QoS management in real-time spatial big data using a feedback control scheduling

Real-time spatial data has become a cornerstone for modernizing asset management and operations in the oil and gas industry. By integrating Geographic Information Systems (GIS) with advanced technologies such as Internet of Things (IoT), Remote Sensing, and Supervisory Control and Data Acquisition (SCADA), the industry achieves enhanced decision-making, operational efficiency, and safety. This paper examines the theoretical foundations of realtime spatial data applications in upstream, midstream, and downstream sectors, supported by case studies, diagrams, and tabular data. Challenges like data integration, security, and cost are addressed alongside future prospects for adopting innovative technologies.

The Health Care Network (HCN ) related information of a country is one of the most influential fields of data sharing and integrating with disparate data sources of physical world and compatible health care organizations on the exponentially growing World Wide Web (WWW ) for improving and maintaining health related services of mass people. Although these accessible HCN related unstructured or semi-structured data in multiple heterogeneous repositories on the web originate challenges to develop a smarter method in finding specific information using traditional keyword-based search engines. Moreover, it is also a formidable task to harmonize spatial data with our HCN for exploring implicit knowledge that assist people in making their decisions regarding of health services and it is getting researchers' attention at a rapid pace. In this regard, we applied the Physical-Cyber-Social (PCS) computing approach on HCN that demonstrates the feasibility of semantic web technologies to represent domain information in the machine understandable format and we also integrate data from accessible Cyber Space Data Sources (CSDSs) to address these challenges. We published our HCN related data using Resource Description Framework (RDF ) to build a knowledge-base. We call this repository as HCN - BD, which is HCN related machine understandable data source of Bangladesh. This yields a huge knowledge graph with 0.25 million nodes, i.e. RDF triples to facilitate reasoning important information pattern by a graph based query language, SPARQL. Furthermore, we integrate this semantic repository with our generic spatial knowledge-base, Geo - Bangladesh, a machine understandable geographic data source related to the administrative structure of Bangladesh, to examine health care locations using SPARQL and therefore, we achieved the semantic interoperability in our Linked Open Data (LOD) application. Moreover, our research effectively addresses nearest health care centers using real time spatial data from Global Positioning System (GPS) and maps these locality to increase searching ability by mass people.

Dynamic Alignment Control Using Depth Imagery for Automated Wheel Assembly

Abstract. This paper presents the first mobile X-band dual-polarization Doppler weather radar termed MXPOL operated by the Laboratory of Hydrometeorology (LABHIDRO) of the University of São Paulo, São Paulo, Brazil. It is used in graduate and under graduate courses, real-time monitoring and nowcasting of severe weather in the Metropolitan Area of São Paulo (MASP). It is one of the first of its kind to be used operationally to provide real-time high spatial resolution polarimetric data. MXPOL is an important component of a Hydrometeorological Forecast System (Pereira Filho et al., 2005) for MASP. This manuscript presents some instances of MXPOL polarimetric measurements of weather systems and their respective microphysical, dynamical and boundary layer features that can improve nowcasting.

The continued expansion of offshore wind as a global energy technology represents a significant expansion of infrastructure into a range of coastal and oceanic regions. Effective design, operation and understanding physical impacts of turbines benefit from a detailed understanding of the wave conditions. In order to cover the spatial extent of offshore wind farms and to ensure high quality data, some combination of in-situ measurements and phase averaging wave modelling are commonly applied. These are used for monitoring current conditions and for short term forecasts that govern crucial operational decisions. Inaccuracies in this process lead to vessels missing suitable conditions to carry out an operation, or operations being aborted due to unsafe conditions. Both of these outcomes, cost money or affect safety.This work reviews recent progress in using machine learning to develop surrogate wave modelling that can offer real-time spatial wave data leveraging a combination of in-situ measurements and model hindcasts, but without relying on continuous processing from traditional wave models. The outcomes show an improvement in accuracy of real-time wave predictions when compared to regional wave modelling, available at a fraction of the computational cost. This highlights the potential of this approach to change how wave data is provided for operational purposes, with immediate potential for reduced costs and improved safety for vessels working at offshore wind farms. The results also highlight the ongoing potential for research and development of surrogate models as part of the future of numerical wave modelling.

Spatial big data architecture: From Data Warehouses and Data Lakes to the LakeHouse

A project titled “Establishing the advanced disaster reduction management system by fusion of real-time disaster simulation and big data assimilation,” was launched as Core Research for Evolutional Science and Technology (CREST) by the Japan Science and Technology Agency (JST). Intended to save as many lives as possible in future national crises involving earthquake and tsunami disasters, the project works on a disaster mitigation system of the big data era, based on cooperation of large-scale, high-resolution, real-time numerical simulations and assimilation of real-time observation data. The world’s most advanced specialists in disaster simulation, disaster management, mathematical science, and information science work together to create the world’s first analysis platform for real-time simulation and big data that effectively processes, analyzes, and assimilates data obtained through various observations. Based on quantitative data, the platform designs proactive measures and supports disaster operations immediately after disaster occurrence. The project was launched in 2014 and is working on the following issues at present.Sophistication and fusion of simulations and damage prediction models using observational big data: Development of a real-time simulation core system that predicts the time evolution of disaster effect by assimilating of location information, fire information, and building collapse information which are obtained from mobile terminals, satellite images, aerial images, and other new observation data in addition to sensing data obtained by the undersea high-density seismic observation network.Latent structure analysis and major disaster scenario creation based on a huge amount of simulation results: Development of an analysis and extraction method for the latent structure of a huge amount of disaster scenarios generated by simulation, and creation of severe scenarios with minimum “unexpectedness” by controlling disaster scenario explosion (an explosive increase in the number of predicted scenarios).Establishment of an earthquake and tsunami disaster mitigation big data analysis platform: Development of an earthquake and tsunami disaster mitigation big data analysis platform that realizes analyses of a huge number of disaster scenarios and increases in speed of data assimilation, and clarifies the requirements for operation of the platform as a disaster mitigation system.The project was launched in 2014 as a 5-year project. It consists of element technology development and system fusion, feasibility study as a next-generation disaster mitigation system (validation with/without introduction of the developed real-time simulation and big data analysis platform) in the affected areas of the Great East Japan Earthquake, and test operations in affected areas of the Tokyo metropolitan earthquake and the Nankai Trough earthquake.

Twitter has withstood the test of time as a successful social networking platform. In many circles globally, the majority of users choose Twitter when choosing a social media outlet for reliable scientific information and news. However, the Twitter application programming interface (API) limitations do not allow for low-cost data science options for academia. It becomes very expensive for academic researchers to gain the full potential of data analytics available from Twitter using a free API account. In this article, we present our big data analytics platform developed at our DaTALab at Lakehead University, Canada, that allows users to focus on their Twitter search criteria and gain access to large amounts of Twitter data at the touch of a button. The platform supports the collection of social media data and applies many filters for cleaning and further use for machine learning (ML) and artificial intelligence (AI)-based systems. Our focus has been primarily on healthcare-related research, which shows the strength of the presented platform. However, the platform itself is malleable to any topic of interest. Data collected and processed are suitable for further AI/ML analysis. We present our platform using a specific healthcare search topic to emphasize the power of our system for future research endeavors in the healthcare field.

Integrating single-cell and spatial multi-omics data is essential for resolving cellular identity, regulatory programs and tissue organization, yet remains challenging under realistic experimental designs. In practice, data are often incomplete, unpaired and partially overlapping across batches and platforms, resulting in mosaic settings where missingness, batch effects and limited correspondence are tightly coupled. Existing methods typically rely on shared cells, explicit anchors or post hoc mapping, and can become unstable as overlap diminishes or spatial structure is not explicitly modeled. Here we present GAMMI (Graph-guided Adversarial Mosaic Multi-omics Integration), a unified graph learning framework for mosaic integration of heterogeneous single-cell and spatial multi-omics data. Rather than performing direct cell or sample alignment, GAMMI learns biologically meaningful relational structure by jointly embedding cells and molecular features in a shared latent space using heterogeneous graphs that encode cell-feature, feature-feature and spatial adjacency relationships. An edge-based contrastive objective with missingness-aware negative sampling avoids false-negative supervision under unobserved modalities, while adversarial domain adaptation suppresses batch-associated variation at the embedding level. Spatial data are incorporated as structurally informative constraints during learning, enabling systematic enrichment of molecular representations across spatial locations. Across diverse mosaic single-cell benchmarks and spatial datasets, GAMMI consistently outperforms state-of-the-art methods in biological conservation, batch correction and spatial reconstruction, including in low-overlap and fully unpaired regimes.

To deal with the bottle-neck of enterprise distributed multi-source heterogeneous geographic spatial data's interoperating, the four-tier integration framework of multi-source heterogeneous spatial information based on web services is designed. Through the integration of web services technology, the geographical information service criterion of OGC and the scalable vector graphics model (SVG) of W3C, the integrated device of spatial data translating services is constructed, which can realize the heterogeneous spatial data's interoperating and showing by spanning the system's heterogeneous and providing a kind of transparent loosely coupling relation between the users and the servers, service modules can be called through job scheduling services, lastly multi-source heterogeneous geographic spatial information can be integrated and remotely computed. It is primarily designed and applied in the State Earthquake Computing Application System (SECAS) based on the above framework and .NET platform, it realities multi-source heterogeneous geographic spatial data's issuing and remote computing.

Taking a byte out of big data

<p><strong>Background</strong></p> <p>Despite having the tools at our disposal to enable an adequate food supply for all people, inequities in food acquisition, distribution, and most importantly, food sovereignty, worsen food insecurity. The detrimental impact of climate change on food systems and mental health is further exacerbated by a lack of food sovereignty. We urgently require innovative solutions to enable food sovereignty, minimize food insecurity, and address climate change–related mental distress (ie, solastalgia). Indigenous communities have a wealth of Traditional Knowledge for climate change adaptation and preparedness to strengthen food systems. Traditional Knowledge combined with Western methods can revolutionize ethical data collection, engagement, and knowledge mobilization.</p> <p><strong>Objective</strong></p> <p>The Food Equity and Environmental Data Sovereignty (FEEDS) Project takes a participatory action, citizen science approach for early detection and warning of climate change impacts on food sovereignty, food security, and solastalgia. The aim of this project is to develop and implement a sustainable digital platform that enables real-time decision-making to mitigate climate change–related impacts on food systems and mental well-being.</p> <p><strong>Methods</strong></p> <p>Citizen science enables citizens to actively contribute to all aspects of the research process. The FEEDS Project is being implemented in five phases: participatory project planning, digital climate change platform customization, community-led evaluation, digital platform and project refinement, and integrated knowledge translation. The project is governed by a Citizen Scientist Advisory Council comprising Elders, Traditional Knowledge Keepers, key community decision makers, youth, and FEEDS Project researchers. The Council governs all phases of the project, including coconceptualizing a climate change platform, which consists of a smartphone app and a digital decision-making dashboard. Apart from capturing environmental and health-related big data (eg, weather, permafrost degradation, fire hazards, and human movement), the custom-built app uses artificial intelligence to engage and enable citizens to report on environmental hazards, changes in biodiversity or wildlife, and related food and mental health issues in their communities. The app provides citizens with valuable information to mitigate health-related risks and relays big data in real time to a digital dashboard.</p> <p><strong>Results</strong></p> <p>This project is currently in phase 1, with the subarctic Métis jurisdiction of Île-à-la-Crosse, Saskatchewan, Canada.</p> <p><strong>Conclusions</strong></p> <p>The FEEDS Project facilitates Indigenous Peoples’ self-determination, governance, and data sovereignty. All citizen data are anonymous and encrypted, and communities have ownership, access, control, and possession of their data. The digital dashboard system provides decision makers with real-time data, thereby increasing the capacity to self-govern. The participatory action research approach, combined with digital citizen science, advances the cocreation of knowledge and multidisciplinary collaboration in the digital age. Given the urgency of climate change, leveraging technology provides communities with tools to respond to existing and emerging crises in a timely manner, as well as scientific evidence regarding the urgency of current health and environmental issues.</p>

In today’s big data era, cleaning big data streams has become a challenging task because of the different formats of big data and the massive amount of big data which is being generated. Many studies have proposed different techniques to overcome these challenges, such as cleaning big data in real time. This systematic literature review presents recently developed techniques that have been used for the cleaning process and for each data cleaning issue. Following the PRISMA framework, four databases are searched, namely IEEE Xplore, ACM Library, Scopus, and Science Direct, to select relevant studies. After selecting the relevant studies, we identify the techniques that have been utilized to clean big data streams and the evaluation methods that have been used to examine their efficiency. Also, we define the cleaning issues that may appear during the cleaning process, namely missing values, duplicated data, outliers, and irrelevant data. Based on our study, the future directions of cleaning big data streams are identified.

Big data refers to data so large, varied and generated at such an alarming rate that is too challenging for the conventional methods, tools, and technologies to handle it. Generating value out of it through analytics has started gaining paramount importance. Advanced analytics in the form of predictive and prescriptive analytics can scour through big data in real time or near real time to create valuable insights, which facilitate an organization in strategic decision making. The purpose of this paper is to review the emerging areas of big data and analytics, and is organized in two phases. The first phase covers taxonomy for classifying big data analytics (BDA), the big data value chain, and comparison of various platforms for BDA. The second phase discusses scope of research in BDA and some related work followed by a research proposal for developing a contextual model for BDA using advanced analytics.

Recent technologies such as Big Data could be used in order to help the improvement of other fields except for the telecommunication field. Due to the Health field, the Big Data technology could contribute with the aim to help for the purpose of analysis and management of the huge amounts of health data. The main objective of this research proposal is an analytical study of the technologies IoT, Cloud Computing (CC) and large-scale data (Big Data) to resolve various issues facing the health sector in relation to these technologies. The purpose of the research proposal is the collection of medical (e-health) big data in real time. The collection will be performed by sensor devices and actuators, which will wear patients who suffer from various ailments. Then is following, the transfer of these data through a network to a cloud server. Additionally, these data will be processed in the cloud which makes its analysis so as to become meaningless. By the analysis of these data is done the data mining. Finally, to address the various problems in the health sector, the transfer of the analyzed health data will be held by the devices of the relevant persons. Also, in our study, we will deal with the security of medical data which constitute personal data and must be protected.