MagHD: a new approach to multi-dimensional data storage, analysis, display and exploitation

Social security of the state is the ability to guarantee and provide its citizens with safe and decent living standards. Study of the impact of the war on the state of national security, in particular human social security, will help to identify weaknesses in the state policy implementation in the political, economic and social areas. The impact of Russia's full-scale war against our country on the social security of Ukrainians requires substantiation of new, more effective mechanisms and tools for strengthening human social security in times of war. Under these circumstances, there is a need to apply new technologies to manage the socio-economic processes of the state for analysis and decision-making, and to reflect the current situation. These technologies will make it possible to predict scenarios and offer managers at all levels the best ways to achieve a balanced result. We need technologies that can effectively combine space and time with large amounts of special data in the form of attribute information about objects in the operational environment, background information about regions, climatic conditions, socio-economic conditions and processes in the region, etc. The need for prompt decision-making based on real indicators of anthropogenic, social, and environmental nature, taking into account the factors of space and time in a particular region or area of study, necessitates the involvement of specialized software platforms – geographic information systems that combine analytical capabilities with geospatial analysis. Today, a large number of general-purpose geographic information systems have been developed, but despite the variety of such software products, there is still a need to create specialized geographic information documents and databases that allow obtaining up-to-date geographic information data, the use of which leads to a real reduction in the time required to solve specialized tasks of information management systems. The analysis and decision support system in the environment of existing geographic information platforms will allow to take into account the influence of external factors, analyze the possibility of development, expansion, and monitor the state of the general environment when assessing the current state of input data, using information on the spatial distribution of the necessary resources, information on the competitive environment, etc. With the help of spatial and surface analysis modules, a GIS user can analyze spatially compatible objects and their interrelationships, taking into account the dynamics of their development. Currently, the world leader among geographic information systems is the ArcGIS products. It is a complete system that allows you to collect, organize, manage, analyze, exchange and distribute geographic data. Additionally, ArcGIS, depending on the type and specification of the platform, has other specialized modules that provide a wide range of opportunities for working with geodata and their visual display, namely: creation, management, visualization, analysis and exchange of geographic information. In addition, there are many compatible applications and extensions that integrate into the GIS platform and are focused on solving highly specialized tasks, such as assessing the state of the environment (water, soil, vegetation) or forecasting the development of natural phenomena and man-made emergencies. The article analyzes and substantiates the approaches to the use of a geographic information system as an effective tool for studying the impact of a full-scale war on human social security, improving the efficiency, completeness and quality of information and spatial support for management processes. The modern and full-featured ArcGIS system is considered as a platform that allows not only to form a geographic information system of any scale and purpose, but also to form on its basis an effective system of socio-economic monitoring, which is combined with decision support systems. The article presents an algorithm for studying the impact of a full-scale war on human social security using the ArcGIS system as an effective decision support tool with the use of spatially specialized databases.

As the research hotspot of intelligent transportation system (ITS), unmanned ground vehicle (UGV) with high intelligence is able to reduce the probability of traffic accidents and traffic congestion, and improve the efficiency of urban transportation system. Environmental perception is one of the key research topics in the research of key technologies of UGV. The campus transportation environment, as a simple version of the urban transportation environment, needs to perceive as much traffic information as the urban traffic environment. At the same time, the geographic information system (GIS) has capabilities of strong geographical information storage and management, which can play a critical role in the environmental perception of UGV. In this paper, we study the autonomous driving of unmanned ground vehicle in the campus traffic environment, and the construction method of GIS database is proposed for the perception demand of UGV in the traffic environment of campus. Then, according to the GIS database, autonomous navigation control of UGV is carried out to verify its applicability and effectiveness. After testing, the GIS database proposed in this paper can assist the autonomous driving of UGV well and complete the driving tasks in the campus environment.

Many aspects of geographic information systems (GIS) lend themselves to applications of fuzzy sets. Fuzzy databases, queries, and other areas of development have helped address problems in geographical information representation, management, query, and modeling. Cost, in a general sense, is an issue not generally considered when addressing the topic of fuzzy sets and GIS. By considering the issue of cost afresh perspective on the development of fuzzy sets in GIS applications is possible. A comparison of fuzzy versus nonfuzzy approaches shows that fuzzy logic-based approaches typically provide more detailed, useful information. As GIS becomes more complex, and intelligent, fuzzy logic-based methods will become a cost-effective approach for improving its problem-solving abilities.

Road network investment in emerging countries has not been able to keep up with demand. As a result, developing methods for managing the road network, including effective project programming to make the best possible use of limited money, has become increasingly vital. However, in the 1960s, the World Bank initiated model developments to guide investment decisions on road infrastructure in developing nations, which gained traction since most of these investments are supported by donor agencies. A significant issue in introducing these models or systems in developing countries is the lack of a comprehensive priority subsystem as a program and monetary planning tool. The essential functions of such a tool are to detect the network status at various points in time, empower requirements identification, and evaluate multiple policies on a temporary but detailed basis within the context of network management. This paper presents a practical and comprehensive system to deal with the above problem. The paper's Geographical Information and Road Asset Management System (GIRAMS) is a Web-based integrated system that provides enhanced operational efficiency for road funding agencies and their implementation partners in the proper management of the road networks and ancillary infrastructures. The subsystem, Advanced Road Asset Management (ARAMS), was developed to conform to the interface of HDM-4, RONET, and RED for Economic Analysis. In addition to these subsystems, the RAMS is enriched with analytical tools such as Asset Valuation, Budgeting, and Multi-Year Work Programming. In addition, predictive models such as Road Deterioration and Work Effects Models were also built into the ARAMS.

Nowadays a lot of massive data is stored and typically, the data itself contains a lot of non-trivial but useful information. Data mining techniques can be used to discover this information which can help the companies for decision-making. However, in real life applications, data is massive and is stored over distributed sites. One of my major research topics is to protect privacy over this kind of data. Previously, the important characteristics, issues and challenges related to management of the large amount of data has been explored. Various open source data analytics frameworks that deal with large amount of Data analytics workloads have been discussed. Comparative study between the given frameworks and suitability of the same has been proposed. Digital universe is flooded with huge amount of data generated by number of users worldwide. These data are of diverse in nature, come from various sources and in many forms. To keep with the desire to store and analyze ever larger volumes of complex data, relational databases vendors have delivered specialized analytical platforms that come in many shapes and sizes from software only to analytical services that run in third party hosted environments. In addition new technologies have emerged to address exploding volumes of complex data, including web traffic, social media content and machine generated data including sensor data, global positioning system data.Big data is defined as large amount of data which requires new technologies and architectures so that it becomes possible to extract value from it by capturing and analysis process. Due to such large size of data it becomes very difficult to perform effective analysis using the existing traditional techniques. Big data has become a prominent research field, especially when it comes to decision making and data analysis. However, Big data due to its various properties like volume, velocity, variety, variability, value and complexity put forward many challenges. Since Big data is a recent upcoming technology in the market which can bring huge benefits to the business organizations, it becomes necessary that various challenges and issues associated in bringing and adapting to this technology are brought into light. Another challenge is that data collection may not have enough accuracy which will lead to a non-consistent analysis that can critically affect the decision based on this analysis. Moreover, it is clearly apparent that organizations need to employ data-driven decision making to gain competitive advantage. Processing, integrating and interacting with more data should make it better data, providing both more panoramic and more granular views to aid strategic decision making. This is made possible via Big Data exploiting affordable and usable Computational and Storage Resources. Many offerings are based on the Map-Reduce and Hadoop paradigms and most focus solely on the analytical side. Nonetheless, in many respects it remains unclear what Big Data actually is; current offerings appear as isolated silos that are difficult to integrate and/or make it difficult to better utilize existing data and systems. Since data is growing at a huge speed making it difficult to handle such large amount of data (Exabyte). The main difficulty in handling such large amount of data is because that the volume is increasing rapidly in comparison to the computing resources. The Big data term which is being used now a days is kind of misnomer as it points out only the size of the data not putting too much of attention to its other existing properties.If data is to be used to make accurate decisions in time it becomes necessary that it should be available in accurate, complete and timely manner. This makes the data management and governance process bit complex adding the necessity to make Data open and make it available to government agencies in standardized manner with standardized APIs, metadata and formats thus leading to better decision making, business intelligence and productivity improvements.This paper presents a discussion and evaluation for the most prominent techniques used in the processes of data collection and analysis in order to identify the privacy defects in them that affects the accuracy of big data. Depending on the results of this analysis, recommendations were provided for improving data collection and analysis techniques that will help to avoid if not all then most of the problems facing the use of big data in decision making. Keywords: Big Data, Big Data Challenges, Big Data Accuracy, Big Data Collection, Big Data Analytics.

Provides an outline of research seeking to apply to computer assisted mass appraisal (CAMA) model capable of use within a geographic information system (GIS). The end product will be a working GIS/valuation integrated model. The model, in an operational context, can be utilized for property taxation purposes, to facilitate the rating and revaluation of residential properties in Northern Ireland. As the value of land and property is a function of economic, legal, physical and locational factors, consequently access to comprehensive, reliable and up‐to‐date transaction evidence is a prerequisite to property valuation. Valuation techniques depend on the collection and analysis of relevant data. Historically, the application of these techniques took place within a non‐spatial environment. Ultimately, market data support any estimate of value. Data searches and collection can prove both time consuming and expensive in relation to the fee earning potential of a valuation report. GIS can facilitate, in a spatial and aspatial context, the storage, manipulation and analysis of data, in a fraction of the time previously required. Current techniques for the mass appraisal of property, and for the prediction of residential property values, can be enhanced by utilizing the data handling capacity of GIS. Integration of a mass appraisal model within a GIS will add value to the valuation process.

Photo by Josh Riemer on Unsplash
 Introduction
 With the rapid advancements in neurotechnological machinery and improved analytical insights from machine learning in neuroscience, the availability of big brain data has increased tremendously. Neurological health research is done using digitized brain data.[1] There must be adequate data governance to secure the privacy of subjects participating in brain research and treatments. If not properly regulated, the research methods could lead to significant breaches of the subject’s autonomy and privacy. This paper will address the necessity for neuroprotection laws, which effectively govern the use of big brain data to ensure respect for patient privacy and autonomy.
 Background
 Artificial intelligence and machine learning can be integrated with neuroscience big brain data to drive research studies. This integrative technology allows patterns of electrical activity in neurons to be studied in detail.[2]Specifically, it uses a robotic system which can reason, plan, and exhibit biologically intelligent behavior. Machine learning is a method of computer programming where the code can adapt its behavior based on big brain data.[3] The big brain data is the collection of large amounts of information for the purpose of deciphering patterns through computer analysis using machine learning.[4] The information that these technologies provide is extensive enough to allow a researcher to read a patient’s mind. AI and machine learning technologies work by finding the underlying structure of brain data, which is then described by patterns known as latent factors, eventually resulting in an understanding of the brain’s temporal dynamics.[5]
 Through these technologies, researchers are able to decipher how the human brain computes its performances and thoughts. However, due to the extensive and complex nature of the data processed through AI and machine learning, researchers may gain access to personal information a patient may not wish to reveal. From a bioethical lens, tensions arise in the realm of patient autonomy. Patients are not able to control the transmission of data from their brains that is analyzed by researchers. Governing brain data through laws may enhance the extent of patient privacy in the case where brain data is being used through AI technologies.[6] A responsible approach to governing brain data would require a sophisticated legal structure.
 Analysis
 Impact on Patient Autonomy and Privacy 
 In research pertaining to big brain data, the consent forms do not fully cover the vast amounts of information that is collected. According to research, personal data has become the most sought out commodity to provide content to corporations and the web-based service industry. Unfortunately, data leaks that release private information frequently occur.[7] The storage of an individual’s data on technologies accessible on the internet during research studies makes it vulnerable to leaks, jeopardizing an individual’s privacy. These data leaks may cause the patient to be identified easily, as the degree of information provided by AI technologies are personalized and may be decoded through brain fingerprinting methods.[8]
 There has been an extensive growth in the development and use of AI. It is efficient in providing information to radiologists who diagnose various diseases including brain cancer and psychiatric disease, and AI assists in the delivery of telemedicine.[9] However, the ethical pitfall of reduced patient autonomy must be addressed by analyzing current AI technologies and creating more options for patient preference in how the data may be used. For instance, facial recognition technology[10] commonly used in health care produces more information than listed in common consent forms, threatening to undermine informed consent. Facial recognition software collects extensive data and may disclose more information than a person would prefer to provide despite being a useful tool for diagnosing medical and genetic conditions.[11] In addition, people may not be aware that their images are being used to generate more clinical data for other purposes. It is difficult to guarantee the data is anonymized. Consent requirements must include informing people about the complexity of the potential uses of the data; software developers should maximize patient privacy.[12] Furthermore, there is a “human element” in the use of AI technologies as medical providers control the use and the extent to which data is captured or accessed through the AI technologies.[13] People must understand the scope of the technology and have clear communication with the physician or health care provider about how the medical information will be used. 
 Existing Laws for Brain Data Governance 
 A strict system of defined legal responsibilities of medical providers will ensure a higher degree of patient privacy and autonomy when AI technologies and data from machine learning are used. Governing specific algorithmic data is crucial in safeguarding a patient’s privacy and developing a gold standard treatment protocol following the procurement of the information.[14] Certain AI technologies provide more data than others, and legal boundaries should be established to ensure strong performance, quality control, and scope for patient privacy and autonomy. For instance, currently AI technologies are being used in the realm of intensive neurological care. However, there is a significant level of patient uncertainty about how much control patients have over the data’s uses.[15] Calibrated legal and ethical standards will allow important brain data to be securely governed and monitored.
 Once brain signals are recorded and processed from one individual, the data may be merged with other data in Brain Computer Interface Technology (BCI).[16] To ensure a right and ability to retrieve personal data or pull it from the collection, specific regulations for varying types of data are needed.[17] The importance of consent and patient privacy must be considered through giving patients a transparent view of how brain data is governed.[18] The legal system must address discriminatory issues and risks to patients whose data is used in studies. Laws like the General Data Protection Regulation (GDPR) and the California Consumer Privacy Protection Act (CCPA) can serve as effective models to protect aggregated data. These laws govern consumer information and ensure the compliance when personal data is collected.[19] California voters recently approved expansion of the CCPA to health data. The Washington Privacy Act, which would have provided rights to access, change, and withdraw personal data, failed to pass. Other states should improve privacy as well,[20] although a federal bill would be preferable. Scientists at the Heidelberg Academy of Sciences argue for data security to be governed in a manner that balances patient privacy and autonomy with the commercial interests of researchers.[21] The balance could be achieved through privacy protections like those in the Washington Privacy Act. Although the Health Insurance Portability and Accountability Act (HIPAA) provides an overall framework to deter the likelihood of dangers to patient protection and privacy, more thorough laws are warranted to combat pervasive data transfer and analysis that technology has brought to the health care industry.[22] Breaches of patient privacy under current HIPAA regulations include releasing patient information to a reporter without their consent and sending HIV data to a patient’s employer without consent.[23] HIPAA does not cover information being shared with outside contractors who do not have an agreement with technology companies to keep patient data confidential. HIPAA regulations also do not always address blatant breaches on patient data confidentiality.[24] Patients must be provided with methods to monitor the data being analyzed to be able to view the extent of private information being generated via AI technologies. In health research, the medical purposes of better diagnosis, earlier detection of diseases, or prevention are ethical justifications for the use of the data if it was collected with permission, the person understood and approved the uses of the data, and the data was deidentified.
 A standard governance framework is required in providing the fairest system of care to patients who allow their brain data to be examined. Informed consent in the neuroscience field could reaffirm the privacy and autonomy of patients by ensuring that they understand the type of information collected. Laws also could protect data after a patient’s death. Malpractice in the scope of brain data could give people a cause of action critical in safeguarding patient’s rights. Data breach lawsuits will become common but generally do not cover deidentified data that becomes part of big data collection. A more synchronized approach to the collection and consent process will encourage an understanding of how big data is used to diagnose and treat patients. Some altruistic people may even be more likely to consent if they know the largescale data collection is helpful to treat and diagnose people. Others should have the ability to opt out of sharing neurological data, especially when there is not certainty surrounding deidentification.[25]
 Conclusion
 Artificial intelligence and machine learning technologies have the potential to aid in the diagnosis and treatment of people globally by extracting and aggregating brain data specific to individuals. However, the secure use of the data is necessary to build trust between care providers and patients, as well as in balancing the bioethical principles of beneficence and patient autonomy. We must ensure the highest quality of care to patients, while protecting their privacy, informed consent, and clinical trust. More sophis

Location‐only and use‐availability data: analysis methods converge

GIS (Geographic Information Systems) data showcase locations of earth observations or features, their associated attributes and spatial relationships that exist between such observations. Analysis of GIS data varies widely and may include some modeling and predictions which are usually computing-intensive and complicated, especially, when large datasets are involved. With advancement in computing technologies, techniques such as Machine learning (ML) are being suggested as a potential game changer in the analysis of GIS data because of their comparative speed, accuracy, automation, and repeatability. Perhaps, the greatest benefit of using both GIS and ML is the ability to transfer results from one database to another. GIS and ML tools have been used extensively in medicine, urban development, and environmental modeling such as landslide susceptibility prediction (LSP). There is also the problem of data loss during conversion between GIS systems in medicine, while in geotechnical areas such as erosion and flood prediction, lack of data and variability in soil has limited the use of GIS and ML techniques. This paper gives an overview of the current ML methods that have been incorporated into the spatial analysis of data obtained from GIS tools for LSP, health, and urban development. The use of Supervised Machine Learning (SML) algorithms such as decision trees, SVM, KNN, and perceptron including Unsupervised Machine Learning algorithms such as k-means, elbow algorithms, and hierarchal algorithm have been discussed. Their benefits, as well as their shortcomings as studied by several researchers have been elucidated in this review. Finally, this review also discusses future optimization techniques.

This paper systematically examines the theoretical significance, technical methodologies and practical applications of Geographic Information Systems (GIS) in geological hazard assessment, with particular emphasis on their role in evaluating typical hazards such as landslides, earthquakes and debris flows. Through spatial data collection, modelling, and analysis, GIS integrates multi-source data including remote sensing imagery, geological surveys, and sensor monitoring to construct hazard susceptibility zoning and risk prediction models, significantly enhancing evaluation precision and operational feasibility. Research indicates that GIS techniques—spatial overlay analysis, proximity analysis, and three-dimensional visualisation—effectively reveal spatial distribution patterns and evolutionary processes of disasters, providing scientific foundations for disaster prevention and mitigation. Nevertheless, GIS applications continue to face challenges including inconsistent data quality, conflicts between model complexity and computational efficiency, and insufficient real-time capabilities. Moving forward, through integration with artificial intelligence, big data, and cloud computing, GIS will demonstrate greater potential in dynamic early warning systems, intelligent modelling, and interdisciplinary collaboration, delivering precise and efficient solutions for geological hazard prevention and control.

Disaster resilience through big data: Way to environmental sustainability

The correlation and synthesis of a variety of two- and three-dimensional data is necessary to conceptualize and characterize ground-water flow systems in regional or site-specific areas. This paper presents an integrated, multidisciplinary, step-wise approach for conceptualizing, characterizing, and numerically simulating ground-water flow systems using Geographic Information Systems (GIS). The method presented involves seven iterative steps that can be accomplished manually or by using GIS: 1) Data gathering and preparation; 2) Field (on-site) conceptualization; 3) Surface characterization; 4) Subsurface characterization; 5) Hydrogeologic model characterization; 6) Hydrologic system characterization; and 7) Numerical model simulation. GIS may be used for: 1) data management, analysis, and visualization; 2) the integration of diverse data sources; 3) rapid development, visualization, and testing of alternative hydrogeologic and hydrologic system models; 4) the incorporation of contouring, attribute tables, and statistical analysis during data analysis and model development; and 5) the integration of basic and interpreted data bases with numerical modeling for purposes of model design, parameter discretization, statistical evaluation, and sensitivity analysis.

of ensuring data compatibility, exchange of data and use of data processing results by a wide range of researchers and other users. The solution of problems mentioned above can be significantly facilitated by creation and application of geographical information systems (GIS) technology, as a cumulative set of means of data filing, joint processing and analysis, with geographical or spatial reference. This technology comprises traditional methods of using databases with advantages of full-fledged visualization and geographical (spatial) analysis. These capabilities can distinguish GIS from other information systems and provide unique opportunities to its application for solving a wide spectrum of problems, related to analyzing and forecasting phenomena and events of the surrounding world, by contemplating and emphasizing main factors and causes, and also their possible consequences, by planning strategic solutions and current results of undertaken activities. The recent study the GIS technology was applied for construction and program realization of integrated geoinformation environment, including digital maps comprising various thematic data levels on geoscience. INDEX TERMS: 0520 Computational Geophysics: Data analysis: algorithms and implementation; 0530 Computational Geophysics: Data presentation and visualization; 0555 Computational Geophysics: Neural networks, fuzzy logic, machine learning; 0594 Computational Geophysics: Instruments and techniques; KEYWORDS: Geographic information system, geoinformatics, artificial intelligence.

GIS (Geographic Information Systems) are concerned with the manipulation and analysis of spatial data at a "Geographic" scale. Apart from issues of storage, database query and visualization, they must deal with several significantly different types of spatial information. These may be roughly classified as: discrete objects; networks; polygonal maps; and surfaces. Each of these has a specific set of assumptions associated with it, a specific data structure, and a specific set of algorithms. This produces a high level of complexity in the construction, manipulation, analysis and comparison of these datasets. We demonstrate that when "here " is replaced by "closest to here " the resulting proximal query (a Voronoi diagram) may be used to manipulate the four categories described above, with a resulting simplification of the system. All discrete objects become "fields ", with a value at any location. These fields provide the 'context' of the object, and many applications require both the primal and dual for analysis: the Quad-Edge is therefore the data structure of choice. Thus in 2D the 'context' the dual set of adjacent neighbours and neighbourhoods, as identified by the Voronoi diagram greatly assists in the interpretation and analysis of the data. In 3D the same thing holds true: the dual graph of the 3D object set in 3D GIS perhaps a model of the rooms of a building interior gives a clear structure for expressing their adjacency relations. A direct modification of the Quad-Edge - splitting the four parts into two pairs, each containing one primal and one dual half-edge -provides a structure appropriate for CAD-type nonmanifold modelling, which we demonstrate by the construction of a large building complex, using Euler-type operators. Thus, again, the dual graph provides the context for the (primal) geometric data.

Geomorphology is the science of studying landforms, landscapes, and their related processes, including the description, materials, classification, origin, evolution, and history of earth/planetary surfaces. Geographic information system (GIS) is a computer-based system used for collection, maintenance, storage, retrieval, analysis, and distribution of geographic data and information. A closely related technique to GIS is remote sensing (RS), the noncontact recording of electromagnetic spectrum of earth/planetary surfaces based on satellite-, aircraft-, or ground-based sensors to measure, detect, and classify ground objects. GIS and remote sensing have been integrated in many geomorphological studies to quantify surface processes and landforms. GIS/RS has been strongly linked with the methodology and concepts in geomorphology since its initial development. With the continual development of GIS and RS techniques, GIS/RS has been widely used to classify landform and landscape units, extract specific landform features, quantify process-landform relationships, and detect geomorphic changes. In particular, the combination of GIS/RS with digital elevation models (DEMs) has become one of the most common approaches for geomorphological research, especially with the early-21st-century progress in using LiDAR (light detection and ranging) and UAS (unmanned aircraft systems) to obtain high-resolution DEMs. A new discipline, geomorphometry, have been developed to quantify landforms and topography at various spatial scales on the basis of mathematical, statistical, and image-processing techniques. This article first includes a section focusing on the use of GIS/RS in general landform and landscape classification and then categorizes literature into a variety of subfields of geomorphology in which GIS/RS has been applied to solve geomorphological issues. These subfields include Glacial Geomorphology, Watershed and Fluvial Geomorphology, Hillslope Processes and Landslides, Coastal Geomorphology, Karst Geomorphology, Aeolian Geomorphology, and Tectonic Geomorphology. Some subfields, such as volcanic geomorphology and planetary geomorphology, are not included, but the methods and principles summarized in this article can be applied to these subfields.