Efficient Data Organization Research Articles

Research on Semantic Driven Urban Pipeline Dataspace Construction Method

Abstract. Urban pipeline data is heterogeneous in multiple sources and rich in data volume, and there are problems such as data conflict and difficult organization and management due to the heterogeneity of multiple sources when accessing the data in large-scale concurrently. To address this problem, this paper proposes a semantics-driven urban pipeline dataspace construction method, which aims to realize the efficient organization of pipeline data. Firstly, this method combines the classification and characteristics of urban pipelines, and expresses the semantic information of pipeline geographic entities from four dimensions: semantic description, spatial location, attribute characteristics and time evolution. Then, the four expression sets are embedded into the dataspace RDF model as predicates, and the associated description mechanism of pipeline geographic entities is established by means of genus classes and so on, so as to construct the pipeline dataspace RDF model. Finally, the model is stored and graphically visualized using neo4j to achieve fast retrieval of data within the pipeline dataspace. The research results show that this method provides a unified expression of pipeline entities, solves the problem of pipeline multi-source heterogeneous data conflict and organization difficulties, and improves the efficiency of multi-source heterogeneous pipeline data organization while ensuring the integrity of pipeline information to the maximum extent.

Life Integrate: Unify Your Life, Simplify Your Success

Abstract: Efficient management and organization of personal and commercial data is critical for reducing stress and increasing productivity in the fast-paced digital environment we live in. An inventive smartphone software called "Life Integrate" attempts to meet this requirement by effectively managing duties and merging different facets of life. The project aims to mimic the effectiveness of an AI personal assistant by bridging the gap between contemporary lives and efficient organizing. The software smoothly distinguishes between personal and professional data by utilizing technologies like machine learning and natural language processing, which improves the user experience and efficiency. One to three clicks can easily share data with the app, which adds to its effectiveness and user-friendliness. Aiming to streamline processes, key features include real-time editing, interactive chats, speech-activated assistants, and contextual awareness. The project's emphasis on developing a personal bot that excels at data collection and generates intelligent ideas based on collected data exemplifies an intriguing combination of customization and artificial intelligence. With its 13 primary sub-tabs and extensive questionnaires for storing both personal and professional data, the app offers an approach to organization and management that is user-centric. Effective app development and deployment are ensured by the use of the Flutter and Dart platforms. In general, "Life Integrate" seeks to transform the information landscape, making it easier to acquire and use, and facilitating more enjoyable interactions between humans and machines. With this project, we hope to create a future in which people can easily incorporate technology into their daily lives, increasing productivity and lowering stress levels

The Impact of Document Management Using Good Practices: A Literature Review

Purpose: The main objective of this work is to identify and contribute to our understanding the relationship, importance, and benefits of applying good practices in Document Management in public entities.
 
 Theoretical framework: Recent literature has highlighted the significance of employing good practices in document management within organizational settings. These practices encompass efficient data organization, accessibility, version control, and security measures (Malekany, 2022). However, the precise impact and implications of implementing these practices remain a subject of ongoing exploration.
 
 Design/Methodology/Approach: through a method of a systematic review and Meta-analysis, for which 51 research articles from the last 05 years were selected to be reviewed, it was compiled from different search sources and served to respond to the 08RQ raised that will allow a better understanding.
 
 Findings: When reviewing, the first discoveries that were found were the multiple benefits that are obtained by using good practices such as cost savings, and speed of service, among others. The results show that in other countries, they already make use of them, and they become not an obligation but a part of the habit, from day to day regardless of the sector or area to which the institution is dedicated to.
 
 Research, Practical & Social implications: We suggest a future research agenda and highlight the contributions made to executive and management education.
 
 Originality/Value: Document management is an evolving domain, and there is still much to uncover and comprehend regarding its effects on productivity, information retrieval, and overall organizational performance.

Learned Lessons Management Model for Project Administration

Around the world, organizations are turning to best practice methods and standards to guide the development of their projects. However, simply following these guidelines does not guarantee success in the execution and conclusion of a project. This research focused on designing a management model for the lessons learned throughout the life cycle of the projects. The ML3C model, developed as a result of this research, demonstrates its effectiveness in project management by facilitating the systematic collection and organization of valuable information derived from lessons learned from past experiences. This efficient organization of data translates into significant advantages, such as cost reduction, time savings, and improved customer satisfaction when receiving the desired service or product. In conclusion, project management is optimized by using tools that allow the effective use of lessons learned, as proposed in this research.

Point Cloud Registration Methods for Long-Span Bridge Spatial Deformation Monitoring Using Terrestrial Laser Scanning

In recent years, efforts have been devoted to utilizing terrestrial laser scanning for bridge spatial performance inspection, but they are still restricted to small or medium-span bridges, like some historical heritages. Due to the large-scale dimensional features of long-span bridges, applications of 3D point cloud techniques still remain challenging, such as the extra-long scan range and extreme-small incidence angle when scanning a bridge with a span over 1000 m. Moreover, rare attempts can be found for the performance evaluation of point cloud registration methods for long-span bridges as well, which is a critical basis for further spatial deformation recognition on the point cloud data. Hence, in this study, a cross-evaluation of three iterative closest point (ICP) registration methods is conducted for long-span suspension bridges, namely, traditional ICP, kd-tree-based ICP, and feature point-based ICP algorithms. We conducted field laser scanning on the Ma’anshan Yangtze Bridge, an 1880 m long suspension bridge located in China. The results show that the feature point-based ICP algorithm outperforms the other two in terms of convergence rate and execution time for a single iteration due to the smaller number of registration points compared to the other two algorithms. Moreover, it also gives more precise values in terms of bridge tower spatial deformation identification. Meanwhile, due to efficient data organization, the kd-tree-based ICP algorithm takes less time for a single iteration than the traditional ICP algorithm. Finally, two suggestions for algorithm improvement in terms of efficiency optimization and accuracy improvement of long-span bridge deformation analysis are proposed based on the assessment results.

A Framework for Big Earth Observation Data Using Horizontal Scaling Strategy

The rapid growth of earth observation (EO) data poses a challenge to the way of data management. An efficient framework based on big data technology can bring new solutions. Some excellent frameworks have been proposed, which provide efficient organization and management of EO data. However, they are not optimized for data distribution in the storage environment. In this paper, an optimized EO data management strategy is proposed. Different horizontal scaling strategies are designed to explore the optimal scheme of EO data distribution. The MapReduce parallel computing model was used to test the performance of data retrieval in the experiment. The results show that the proposed strategy contributes to the efficient organization and arrangement of data. Remote sensing (RS) data blocks can be evenly distributed to different shards according to the time characteristics and hash characteristics of the strategy, and the logical index of the data reduces the time consumed by the routing process. This distributed management mode that achieves load balancing provides a framework foundation for parallel computing. Therefore, the framework with an efficient strategy can improve the performance of data management.

An efficient memory data organization strategy for application-characteristic graph processing

An efficient memory data organization strategy for application-characteristic graph processing

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.

East Asian dance/movement therapy educators’ experiences of teaching dance/movement therapy in East Asia after training in the US

This qualitative study aimed to explore the lived teaching experiences of native East Asian dance/movement therapy (DMT) educators who completed their DMT education in the US, and then returned to their home countries to teach. The researcher intended to learn how participants adapted what they had learned to teach DMT to students in East Asia, including their challenges and successes. Six participants were recruited from Korea, Japan, Hong Kong, and Taiwan. Data were gathered via a researcher-designed demographic questionnaire and individual interviews. Nvivo10 software was used to ensure efficient data organization. Member checking and peer debriefing were used in different stages of data analysis to heighten the internal validity. Finally, five themes were revealed: (1) challenges as a DMT educator in one’s home country, (2) unfamiliar and different learning styles, (3) problems and needs, (4) efforts to develop the field of DMT as a DMT educator, and (5) giving advice and sharing meaningful moments. These findings support previous research on culturally competent teaching. This study hopes to contribute to creating a communicative and educational DMT bridge between East and West, and to enhance the indigenization of the field of DMT in East Asia.

Methods for Semantic Description of Digital Data in the Field of Medical Systems

The article discusses modern approaches to the study of patients with cardiac complaints. Methods for semantic description of digital data in the field of medical systems and construction of an ontology for Holter monitoring system are presented. A method of efficient organization of unstructured data used in this system has been studied.

Construction of multi-scale grid for massive land survey data

In the face of ever-growing and complex massive multi-source spatiotemporal data, the traditional vector data model is increasingly difficult to meet the needs of efficient data organization, management, calculation and analysis. Based on the simple and widely used geographic grid data organization model, this paper designs a technical method to convert vector data into multi-scale grid data, establishes a unified, standardized and seamless land spatial grid data model, and analyses the area accuracy of multi-scale grid data. Practice shows that the model can better meet the needs of multi-scale geospatial information integration and analysis, and it is easy to carry out distributed data processing, which provides technical support for the efficient organization, fusion and analysis of spatiotemporal data.

An efficient data organization and scheduling strategy for accelerating large vector data rendering

AbstractRendering large volumes of vector data is computationally intensive and therefore time consuming, leading to lower efficiency and poorer interactive experience. Graphics processing units (GPUs) are powerful tools in data parallel processing but lie idle most of the time. In this study, we propose an approach to improve the performance of vector data rendering by using the parallel computing capability of many‐core GPUs. Vertex transformation, largely a mathematical calculation that does not require communication with the host storage device, is a time‐consuming procedure because all coordinates of each vector feature need to be transformed to screen vertices. Use of a GPU enables optimization of a general‐purpose mathematical calculation, enabling the procedure to be executed in parallel on a many‐core GPU and optimized effectively. This study mainly focuses on: (1) an organization and storage strategy for vector data based on equal pitch alignment, which can adapt to the GPU's calculating characteristics; (2) a paging‐coalescing transfer and memory access strategy for vector data between the CPU and the GPU; and (3) a balancing allocation strategy to take full advantage of all processing cores of the GPU. Experimental results demonstrate that the approach proposed can significantly improve the efficiency of vector data rendering.

Context Sensitive Search String Composition Algorithm using User Intention to Handle Ambiguous Keywords

<p>Finding the required URL among the first few result pages of a search engine is still a challenging task. This may require number of reformulations of the search string thus adversely affecting user's search time. Query ambiguity and polysemy are major reasons for not obtaining relevant results in the top few result pages. Efficient query composition and data organization are necessary for getting effective results. Context of the information need and the user intent may improve the autocomplete feature of existing search engines. This research proposes a Funnel Mesh-5 algorithm (FM5) to construct a search string taking into account context of information need and user intention with three main steps 1) Predict user intention with user profiles and the past searches via weighted mesh structure 2) Resolve ambiguity and polysemy of search strings with context and user intention 3) Generate a personalized disambiguated search string by query expansion encompassing user intention and predicted query. Experimental results for the proposed approach and a comparison with direct use of search engine are presented. A comparison of FM5 algorithm with K Nearest Neighbor algorithm for user intention identification is also presented. The proposed system provides better precision for search results for ambiguous search strings with improved identification of the user intention. Results are presented for English language dataset as well as Marathi (an Indian language) dataset of ambiguous search strings.</p><p> </p>

REDLetr: Workflow and tools to support the migration of legacy clinical data capture systems to REDCap

ObjectiveA memory clinic at an academic medical center has relied on several ad hoc data capture systems including Microsoft Access and Excel for cognitive assessments over the last several years. However these solutions are challenging to maintain and limit the potential of hypothesis-driven or longitudinal research. REDCap, a secure web application based on PHP and MySQL, is a practical solution for improving data capture and organization. Here, we present a workflow and toolset to facilitate legacy data migration and real-time clinical research data collection into REDCap as well as challenges encountered. Materials and methodsLegacy data consisted of neuropsychological tests stored in over 4000 Excel workbooks. Functions for data extraction, norm scoring, converting to REDCap-compatible formats, accessing the REDCap API, and clinical report generation were developed and executed in Python. ResultsOver 400 unique data points for each workbook were migrated and integrated into our REDCap database. Moving forward, our REDCap-based system replaces the Excel-based data collection method as well as eases the integration into the standard clinical research workflow and Electronic Health Record. ConclusionIn the age of growing data, efficient organization and storage of clinical and research data is critical for advancing research and providing efficient patient care. We believe that the workflow and tools described in this work to promote legacy data integration as well as real time data collection into REDCap ultimately facilitate these goals.

Performance Analysis of Video Data Image using Clustering Technique

Objectives: This research paper focuses on design of a hierarchical clustering algorithm for efficient and effective organization of data for information retrieval. Method/Analysis: A classification tree is formed in COBWEB which indicates hierarchical clustering model. Findings: The proposed method utilizes less memory and worked well for all types of video files. Also this paper brings the comparison result of existing three types of video clustering algorithms BRICH, CURE, and CHAMELEON and their performances.

Conto: A Protégé Plugin for Configuring Ontologies

With the extensive proliferation of data, the efficient organization and integration of data has become exceedingly important. To achieve this, ontologies have been employed to govern the data by providing structure and a layer of abstraction over the data sources. However, the ontologies themselves present shortcomings in how they capture the definition of concepts of a domain. The definitions are static and unable to sufficiently conceptualize the dynamic data sources in a way that makes use of the knowledge provided by the data. This limitation carries over to the reasoning processes performed using ontologies.We introduce Conto, a Protégé plugin that overcomes this limitation by interpreting a concept as an abstract data type. These interpretations allow for new ways of understanding the data, which when coupled with knowledge generation processes leads to the generation of new knowledge that would traditionally be unobtainable.

DROP: A Data Reduction and Organization Paradigm and its Application in Image Analysis

In this paper, we deal with the problem of the annotation process in image analysis. This problem refers to the trade-off, wherein the human knowledge is indispensable for the success of the process and human's time and effort are precious resources. Can the human annotate a minimum number of images and the classifier label the remaining ones with high accuracy? Active learning techniques have been investigated to answer this question. However, these techniques very often ignore the need for interactive response times during the active learning process. They usually adopt a common paradigm which is impractical considering large datasets. We propose an effective and efficient Data Reduction and Organization Paradigm for image analysis. In our paradigm, the proposed active learning methods should be able to reduce and/or organize the large dataset such that sample selection does not require to reprocess it entirely at each learning iteration. Moreover, it can be interrupted as soon as a desired number of samples from the reduced and organized dataset is identified. These methods show an increasing progress, first with data reduction only, and then with subsequent organization of the reduced dataset. Experimental results have demonstrated the robustness of the proposed paradigm using datasets from distinct applications and baseline approaches.

Fusing cross-media for topic detection by dense keyword groups

Events are real-world occurrences that lead to the explosive growth of web multimedia content such as images, videos and texts. Efficient organization and navigation of multimedia data in the topic level can boost users׳ understanding and enhance their experience of the events that have happened. Due to the potential application prospects, multimedia topic detection has been an active area of research with notable progress in the last decade. Traditional methods mainly focus on single media, so the results only reflect the characteristics of one certain media and topic browsing was not comprehensive enough. In this paper, we propose a method of utilizing and fusing rich media information from web videos and news reports to extract weighted keyword groups, which are used for cross-media topic detection. Firstly by utilizing the video-related textual information and the titles of news articles, a maximum local average score is proposed to find coarse weighted dense keyword groups; after that, textual linking and visual linking are applied to refine the keyword groups and update the weights; finally, the documents are re-linked with the refined keyword groups to form an event-related document set. Experiments are conducted on cross-media datasets containing web videos and news reports. The web videos are from Youku, YouTube׳s equivalent in China, the news reports from sina.com, some of which contain topic-related images. The experimental results demonstrate the effectiveness and efficiency of the proposed approach.

Integrating data storage and annotation in the data workflow using the NIX format and libraries

Integrating data storage and annotation in the data workflow using the NIX format and libraries

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.