Data Organization Method Research Articles

Efficient Visualization of Large-Scale Oblique Photogrammetry Models in Unreal Engine

Oblique photogrammetry models are indispensable for implementing digital twins of cities. Geographic information system researchers have proposed plenty of methods to load and visualize these city-scaled scenes. However, when the area viewed changes quickly in real-time rendering, current methods still require excessive GPU calculation and memory occupation. In this study, we propose a data organization method in which we merged all quadtrees and used a binary encoding method to encode nodes in a merged tree so that the parent–child relationship between the tree nodes could be calculated using rapid binary operations. After that, we developed a strategy to cancel the loading of redundant nodes based on the parent–child relationship, which helped to reduce the hard disk loading time and the amount of memory occupied in visualization. Moreover, we introduced a parameter to measure the area of the triangle mesh per pixel to achieve unified data scheduling under different production standards. We implemented our method based on Unreal Engine (UE), and three experiments were designed to illustrate the advantages of our methods in index acceleration, frame time, and memory reduction. The results show that our methods can significantly improve visualization fluency and reduce memory usage.

Convergence application of handle platform for industrial data classification and grading and 5G technology

With the continuous upgrading of the national industrial Internet, the traditional application-centric data organization method has gradually been unable to meet the requirements of large-scale interoperability and sharing of industrial data and data security in the era of big data. With the help of the classification and grading of industrial data, cross-regional, cross-departmental, and cross-domain information query and sharing can be realized at the macro level; at the micro level, product information can be traced through identification analysis, and information can be achieved safely and controllably through its internal security management mechanism. In the field of industrial Internet, network equipment is usually required to have a large-scale connection expansion capability; in some fields such as telemedicine and industrial control, it requires the communication process to have low latency and high reliability capabilities; and in some areas such as autonomous driving and ultra-high-definition live transmission, the communication process requires the ability to transmit large-bandwidth data. Compared with 4G technology, 5G communication technology, its massive machine communication capabilities and high reliability and low latency characteristics make it a reliable technical guarantee for the Internet of Everything at this stage. Therefore, it is urgent to realize the integration of 5G technology and industrial data classification and grading technology. Based on the in-depth interpretation of the characteristics of 5G technology and the detailed description of the handle system, this paper proposes a method of fusion of the handle platform and 5G technology, laying the cornerstone for the effective implementation of industrial data classification and grading.

3D Information Visualization Interactive Technology and Its Application in Network Security

With the increase of network complexity, network security incidents are showing a trend of rapid growth. Aiming at the low efficiency of large-scale network security data visual analysis, this paper proposes a heterogeneous tree network security data organization method, which improves the real-time performance of data analysis, and designs a three-dimensional multi-layer spherical space for large-scale networks Visualization model (TMSM). The experimental results show that this method has strong analytical capabilities for large-scale, high-dispersion network security data, and can effectively identify different types of intrusions and facilitate network forensics.

An Efficient Organization Method for Large-Scale and Long Time-Series Remote Sensing Data in a Cloud Computing Environment

Historical earth observation (EO) data have played an important role in long-term scientific and environmental monitoring. The effective organization of large-scale and long-term remote-sensing data to achieve efficient retrieval and access has become one of the important issues. However, inherent big data characteristics, such as a large scale, and asymmetrical temporal and spatial distributions, have caused problems with the efficiency of data retrieval and access. Therefore, this study proposes an efficient data organization method for use in a cloud-computing environment that has two aims. First, it addresses the problem of low retrieval efficiency. An asymmetrical index model for the image metadata is constructed that is based on a unified spatio-temporal grid coding; a prepartitioning mechanism under the HBase architecture is established to realize the uniform storage of the metadata with an asymmetrical spatiotemporal distribution and to avoid retrieval efficiency bottlenecks caused by a load imbalance. Second, it addresses low access efficiency. By dividing the remote-sensing image into tiles, a unified spatio-temporal code is established for each tile, and a consistent hash operation is performed; tiles with similar hash values are stored in the same or adjacent Hadoop Distributed File System nodes. In this way, tiles with temporal or spatial correlations can be gathered and stored, and lots of disk seeks can be avoided during retrieval, thereby greatly improving the data access efficiency. Comparative experiments showed that the data organization method can effectively improve the retrieval and access efficiencies of large-scale and long time-series remote-sensing data in a cloud-computing environment.

Institutional and Collaborative Work Perspectives on Specimen Databases

The U.S. National Science Foundation (NSF) funded a grand experiment on the U.S. biological collections community, although it may not have anticipated the significance of the results. For over 30 years, the NSF made recurring investments through competitive grants in software engineering and technical support for biological collections databases. The Specify Project (now the Specify Collections Consortium), and its predecessor the MUSE Project, which was first funded in 1987, represent a lineage of sustained NSF investment in biological collections database systems. Specify is largely scoped for institutional curatorial, collections management, and data publishing functions, and it is generally deployed collection-by-collection within research institutions reflecting traditional administrative and disciplinary boundaries. MUSE and Specify grew out of a need U.S. collections institutions had for a common data model, a source of ongoing technical support, and desktop applications for the activities associated with collections management, e.g., tracking loans, accessions, gifts, and for printing labels and reports. In 2011, NSF announced its first "Thematic Collections Network" (TCN) awards from the "Advancing Digitization of Biodiversity Collections" (ADBC) program. TCN projects are also focused on the computerization of collections data but from a different perspective, that is the digitization of specimens organized around a particular taxonomic group and/or research theme. The NSF TCN projects use Symbiota database software. Symbiota is designed for collaborative digitization workgroups across institution and collection boundaries. Symbiota fits the model advanced by the ADBC Program's TCN awards partially because it mirrors the way museum and herbarium scientists collaborate professionally—along taxonomic lines—in order to share common research interests and expertise, and to organize projects around their focal group of organisms. Thus after 10 years of successive rounds of TCN project funding, millions of specimen data records have been digitized for the first time in Symbiota databases, however, a considerable percentage do not exist in institutional collection management platforms. Today in the U.S., we have a duality of approaches toward specimen data computerization, collaborative workgroup databases at one pole and institutional collection database management systems at the other (while the Arctos Project sits somewhere in the middle). Tens of millions of species occurrence records are now online from NSF grant funding for the TCNs and for Specify databases over the years, but the dynamics and constraints of the duality that exists today between these two perspectives for digitizing and publishing collections data, initially may not have been so obvious. The duality reflects the partially-distinct and partially-overlapping goals of their stakeholders—institutional collection owners on the one hand, and "thematic" research users of specimen data on the other. Both methods of specimen data organization and the stakeholders associated with them are critical to the long-term engagement and sustainability not only of the data and their software platforms, but also of the biological collections themselves. This duality distinguishing institutional perspectives on collections data from thematic research perspectives has become apparent because of NSF's support of both types of computing, but their existence is due to more than grant funding patterns. They represent parallel computational perspectives that biodiversity data community architecture has yet to fully reconcile. It will be critical to bridge these two worlds of specimen data practice and recognize the strengths and essential nature of both paradigms. This presentation will discuss the characteristics of these two modes of specimen digitization and why they should both be fundamental components of digital specimen architecture planning. Although NSF funding revealed and accentuated the differences between these two ways of processing species occurrence data, the duality is productive and permanent.

Practical recommendations to conduct a neuroimaging meta-analysis for neuropsychiatric disorders.

Over the past decades, neuroimaging has become widely used to investigate structural and functional brain abnormality in neuropsychiatric disorders. The results of individual neuroimaging studies, however, are frequently inconsistent due to small and heterogeneous samples, analytical flexibility, and publication bias toward positive findings. To consolidate the emergent findings toward clinically useful insight, meta-analyses have been developed to integrate the results of studies and identify areas that are consistently involved in pathophysiology of particular neuropsychiatric disorders. However, it should be considered that the results of meta-analyses could also be divergent due to heterogeneity in search strategy, selection criteria, imaging modalities, behavioral tasks, number of experiments, data organization methods, and statistical analysis with different multiple comparison thresholds. Following an introduction to the problem and the concepts of quantitative summaries of neuroimaging findings, we propose practical recommendations for clinicians and researchers for conducting transparent and methodologically sound neuroimaging meta-analyses. This should help to consolidate the search for convergent regional brain abnormality in neuropsychiatric disorders.

Geospatial Data Organization Methods with Emphasis on Aperture-3 Hexagonal Discrete Global Grid Systems

Digital Earth frameworks deal with data sets of different types collected from various sources. To effectively store, retrieve, and transmit these data sets, efficient multi-scale data representations that are compatible with the underlying structure of the Digital Earth framework are required. In this article, we describe several such techniques and their properties: namely, how to represent data in the multi-scale cell hierarchy of a discrete global grid system (DGGS) or in the multi-scale hierarchy of a customized wavelet transform. We also discuss how these techniques can be tuned to be applicable to the A3H DGGS.

BcBIM: A Blockchain‐Based Big Data Model for BIM Modification Audit and Provenance in Mobile Cloud

Building Information Modeling (BIM) is envisioned as an indispensable opportunity in the architecture, engineering, and construction (AEC) industries as a revolutionary technology and process. Smart construction relies on BIM for manipulating information flow, data flow, and management flow. Currently, BIM model has been explored mainly for information construction and utilization, but rare works pay efforts to information security, e.g., critical model audit and sensitive model exposure. Moreover, few BIM systems are proposed to chase after upcoming computing paradigms, such as mobile cloud computing, big data, blockchain, and Internet of Things. In this paper, we make the first attempt to propose a novel BIM system model called bcBIM to tackle information security in mobile cloud architectures. More specifically, bcBIM is proposed to facilitate BIM data audit for historical modifications by blockchain in mobile cloud with big data sharing. The proposed bcBIM model can guide the architecture design for further BIM information management system, especially for integrating BIM cloud as a service for further big data sharing. We propose a method of BIM data organization based on blockchains and discuss it based on private and public blockchain. It guarantees to trace, authenticate, and prevent tampering with BIM historical data. At the same time, it can generate a unified format to support future open sharing, data audit, and data provenance.

Research on Network Security Visualization Based on Graph Database

The combination of network security technology and data visualization has formed a new hot research direction - network security visualization. In this paper, network security visualization framework based web is proposed firstly, and then the composition of the framework and the main functions of its constituent elements is introduced. Secondly, based on the analysis of network security data structure, a network security data organization method based on graph database is proposed. Finally, based on the above method, a network security visualization prototype system is implemented. The results show that the two types of views of the system show network security situation from different dimensions, and it is feasible and effective to analyze network security data.

A hybrid Genetic-Ant Colony Optimization Algorithm for the Optimal Path Selection

AbstractThe shortest path problem lies at the heart of network flows that seeks for the paths with minimum cost from source node to sink node in networks. This paper presents a hybrid genetic-ant colony optimization algorithmic approach to the optimal path selection problem. First, some existing solutions for the optimal path selection problem are analyzed, and some shortages and flaws are pointed out. Second, the data organization method for road network based on the graph theory is proposed. Furthermore, the optimal path selection algorithm integrated of sinusoidal probability transfer rules, pheromone update strategy and dual population is presented. Finally, the experimental results show that the proposed algorithm speeds up the convergence rate and improves the efficiency.

Education, Internal Remittances and Safety Nets in Africa: Some Evidence

This paper uses LSS data from Cote d'Ivoire and Ghana to investigate the effects of education on social safety nets proxied by internal migrant remittances in Africa. We find that education has positive and statistically significant effect on the probability of sending as well as of the amount of remittance. The estimates are robust to model specification, data organization, and estimation method. The results suggest that one mechanism through which education provides a social safety net in Africa is the migration and remittances channel. Our results suggest that investment in education may not only accelerate economic growth in Africa, it may also provide a social safety net for the population.

Experimental data description ontology model

The question of most convenient and effective experimental data presentation for data-base storage are wrote in this paper. A widely used data organization methods review are also presented for choosing the most suitable one for this subject. This paper also included a description of the data storage in databases.

Database and data structure for the novel TOF-PET detector developed for the J-PET project

Abstract The complexity of the hardware and the amount of data collected during the PET imaging process require application of modern methods of efficient data organization and processing. In this article, we will discuss the data structures and the flow of collected data from the novel TOF-PET medical scanner that is being developed at the Jagiellonian University. The developed data format reflects the registration process of the γ quanta emitted from positron electron annihilation, front-end electronic structure, and required input information for the image reconstruction. In addition, the system database fulfills possible demands of the evolving J-PET project.

Rational evaluation of antimicrobial properties of lactobacilli isolates against some pathogenic microorganisms: a new method comparing the susceptibility of indicator microorganisms

BACKGROUND: Lactobacilli are known as a valuable source of antimicrobial compounds and have a high potential of use in food biopreservation against food related microorganisms. OBJECTIVES: Antimicrobial potency of 63 dairy lactobacilli isolates against four highly important food-related microorganisms were evaluated. In addition, a new way in data organization was introduced, which led to a more informative and rational comparison of indicator microorganisms' susceptibilities to a set of compounds. Correlation of pH and antimicrobial properties was investigated. METHODS: Microbroth dilution assay was used to evaluate indicator microorganisms' susceptibility to lactobacilli CFCS (cell free culture supernatant). Results were organized by both the conventional way - demonstrating the minimum inhibitory and lethal concentrations of CFCS - and a new suggested method, representing comparative effectiveness of each CFCS specimen against indicator microorganisms of comparison interest. RESULTS: Susceptibilities of tested strains were in the following order: Escherichia coli O157:H7 > Listeria monocytogenes > Aspergillus parasiticus > Candida parapsilosis. Despite the high susceptibility of L. monocytogenes, it showed the highest resistance to death among the tested microorganisms. Eefficiency of Lactobacilli CFCS in killing the tested strains showed the following susceptibility order: E. coli O157:H7 > A. parasiticus > C. parapsilosis > L. monocytogenes. Antimicrobi- al property was in correlation with the pH value of CFCS. PH had a pronounced impact on susceptibilities of C. parapsilosis and E. coli in pH values of concentrated CFCS lower than 4 and 4.5, respectively. CONCLUSIONS: Potency of lactobacilli isolates in growth inhibition of the indicator microorganisms was found promising, and the suggested data organization method provided additional information, leading to more precise comparison of indicator microorganisms.

Managing the Global Dataset in Block-index based on ESSG

Abstract. Earth observation system, which can realize global coverage in three dimensions, has provided massive data from earth observations at global scale for Earth System Science (ESS) and global change researches. It is named SDOG-based ESSG that the Spheroid Degenerated Octree Grid (SDOG) was chosen as an initial grid to develop an Earth System Spatial Grid (ESSG), which provides storage strategy and management framework based on triple (C, T, A) and SDOG grid coding for massive data. The objection of this paper is to provide an effective spatial data organization and indexing method, which organizes triple units into block and encodes for each triples block and converts multi-source global scale datasets into triples block so as to manage massive raster data at global scale.

Data-modeling and implementation for massive construction project data based on manageable entity-oriented object

For the requirements of building Project Information Portal(PIP) data center based on a unified data model,a manageable entity object-oriented data model was proposed.The project data were treated as a series of managerial entity based on management workflows which were decomposed according to the whole life cycle.The conceptual layer data model was designed.The project data could be naturally represented and recorded by using this model.The data organization method was presented based on MongoDB(document-oriented database technology).The cluster storage architecture for PIP was also addressed.The experiments show that it has efficient performance in data writing and querying.It also has high availability and storage capacity scalability.

Parallel Rendering Strategies for 3D Emulational Scene of Live Working

Because of abundant deep scene nodes in 3D emulational scene of live working, the existing three-dimensional scene data organization methods and rendering strategies have many flaws, such as the jumping of rendering and the delay of interactive response. A real-time rendering method for huge amount of urban data was presented utilizing the techniques such as identifying model that is based on multi-grid block partition, thread pool, caching and real time external memory scheduling algorithms. The whole scene was partitioned into blocks of different size and the blocks were arranged with multi-grid which is related to model ID and tile ID to accelerate model scheduling. Fast clipping was achieved through the nailing of position and direction of block-based view frustum, and touching task of data downloading off into thread pool executed in background which achieve the dynamic data loading and parallelism of three-dimensional scene rendering. To solve the choke point at computer hardware, in-out memory scheduling algorithms are adopted to eliminate invisible scene models and recycle dirty data in memory. Experimental results showed that the method is very efficient and suitable for applications in massive urban models rendering and interactive walkthrough.

Making sense of complex data: a mapping process for analyzing findings of a realist review on guideline implementability

BackgroundRealist reviews offer a rigorous method to analyze heterogeneous data emerging from multiple disciplines as a means to develop new concepts, understand the relationships between them, and identify the evidentiary base underpinning them. However, emerging synthesis methods such as the Realist Review are not well operationalized and may be difficult for the novice researcher to grasp. The objective of this paper is to describe the development of an analytic process to organize and synthesize data from a realist review.MethodsClinical practice guidelines have had an inconsistent and modest impact on clinical practice, which may in part be due to limitations in their design. This study illustrates the development of a transparent method for organizing and analyzing a complex data set informed by a Realist Review on guideline implementability to better understand the characteristics of guidelines that affect their uptake in practice (e.g., clarity, format). The data organization method consisted of 4 levels of refinement: 1) extraction and 2) organization of data; 3) creation of a conceptual map of guideline implementability; and 4) the development of a codebook of definitions.ResultsThis new method is comprised of four steps: data extraction, data organization, development of a conceptual map, and operationalization vis-a-vis a codebook. Applying this method, we extracted 1736 guideline attributes from 278 articles into a consensus-based set of categories, and collapsed them into 5 core conceptual domains for our guideline implementability map: Language, Format, Rigor of development, Feasibility, Decision-making.ConclusionsThis study advances analysis methods by offering a systematic approach to analyzing complex data sets where the goals are to condense, organize and identify relationships.

Two Database Related Interpretations of Rough Approximations: Data Organization and Query Execution

We present analytic data processing technology derived from the principles of rough sets and granular computing. We show how the idea of approximate computations on granulated data has evolved toward complete product supporting standard analytic database operations and their extensions. We refer to our previous works where our query execution algorithms were described in terms of iteratively computed rough approximations. We explain how to interpret our data organization methods in terms of classical rough set notions such as reducts and generalized decisions.

LIDAR PHOTOGRAMMETRY AND ITS DATA ORGANIZATION

Abstract. Light detection and ranging (LiDAR) is a technology capable of measuring geo-referenced coordinate of object points in a straightforward way. It has been widely used in mapping projects in last 20 years. LiDAR and photogrammetry has their own advantages and disadvantages that compensate for each other very well. The combination of the two definitely can achieve more efficient and precise acquisition of geospatial information. Nowadays main LiDAR system vendors are providing integrated laser scanning and camera sensors in many of their systems (or at least offering camera as optional equipment). This makes seamlessly integrating the two technologies possible, but the potential of the integrated system has to be explored yet. In this paper we propose a new subject – LiDAR photogrammetry based on the integrated sensor system and discuss its research scope and fundamental topics (or techniques to be developed). Organizing integrated data properly is the first step in LiDAR photogrammetry because existing image and LiDAR point cloud data are organized separately that leads to inefficient data access and usage in processing. Based on the extension of LAS format we propose a new data organization scheme called DPL format. Examples are given to show advantages of the new data organization method.