Environmental Data ◽  
Data Sources ◽  
Apple Variety ◽  
Testing Program ◽  
Variety Testing

&lt;p&gt;Increasing agricultural production challenges, such as climate change, environmental concerns, energy demands, and growing expectations from consumers triggered the necessity for innovation using data-driven approaches such as visual analytics. Although the visual analytics concept was introduced more than a decade ago, the latest developments in the data mining capacities made it possible to fully exploit the potential of this approach and gain insights into high complexity datasets (multi-source, multi-scale, and different stages).&amp;#160;The current study focuses on developing prototypical visual analytics for an apple variety testing program in South Tyrol, Italy. Thus, the work aims (1) to establish a visual analytics interface enabled to integrate and harmonize information about apple variety testing and its interaction with climate by designing a semantic model; and (2) to create a single visual analytics user interface that can turn the data into knowledge for domain experts.&amp;#160;&lt;/p&gt;&lt;p&gt;This study extends the visual analytics approach with a structural way of data organization&amp;#160;(ontologies), data mining, and visualization techniques to retrieve knowledge from an extensive collection of apple variety testing program and environmental data. The prototype stands on three main components: ontology, data analysis, and data visualization. Ontologies provide a representation of expert knowledge and create standard concepts for data integration, opening the possibility to share the knowledge using a unified terminology and allowing for inference. Building upon relevant semantic models (e.g., agri-food experiment ontology, plant trait ontology, GeoSPARQL), we propose to extend them based on the apple variety testing and climate data. Data integration and harmonization through developing an ontology-based model provides a framework for integrating relevant concepts and relationships between them, data sources from different repositories, and defining a precise specification for the knowledge retrieval. Besides, as the variety testing is performed on different locations, the geospatial component can enrich the analysis with spatial properties. Furthermore, the visual narratives designed within this study will give a better-integrated view of data entities' relations and the meaningful patterns and clustering based on semantic concepts.&lt;/p&gt;&lt;p&gt;Therefore, the proposed approach is designed to improve decision-making about variety management through an interactive visual analytics system that can answer &quot;what&quot; and &quot;why&quot; about fruit-growing activities. Thus, the prototype has the potential to go beyond the traditional ways of organizing data by creating an advanced information system enabled to manage heterogeneous data sources and to provide a framework for more collaborative scientific data analysis. This study unites various interdisciplinary aspects and, in particular: Big Data analytics in the agricultural sector and visual methods; thus, the findings will contribute to the EU priority program in digital transformation in the European agricultural sector.&lt;/p&gt;&lt;p&gt;This project has received funding from the European Union's Horizon 2020 research and innovation program under the Marie Sk&amp;#322;odowska-Curie grant agreement No 894215.&lt;/p&gt;

A Visual Analytics Framework for Big Spatiotemporal Data

2018 ◽  
Author(s):  
Shaohua Wang ◽  
Ershun Zhong ◽  
Wenwen Cai ◽  
Qiang Zhou ◽  
Hao Lu ◽  
...  
Keyword(s):  
Visual Analytics ◽  
Spatiotemporal Data

scholarly journals Automatic Recognition of Human Interaction via Hybrid Descriptors and Maximum Entropy Markov Model Using Depth Sensors

Entropy ◽  
2020 ◽  
Vol 22 (8) ◽  
pp. 817 ◽  
Author(s):  
Ahmad Jalal ◽  
Nida Khalid ◽  
Kibum Kim
Keyword(s):  
Markov Model ◽  
Maximum Entropy ◽  
Social Activity ◽  
Gaussian Mixture ◽  
Features Extraction ◽  
Human Interaction ◽  
Cross Entropy ◽  
Automatic Identification ◽  
Depth Sensors ◽  
Entropy Optimization

Automatic identification of human interaction is a challenging task especially in dynamic environments with cluttered backgrounds from video sequences. Advancements in computer vision sensor technologies provide powerful effects in human interaction recognition (HIR) during routine daily life. In this paper, we propose a novel features extraction method which incorporates robust entropy optimization and an efficient Maximum Entropy Markov Model (MEMM) for HIR via multiple vision sensors. The main objectives of proposed methodology are: (1) to propose a hybrid of four novel features—i.e., spatio-temporal features, energy-based features, shape based angular and geometric features—and a motion-orthogonal histogram of oriented gradient (MO-HOG); (2) to encode hybrid feature descriptors using a codebook, a Gaussian mixture model (GMM) and fisher encoding; (3) to optimize the encoded feature using a cross entropy optimization function; (4) to apply a MEMM classification algorithm to examine empirical expectations and highest entropy, which measure pattern variances to achieve outperformed HIR accuracy results. Our system is tested over three well-known datasets: SBU Kinect interaction; UoL 3D social activity; UT-interaction datasets. Through wide experimentations, the proposed features extraction algorithm, along with cross entropy optimization, has achieved the average accuracy rate of 91.25% with SBU, 90.4% with UoL and 87.4% with UT-Interaction datasets. The proposed HIR system will be applicable to a wide variety of man–machine interfaces, such as public-place surveillance, future medical applications, virtual reality, fitness exercises and 3D interactive gaming.

Data Transmission System Design for Environmental Monitoring System Based on 3G Network

2013 ◽  
Vol 846-847 ◽  
pp. 831-835
Author(s):  
Shi Yue Sheng ◽  
Jian Yi ◽  
Qing Yuan Zhu
Keyword(s):  
Environmental Monitoring ◽  
Mobile Communications ◽  
Data Transmission ◽  
Transmission System ◽  
Environmental Data ◽  
Monitoring Data ◽  
Server Side ◽  
Environmental Monitoring System ◽  
Data Transmission System ◽  
Environmental Monitoring Data

As the accelerated development of the existing mobile communications and Internet integration, high-speed mobile access and Internet Protocol-based service become mature for different kinds of applications, which provide a great convenience for the remote wireless monitoring, mobile data transmission and so on. In this paper, an environmental monitoring data transmission system based on 3G networks is designed to transmit environmental data which is collected through sensors to server-side. The data transmission system is functioning with remote data transmission, and monitoring data reviewing at remote terminal any time. Scientific monitoring data can accurately, timely and comprehensively reflect of the various environmental parameters on-site detection status. The system components of environment monitoring platform based on virtual instrument is introduced firstly. Then, the network transmission system scheme based on Socket communication is proposed. Finally, the data transmission between LabVIEW application of monitoring terminal and PHP application of server-side through Socket interface is achieved. This system transmits well, and queries conveniently. It is safe and timely while being applied in environmental monitoring data transmission.

scholarly journals Advancing User-Centered Evaluation of Visual Analytic Environments through Contests

2009 ◽  
Vol 8 (3) ◽  
pp. 230-238 ◽  
Author(s):  
Loura Costello ◽  
Georges Grinstein ◽  
Catherine Plaisant ◽  
Jean Scholtz
Keyword(s):  
Visual Analytics ◽  
Science And Technology ◽  
End Users ◽  
Lessons Learned ◽  
Time Constraints ◽  
Future Directions ◽  
The Future ◽  
User Centered

In this paper, the authors describe the Visual Analytics Science and Technology (VAST) Symposium contests run in 2006 and 2007 and the VAST 2008 and 2009 challenges. These contests were designed to provide researchers with a better understanding of the tasks and data that face potential end users. Access to these end users is limited because of time constraints and the classified nature of the tasks and data. In that respect, the contests serve as an intermediary, with the metrics and feedback serving as measures of utility to the end users. The authors summarize the lessons learned and the future directions for VAST Challenges.

scholarly journals ORGANIZATION OF ENVIRONMENTAL MONITORING IN THE CASPIAN SEA

ScienceRise ◽  
2020 ◽  
Vol 2 ◽  
pp. 3-9
Author(s):  
Nazila Ali Ragimova ◽  
Vugar Hajimahmud Abdullayev ◽  
Vasila Soltanaga Abbasova
Keyword(s):  
Environmental Monitoring ◽  
Monitoring System ◽  
Caspian Sea ◽  
Ecological Monitoring ◽  
Water Area ◽  
Environmental Data ◽  
The Caspian Sea ◽  
Environmental Situation ◽  
Environmental Monitoring System ◽  
Technological Product

The object of research is ecological monitoring of the Caspian Sea. This article addressed the objectives and components of environmental monitoring. It also describes the objectives for the establishment of a Unified State Environmental Monitoring System. Special attention is paid to the structure of the environmental network monitoring system, which consists of three levels: low, medium and high. One of the main problems is the establishment of the Unified State Environmental Monitoring System of the Caspian Sea. This article considered the main functions and objectives of the Unified State Environmental Monitoring System. Here are also discussed the computing center of the environmental monitoring system and its functions and components. The research used three main components for environmental data processing: database management systems, geographic information system and integrated software packages. Examples of a computer system of environmental monitoring include: ArcGIS, MapInfo, ArcView and OCEAN. The main scientific results of this research are the main functions, objectives and components of environmental monitoring of the Caspian Sea to reduce pollution levels. The obtained results can be used to optimize the characteristics of environmental information systems, which are used to organize environmental monitoring. Innovative technological product of this research is the development of an algorithm for the organization of environmental monitoring of the Caspian Sea. It will allow ecologists to monitor the environmental situation of the Caspian Sea and further improve it. The obtained innovative technological product will be useful for carrying out environmental monitoring of the most contaminated section of the water basin, and more precisely for monitoring the scale of pollution and further improving the environmental situation of the water area.

scholarly journals Maintaining Access to Public Data: Lessons from Data Refuge

2018 ◽  
Author(s):  
Margaret Miller Janz
Keyword(s):  
Lessons Learned ◽  
Environmental Data ◽  
Environmental Humanities ◽  
Public Data ◽  
The Future ◽  
Government Data ◽  
Limit Access

The Data Refuge project began in December 2016 after fellows in the Penn Program for Environmental Humanities (PPEH) grew concerned about how the incoming presidential administration might find ways to limit access to federal climate and environmental data. This article describes the history and precedence for this project, the lessons learned through our activities, and some of the future en devours undertaken by stakeholders in the landscape of preserving and managing government data. Originally published in Against the Grain, v29 (6), Dec. 2017-Jan. 2018, http://www.against-the-grain.com/2018/03/v29-6-maintaining-access-to-public-data-lessons-from-data-refuge/.

scholarly journals A Survey on IoT related Security Issues, Challenges And their Solutions

2020 ◽  
pp. 378-383
Keyword(s):  
Internet Of Things ◽  
Smart Cities ◽  
Smart Homes ◽  
Human Interaction ◽  
Security And Privacy ◽  
The Internet ◽  
Security Issues ◽  
High Interest ◽  
The Future ◽  
The Internet Of Things

The future of Internet of Things (IoT) is already upon us. The Internet of Things (IoT) is the ability to provide everyday devices with a way of identification and another way for communication with each other. The spectrum of IoT application domains is very large including smart homes, smart cities, wearables, e-health, etc. Consequently, tens and even hundreds of billions of devices will be connected. Such devices will have smart capabilities to collect, analyze and even make decisions without any human interaction. Security is a supreme requirement in such circumstances, and in particular authentication is of high interest given the damage that could happen from a malicious unauthenticated device in an IoT system. While enjoying the convenience and efficiency that IoT brings to us, new threats from IoT also have emerged. There are increasing research works to ease these threats, but many problems remain open. To better understand the essential reasons of new threats and the challenges in current research, this survey first proposes the concept of “IoT features”. Then, the security and privacy effects of eight IoT new features were discussed including the threats they cause, existing solutions and challenges yet to be solved.

Building a Competitive Intelligence System

2011 ◽  
pp. 228-247 ◽  
Author(s):  
Egbert Philips
Keyword(s):  
Human Resources ◽  
Design Process ◽  
Development Process ◽  
Environmental Data ◽  
Competitive Intelligence ◽  
Successful Implementation ◽  
Intelligence System ◽  
The Future ◽  
Participative Design ◽  
Competitive Intelligence System

Competitive intelligence is understood as the process of acquiring environmental data and transforming them into strategic relevant intelligence. To realize the activities in the four stages of the intelligence process (directing, collecting, analyzing and distributing), a so-called intelligence infrastructure is needed. This infrastructure consists of all the requirements (division of tasks and responsibilities, human resources, and ICT) to perform the intelligence activities. In this chapter we propose an infrastructural approach to designing and implementing a competitive intelligence system. In the infrastructural approach, it is acknowledged that ICT solutions are only a part of the total infrastructure, realizing the CI activities. Moreover, the infrastructural approach is characterized by a specific view on the development process. The different elements of the infrastructure are simultaneously developed and the design of the process is actually executed by the future users. The goal of this participative design process is to create user commitment by taking the interests and needs of the potential users into account. This commitment is supposed to be a necessary prerequisite for a successful implementation of a CI infrastructure. In this chapter, a case is described to illuminate how an infrastructural approach with respect to CI works.

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