Data Organization Method Research Articles

Research on Spatial Data Aggregation Based on Aggregation Degree Function

Abstract. Restricted by the development level of hardware and software technology, computers cannot load massive data at one time, coupled with the fact that the three-dimensional linear scene is characterized by complicated lines, irregular shapes of features and a large number of features. Therefore, it is necessary to seek a three-dimensional spatial data organization and scheduling method applicable to the railroad scene to realize the display and interaction of the scene. In this paper, we propose a data organization method based on the aggregation degree function for the 3D model of features. The feature model is projected to generate an orthographic projection map, the cluster division rule is designed to divide the subsets, the feature model after clustering is aggregated using the feature model aggregation rule, and the aggregated model is displayed according to the aggregation degree function and viewpoint factor. The results of the test applied to the railroad scene show that the 3D spatial data aggregation based on the aggregation degree function can improve the loading speed of the 3D scene, and can effectively support the application of interactive visualization of the railroad 3D scene.

Data reorganization for image encryption: presentation in the data of hare

ABSTRACT In track and field, a hare (or pacemaker) is a runner who leads the early part of a race to help other competitors achieve better performance and potentially set records. This practice became common in the early 1980s. In cryptography, organizing data before encryption serves as a strategic technique to strengthen defenses against cryptanalytic attacks. Attackers often exploit patterns in encrypted data to narrow down key possibilities. By deliberately disrupting data structure prior to encryption, we can complicate these efforts. This concept, termed “Presentation in the Data of Hare,” proposes various data organization methods (e.g., line by line, column by column) to enhance encryption. This approach can add 21 bits to the encryption key, significantly increasing the complexity and time required to crack it—equivalent to a 2 million-fold increase. This work focuses on improving security in cryptographic systems by employing innovative techniques, including those related to post-quantum cryptography. By strategically organizing data before encryption, we aim to hinder potential cryptanalytic attacks, making it considerably harder for attackers to exploit any regularities, thus bolstering resilience against both classical and quantum threats.

A Semantic-Sensitive Approach to Indoor and Outdoor 3D Data Organization

Building model data organization is often programmed to solve a specific problem, resulting in the inability to organize indoor and outdoor 3D scenes in an integrated manner. In this paper, existing building spatial data models are studied, and the characteristics of building information modeling standards (IFC), city geographic modeling language (CityGML), indoor modeling language (IndoorGML), and other models are compared and analyzed. CityGML and IndoorGML models face challenges in satisfying diverse application scenarios and requirements due to limitations in their expression capabilities. It is proposed to combine the semantic information of the model objects to effectively partition and organize the indoor and outdoor spatial 3D model data and to construct the indoor and outdoor data organization mechanism of “chunk-layer-subobject-entrances-area-detail object.” This method is verified by proposing a 3D data organization method for indoor and outdoor space and constructing a 3D visualization system based on it.

Efficient image reconstruction for a small animal PET system with dual-layer-offset detector design.

A compact PET/SPECT/CT system Inliview-3000B has been developed to provide multi-modality information on small animals for biomedical research. Its PET subsystem employed a dual-layer-offset detector design for depth-of-interaction capability and higher detection efficiency, but the irregular design caused some difficulties in calculating the normalization factors and the sensitivity map. Besides, the relatively larger (2mm) crystal cross-section size also posed a challenge to high-resolution image reconstruction. We present an efficient image reconstruction method to achieve high imaging performance for the PET subsystem of Inliview-3000B. List mode reconstruction with efficient system modeling was used for the PET imaging. We adopt an on-the-fly multi-ray tracing method with random crystal sampling to model the solid angle, crystal penetration and object attenuation effect, and modify the system response model during each iteration to improve the reconstruction performance and computational efficiency. We estimate crystal efficiency with a novel iterative approach that combines measured cylinder phantom data with simulated line-of-response (LOR)-based factors for normalization correction before reconstruction. Since it is necessary to calculate normalization factors and the sensitivity map, we stack the two crystal layers together and extend the conventional data organization method here to index all useful LORs. Simulations and experiments were performed to demonstrate the feasibility and advantage of the proposed method. Simulation results showed that the iterative algorithm for crystal efficiency estimation could achieve good accuracy. NEMA image quality phantom studies have demonstrated the superiority of random sampling, which is able to achieve good imaging performance with much less computation than traditional uniform sampling. In the spatial resolution evaluation based on the mini-Derenzo phantom, 1.1mm hot rods could be identified with the proposed reconstruction method. Reconstruction of double mice and a rat showed good spatial resolution and a high signal-to-noise ratio, and organs with higher uptake could be recognized well. The results validated the superiority of introducing randomness into reconstruction, and demonstrated its reliability for high-performance imaging. The Inliview-3000B PET subsystem with the proposed image reconstruction can provide rich and detailed information on small animals for preclinical research.

A Pragmatic Framework for Data-Driven Decision-Making Process in the Energy Sector: Insights from a Wind Farm Case Study

In an era of big data, organizations increasingly aim to adopt data-driven decision-making processes to enhance their performance. This paper investigates the data-driven decision-making process by developing a framework tailored for application in the energy sector. The proposed framework integrates interdisciplinary approaches to comprehensively address the “data, information, knowledge” triad, applying it to both operational and maintenance decision-making. Designed to be managerially focused rather than technically oriented, the framework aims to engage all employees, including those without technical backgrounds, enabling them to effectively contribute to the decision-making process from their respective roles. To demonstrate the practical application of the proposed framework, this paper presents a case study of an energy organization managing a wind farm project, which implemented the framework to improve its decision-making process. The case study examines how the organization identified its objectives and information needs, formulated key performance questions for each stakeholder, explicitly defined and measured the key performance indicators, employed data collection and organization methods, managed the progression from data to information to knowledge, and transformed the acquired knowledge into informed decisions. By adopting this pragmatic framework, energy organizations are anticipated to solve problems, predict trends, and discover new opportunities, thereby enhancing their efficiency and predictability.

Subgrouping in a ‘dialect continuum’: A Bayesian phylogenetic analysis of the Mixtecan language family

AbstractSubgrouping language varieties within dialect continua poses challenges for the application of the comparative method of historical linguistics, and similar claims have been made for the use of Bayesian phylogenetic methods. In this article, we present the first Bayesian phylogenetic analysis of the Mixtecan language family of southern Mexico and show that the method produces valuable results and new insights with respect to subgrouping beyond what the comparative method and dialect geography have provided. Our findings reveal potential new subgroups that should be further investigated. We show that some unexpected groupings raise important questions for phylogenetics and historical linguistics about the effects of different methods of primary data gathering and organization that should be considered when interpreting subgrouping results.

Key theories and technologies and implementation mechanism of parallel computing for ternary optical computer.

Ternary Optical Computer (TOC) is more advanced than traditional computer systems in parallel computing, which is characterized by huge amounts of repeated computations. However, the application of the TOC is still limited because of lack of key theories and technologies. In order to make the TOC applicable and advantageous, this paper systematically elaborates the key theories and technologies of parallel computing for the TOC through a programming platform, including reconfigurability and groupable usability of optical processor bits, parallel carry-free optical adder and the TOC's application characteristics, communication file to express user's needs and data organization method of the TOC. Finally, experiments are carried out to show the effectiveness of the present theories and technologies for parallel computing, as well as the feasibility of the implementation method of the programming platform. For a special instance, it is shown that the clock cycle on the TOC is only 0.26% of on a traditional computer, and the computing resource spent on the TOC is 25% of that on a traditional computer. Based on the study of the TOC in this paper, more complex parallel computing can be realized in the future.

ОСОБЕННОСТИ ФОРМИРОВАНИЯ И МЕТОДИКИ СБОРА ПОЛЕВЫХ МАТЕРИАЛОВ, СОБРАННЫХ НА ТЕРРИТОРИИ АЛТАЙСКОГО КРАЯ В ЛИЧНОМ ФОНДЕ ФОЛЬКЛОРИСТА М.Н. МЕЛЬНИКОВА В ГОСУДАРСТВЕННОМ АРХИВЕ НОВОСИБИРСКОЙ ОБЛАСТИ

The paper analyzes the features of field data collection and organization methods used by Mikhail N. Melnikov during his work in Altai Krai, as seen in his personal collection stored at Novosibirsk Oblast State Archive. The author pays special attention to identifying the reasons for collecting the data, as well as the applied organizational principles. The features of different types of materials available in the collection (folklore, ethnographic, oral history) are characterized in the paper as well. Special emphasis is put on analyzing the specifics of methods used in the data collection carried out by external degree students.

A Method for Managing ADS-B Data Based on a 4D Airspace-Temporal Grid (GeoSOT-AS)

With the exponential increase in the volume of automatic dependent surveillance-broadcast (ADS-B), and other types of air traffic control (ATC) data containing spatiotemporal attributes, it remains uncertain how to respond to immediate ATC data access within a target area. Accordingly, an original multi-level disaggregated framework for airspace, and its corresponding information management is proposed. Further, a multi-scale grid modeling and coding mapping method of airspace information represented by ADS-B is put forth. Finally, tests on the validity of the 4D airspace-temporal grid we named as the GeoSOT-AS framework were conducted across key areas based on the development of an effective data organization method for ADS-B, or an effective algorithm for extracting relevant spatiotemporal data. Experimentally, it was demonstrated that GeoSOT-AS conforms to the existing Chinese specification of civil aeronautical charting and is advantageous for its low deformation and high practicality; furthermore, the airspace grid identification code modeling was less costly, and improved performance by >80% when used for ADS-B data extraction. GeoSOT-AS can thus provide effective reference and practical information for existing airspace data management methods represented by ADS-B and can subsequently be extended to other forms of airspace management scenarios.

ZINC-22─A Free Multi-Billion-Scale Database of Tangible Compounds for Ligand Discovery.

Purchasable chemical space has grown rapidly into the tens of billions of molecules, providing unprecedented opportunities for ligand discovery but straining the tools that might exploit these molecules at scale. We have therefore developed ZINC-22, a database of commercially accessible small molecules derived from multi-billion-scale make-on-demand libraries. The new database and tools enable analog searching in this vast new space via a facile GUI, CartBlanche, drawing on similarity methods that scale sublinearly in the number of molecules. The new library also uses data organization methods, enabling rapid lookup of molecules and their physical properties, including conformations, partial atomic charges, c Log P values, and solvation energies, all crucial for molecule docking, which had become slow with older database organizations in previous versions of ZINC. As the libraries have continued to grow, we have been interested in finding whether molecular diversity has suffered, for instance, because certain scaffolds have come to dominate via easy analoging. This has not occurred thus far, and chemical diversity continues to grow with database size, with a log increase in Bemis-Murcko scaffolds for every two-log unit increase in database size. Most new scaffolds come from compounds with the highest heavy atom count. Finally, we consider the implications for databases like ZINC as the libraries grow toward and beyond the trillion-molecule range. ZINC is freely available to everyone and may be accessed at cartblanche22.docking.org, via Globus, and in the Amazon AWS and Oracle OCI clouds.

Challenges of Documenting Historic Water Systems

ABSTRACT Geospatial research in archaeology often relies on datasets previously collected by other archaeologists or third-party groups, such as state or federal government entities. This article discusses our work with geospatial datasets for identifying, documenting, and evaluating prehistoric and historic water features in the western United States. As part of a project on water heritage and long-term views on water management, our research has involved aggregating spatial data from an array of open access and semi-open access sources. Here, we consider the challenges of working with such datasets, including outdated or disorganized information, and fragmentary data. Based on our experiences, we recommend best practices: (1) locating relevant data and creating a data organization method for working with spatial data, (2) addressing data integrity, (3) integrating datasets in systematic ways across research cohorts, and (4) improving data accessibility.

Number of trials and data structure affect the number and components of muscle synergies in upper-limb reaching movements

Objective. Due to the variability of human movements, muscle activations vary among trials and subjects. However, few studies investigated how data organization methods for addressing variability impact the extracted muscle synergies. Approach. Fifteen healthy subjects performed a large set of upper limb multi-directional point-to-point reaching movements. Then, the study extracted muscle synergies under different data settings and investigated how data structure prior to synergy extraction, namely concatenation, averaging, and single trial, the number of considered trials, and the number of reaching directions affected the number and components of muscle synergies. Main results. The results showed that the number and components of synergies were significantly affected by the data structure. The concatenation method identified the highest number of synergies, and the averaging method usually found a smaller number of synergies. When the concatenated trials or reaching directions was lower than a minimum value, the number of synergies increased with the increase of the number of trials or reaching directions; however, when the number of trials or reaching directions reached a threshold, the number of synergies was usually constant or with less variation even when novel directions and trials were added. Similarity analysis also showed a slight increase when the number of trials or reaching directions was lower than a threshold. This study recommends that at least five trials and four reaching directions and the concatenation method are considered in muscle synergies analysis during upper limb tasks. Significance. This study makes the researchers focus on the variability analysis induced by the diseases rather than the techniques applied for synergies analysis and promotes applications of muscle synergies in clinical scenarios.

Securing healthcare information system through fuzzy based decision-making methodology.

The purpose of the healthcare Information System (HIS) is to replace the conventional method of data gathering and organization in hospitals into a modern method of systematic data collection, maintenance and dissemination. There has been an unprecedented rise in the malware and cyber-attacks on HIS recently. Cyber-attacks have become a major crisis for the healthcare industry. To address this scenario, the present paper conducts a study on the security factors integral to the healthcare information system and conducts the performance analysis of these factors. For this intent, the study has employed the Fuzzy Analytic Hierarchy Process (F.AHP) integrated with Technique Order Preference by Similarity to Ideal Solution (TOPSIS) integrated framework for evaluating the performance of each factor. Thereafter, the factors that play a vital role in healthcare data security breaches have been prioritized as per their security weights. Furthermore, the validity of the results obtained by the stated methodology has been established by conducting the sensitivity analysis and comparison of results with the other methods by using the same data set. Based on results thus obtained, the access control and software security have been identified as the most promising security factors.

Intelligent City 3D Modeling Model Based on Multisource Data Point Cloud Algorithm

With the rapid development of smart cities, intelligent navigation, and autonomous driving, how to quickly obtain 3D spatial information of urban buildings and build a high-precision 3D fine model has become a key problem to be solved. As the two-dimensional mapping results have constrained various needs in people’s social life, coupled with the concept of digital city and advocacy, making three-dimensional, virtualization and actualization become the common pursuit of people’s goals. However, the original point cloud obtained is always incomplete due to reasons such as occlusion during acquisition and data density decreasing with distance, resulting in extracted boundaries that are often incomplete as well. In this paper, based on the study of current mainstream 3D model data organization methods, geographic grids and map service specifications, and other related technologies, an intelligent urban 3D modeling model based on multisource data point cloud algorithm is designed for the two problems of unified organization and expression of urban multisource 3D model data. A point cloud preprocessing process is also designed: point cloud noise reduction and downsampling to ensure the original point cloud geometry structure remain unchanged, while improving the point cloud quality and reducing the number of point clouds. By outputting to a common 3D format, the 3D model constructed in this paper can be applied to many fields such as urban planning and design, architectural landscape design, urban management, emergency disaster relief, environmental protection, and virtual tourism.

Development of the InTelligence And Machine LEarning (TAME) Toolkit for Introductory Data Science, Chemical-Biological Analyses, Predictive Modeling, and Database Mining for Environmental Health Research.

Research in environmental health is becoming increasingly reliant upon data science and computational methods that can more efficiently extract information from complex datasets. Data science and computational methods can be leveraged to better identify relationships between exposures to stressors in the environment and human disease outcomes, representing critical information needed to protect and improve global public health. Still, there remains a critical gap surrounding the training of researchers on these in silico methods. We aimed to address this gap by developing the inTelligence And Machine lEarning (TAME) Toolkit, promoting trainee-driven data generation, management, and analysis methods to “TAME” data in environmental health studies. Training modules were developed to provide applications-driven examples of data organization and analysis methods that can be used to address environmental health questions. Target audiences for these modules include students, post-baccalaureate and post-doctorate trainees, and professionals that are interested in expanding their skillset to include recent advances in data analysis methods relevant to environmental health, toxicology, exposure science, epidemiology, and bioinformatics/cheminformatics. Modules were developed by study coauthors using annotated script and were organized into three chapters within a GitHub Bookdown site. The first chapter of modules focuses on introductory data science, which includes the following topics: setting up R/RStudio and coding in the R environment; data organization basics; finding and visualizing data trends; high-dimensional data visualizations; and Findability, Accessibility, Interoperability, and Reusability (FAIR) data management practices. The second chapter of modules incorporates chemical-biological analyses and predictive modeling, spanning the following methods: dose-response modeling; machine learning and predictive modeling; mixtures analyses; -omics analyses; toxicokinetic modeling; and read-across toxicity predictions. The last chapter of modules was organized to provide examples on environmental health database mining and integration, including chemical exposure, health outcome, and environmental justice indicators. Training modules and associated data are publicly available online (https://uncsrp.github.io/Data-Analysis-Training-Modules/). Together, this resource provides unique opportunities to obtain introductory-level training on current data analysis methods applicable to 21st century science and environmental health.

Study on the Efficiency of Data Collection, Organization and Feedback Methods for Triathlon Races

Considering the problems of data handling methods in the sports field, the use of smartphones, free tools, going online with the COVID-19 disaster, reducing the workload of data managers, creating graphs automatically, and building a system that can provide immediate feedback are considered to be useful. This study aims to improve data handling at sporting events by examining methods of enhancing data provision efficiency, such as collecting the athletes' physical condition and weather data, for the athletes, coach, and trainer. The data were collected using Google Forms and Google Sheets, and the study considered the problems associated with the data handling process. A questionnaire-based survey regarding data handling was conducted among seven athletes, one coach, and one trainer from a local triathlon team. The questions included collecting information on whether the (1) participants had viewed the Google Sheets, (2) data were useful for the race, (3) Google Sheets were easy to view, and (4) participants had any requests. In addition, eight university students were targeted as simulations for data organization. The results indicated that the use of Google Forms and Google Sheets was more efficient than the traditional paper-based method. In conclusion, this method is useful in collecting, organizing, and providing data at sporting events. The results of this study may lead to improvements in the way data are used in sports. In the future, it may be necessary to fine-tune the system to meet the needs of individual users.

Research on the Method of Predicting Consumer Financial Loan Default Based on the Big Data Model

With the continuous advancement of Internet technology and the continuous development of big data model applications, the data model is a method of data organization and storage, which emphasizes the reasonable storage of data from the perspective of business, data access, and use. The rapid development of information technology on the Internet and the increasing consumption level of people have created conditions for the booming development of consumer finance. Compared with traditional lending methods, more and more consumers are more inclined to choose consumption channels such as the Internet and e-commerce. Consumer finance lending is more convenient and fast. The prediction of consumer financial loan default is very important, because the inaccuracy of the prediction not only leads to the loss of profits but also infringes the rights and interests of consumers. Therefore, it is very important to propose a default prediction method in the consumer finance field with good performance based on the big data model. Simulation experiment conclusions are shown as follows: (1) the pseudo R -squared value of the model is 0.3660, indicating that the control variable can better explain the change of y . (2) The chi-square test statistic is equal to 51632.31, the degree of freedom is 68, and the corresponding P < 0.0001 , which also shows that the entire model can significantly predict the change of y . (3) The regression coefficient of the number of loan performances is -0.207553, indicating that the number of consumer loans is negatively correlated with loan defaults. (4) The regression coefficient of the monthly loan frequency is 0.0500152, indicating that the customer applies the frequency of personal credit consumer loans which is positively correlated with loan defaults. (5) The accurate prediction ratio of the model is 86.11%, which further shows that the prediction model has a better effect.

A Data Organization Method for LSTM and Transformer When Predicting Chinese Banking Stock Prices

The accurate prediction of stock prices is not an easy task. The long short-term memory (LSTM) neural network and the transformer are good machine learning models for times series forecasting. In this paper, we use LSTM and transformer to predict prices of banking stocks in China’s A-share market. It is shown that organizing the input data can help get accurate outcomes of the models. In this paper, we first introduce some basic knowledge about LSTM and present prediction results using a standard LSTM model. Then, we show how to organize the input data during the training period and give the comparison results for not only LSTM but also the transformer model. The numerical results show that the prediction results of LSTM and transformer can be improved after the input data are organized when training.

Адаптація масивів газодинамічних характеристик до автоматизованого балістичного супроводу руху космічних апаратів

The current level of the design and use of new-generation spacecraft calls for a maximally automated ballistics support of engineering developments. An integral part of the solution of this problem is the development of an effective tool to adapt discrete functions of gas-dynamic characteristics to the solution of various problems that arise in the development and use of space complexes. Simplifying the use of bulky information arrays together with improving the accuracy of approximation of key coefficients will significantly improve the ballistics support quality. The aim of this work is to choose an optimum method for the approximation of a discrete function of two variable spacecraft aerodynamic characteristics. Based on the analysis of the advantages and drawbacks of basic methods of approximation by two fitting criteria: the maximum error and the root-mean-square deviation, recommendations on this choice were made. The methods were assessed by the example of the aerodynamic coefficients of the Sich-2M spacecraft’s simplified geometrical model tabulated as a function of the spacecraft orientation angles relative to the incident flow velocity. Multiparameter numerical studies were conducted for different approximation methods with varying the parameters of the approximation types under consideration and the approximation grid density. It was found that increasing the number of nodes of an input array does not always improve the accuracy of approximation. The node arrangement exerts a greater effect on the approximation quality. It was established that the most easily implementable method among those considered is a step interpolation, whose advantages are simplicity, quickness, and limitless possibilities in accuracy improvement, while its significant drawbacks are the lack of an analytical description and the dependence of the accuracy on the grid density. It was shown that spline functions feature the best approximating properties in comparison with other mathematical models. A polynomial approximation or any approximation by a general form function provide an analytical description with a single approximating function, but their accuracy of approximation is not so high as that provided by splines. It was found that there exists no approximation method that would be best by all criteria taken together: each method has some advantages, but at the same time, it has significant drawbacks too. An optimum approximation method is chosen according to the features of the problem, the priorities in approximation requirements, the required degree of approximation, and the initial data organization method.

Data organization issues in civil engineering

In today's organization of data in civil engineering there remains ambiguity, diversity of terminology and consequent lack of clarity. The daily reality is to work with hybrid data sources in a variety of software programs. In CAD programs it is possible to import and convert elements from GIS, but they will be classified only into layers. Or, attach external references from CAD files in BIM, but there still remains the problem of finding your way around the confusing list of layers, and their abbreviations. Also broadly used filesystems as place for store and organise data on disk into files (entities) and hierarchical structure of folders (categories) is limited by reductionist categorization approach. Another problem shows with absence of multilingual approach. The limits are in the methods of data organization, and so only partially portable. Therefore, if there is a common denominator to all problems, it is the absence of a unifying organizational environment. In current computer applications used in civil engineering, methods of data classification are implemented that do not allow multiplicative or faceted categorization and thus inevitably create an antagonistic environment with any previous or future classification system. Unfortunately, even if a software tool implements a modern multiplicative categorization or domain ontology, it will not significantly solve the overall situation, because it will still not be possible to use such a method of categorization in other applications. Therefore, if there should exist a solution that would significantly facilitate cooperation and orientation in the data, it must inevitably work independently, across diverse software applications, with the ability to link the functions of the application with such a separate categorization system.