Data Integration Module Research Articles

BIM-INTEGRATED SYSTEM: A SUCCESSFUL ALTERNATIVE FOR ESTIMATING CASH FLOW IN BUILDING PROJECTS

Cash flow calculation is an essential economic aspect of evaluating the success of a construction project. Strong cash flow positively impacts not only project effectiveness but also company profitability. Cash flow calculation is a good way for the contractors to avoid risks related to delay, cost overrun, or incompletion caused by financial deficits in the project’s performance. Building Information Modeling (BIM) is a revolutionary technology 4.0 that has been mandated and adopted by many countries all over the world. In Vietnam, a roadmap for BIM application in the construction sector was issued in 2023, under which obligatory new investment projects in public investment state capital provide BIM files to serve the appraisal of feasibility study reports, construction designs carried out after fundamental designs, applications for construction permits, and acceptance testing tasks. However, BIM authoring programming is still limited to cost management, especially cash flow calculations, because Vietnamese contractors are still facing a lot of difficulties in applying BIM to their projects, especially cash flow calculations. Therefore, this paper proposes a BIM-integrated system for calculating construction cash flow at the early phase of the project life cycle. The proposed system includes four modules: BIM visualization and data integration, construction cost estimating, construction project scheduling, and project cash flow calculation. The main functionality of the BIM visualization and data integration module is to provide a 3D visualized model with enough geometric and non-geometric data for the project quantity take-off. The functionality of the construction cost estimating module is to provide the direct cost categories, such as labor, material, and machine, and the indirect cost categories to perform all work tasks in the construction project. The main functionality of the construction project scheduling module is to provide the required time to complete all work tasks in the project implementation process. The main functionality of the project cash flow calculating module is to present cash inflows such as advance payment, payments from the owner, and cash outflows such as construction costs, equipment costs, project management costs, and project consultant costs to carry out the projects. The BIM-integrated system will make the calculation process of construction project cash flow easier and more accurate.

Data-attention-YOLO (DAY): A comprehensive framework for mesoscale eddy identification

The accurate mesoscale eddy identification methods with deep learning framework depend on either single eddy characteristic from altimeter missions or multi-step eddy examination strategies, disregarding those indistinguishable features from multiple eddy data integration. In this article, we first propose a data-attention-based YOLO (DAY) to precisely recognize mesoscale eddies in the South China Sea (SCS), which can hierarchically unite multiple eddy attributes and efficiently predict eddies with one-step strategy involving detection and classification. It consists of two main components: heterogeneous eddy data integration module and dynamic attention detecting module for eddy identification. The data integration component empirically transforms the field of multi-source eddy data and propagates eddy labels through automatic labeling method, which sustains a good supply for our dynamic attention-base detecting network. To thoroughly identify mesoscale eddies based on spatio-temporal patterns, DAY efficiently learns the characteristics of mesoscale eddies with an improved one-step identification YOLO network. The comparative evaluation results demonstrate that DAY achieves 54% performance improvement over the state-of-the-art methods on single gray SLA data and outperforms two-stage detecting technique Faster R-CNN by 51%.

해군 무기체계 전력발전업무 단계별 디지털 트윈 적용 방안 연구

The purpose of this study is to propose a digital twin application plan according to the three stages of naval weapon system force development work. Firstly, in the planning stage, pre-conceptual research is currently performed by brainstorming and data research. Therefore, this study proposed to create and test a digital model in a virtual environment using digital twin technology. Secondly, in the acquisition stage, a plan was proposed to upgrade the digital twin system based on the previously established virtual environment. In the previous stage, M&S system is created and a digital twin modeling and sensors are created in the exploration and development stage. After that, advanced digital twin system will be built in the system development stage. Lastly, in the operation and maintenance stage, analyzing the data obtained from the real environment using the digital twin system, it predicts equipment failure and repair the broken equipment quickly. Next, a digital twin architecture suitable for the naval weapon system was proposed. The architecture consisted of a virtual-physical interaction module, a data integration module, and a knowledge calculation and visualization module.

A Taxi Trajectory and Social Media Data Management Platform for Tourist Behavior Analysis

Taxis are commonly used by tourists to travel around unfamiliar cities they visit. These taxis today have GPS devices, which can then be used to collect a significant amount of data on the movement of tourists. One problem with this idea, however, is the question of how to extract that movement data from the raw GPS data, which includes a lot of other data, such as vehicle IDs, timestamps, and speeds, etc. The purpose of this research is to propose a data management platform to process heterogeneous data including taxi data, social media data, and place data for tourist behavior analysis. We propose a data pipeline that can be scaled in order to process a significant amount of data regarding taxi trajectory and social media, with two objectives. The first objective is to extract the tourist trajectory data from the raw GPS data and produce a data integration module enriched with a knowledge base of tourist trajectories. This knowledge base is constructed through the extension of semantic trajectory ontology (STO) and mobility behavior ontology (MBO). The second objective is to extract tourist activities/point of interests (POIs) from geo-tagged Twitter data. The results of the data pipeline can readily be used for tourist behavior analysis, such as tourist descriptive analysis, popular tourist destinations/zones, and tourist movement patterns identification. We leverage the study’s results to demonstrate the real-life case study in Bangkok during the Songkran Festival in 2019. Thus, we could precisely identify tourist movement during various periods, determine popular destinations/zones, discover high density density of taxi destination points for a given trajectory type, and display the top ten tourist destinations, as well as prominent tourism keywords or trends at the time. This can provide insight to governments and businesses related to tourism regarding the trajectories and activities of tourists, and it will help predict future tourism trends.

The Design and Application of College English-Aided Teaching System Based on Web

With the rapid advancement of information technology, the Internet and the web have become the central technology tools for information sharing in education and teaching. Modern education combines teaching with information technology and gives full play to the role of information technology in education and teaching. To improve the resource sharing and scheduling level a web-based college English-assisted teaching system is presented in this study. Using the JavaScript object notation syntax (JSON) as the data communication format between the intelligent terminal and the server, the system is implemented using a web server and mobile Android terminal, and a web-based data integration module is designed. Statistical data mining and fuzzy feature detection are employed to implement the decision-making scheduling and self-adaptive optimization of English teaching resources in a web aided English teaching system. The fusion particle swarm optimization algorithm is used to integrate English teaching resources and implement adaptive scheduling. To verify the application performance of the designed system, a simulation experiment is designed. The experimental results show that the proposed English-assisted instruction system has good convergence and high accuracy of learning information, optimal allocation, and scheduling.

The Collaborative Metadata Repository (CoMetaR) Web App: Quantitative and Qualitative Usability Evaluation.

BackgroundIn the field of medicine and medical informatics, the importance of comprehensive metadata has long been recognized, and the composition of metadata has become its own field of profession and research. To ensure sustainable and meaningful metadata are maintained, standards and guidelines such as the FAIR (Findability, Accessibility, Interoperability, Reusability) principles have been published. The compilation and maintenance of metadata is performed by field experts supported by metadata management apps. The usability of these apps, for example, in terms of ease of use, efficiency, and error tolerance, crucially determines their benefit to those interested in the data.ObjectiveThis study aims to provide a metadata management app with high usability that assists scientists in compiling and using rich metadata. We aim to evaluate our recently developed interactive web app for our collaborative metadata repository (CoMetaR). This study reflects how real users perceive the app by assessing usability scores and explicit usability issues.MethodsWe evaluated the CoMetaR web app by measuring the usability of 3 modules: core module, provenance module, and data integration module. We defined 10 tasks in which users must acquire information specific to their user role. The participants were asked to complete the tasks in a live web meeting. We used the System Usability Scale questionnaire to measure the usability of the app. For qualitative analysis, we applied a modified think aloud method with the following thematic analysis and categorization into the ISO 9241-110 usability categories.ResultsA total of 12 individuals participated in the study. We found that over 97% (85/88) of all the tasks were completed successfully. We measured usability scores of 81, 81, and 72 for the 3 evaluated modules. The qualitative analysis resulted in 24 issues with the app.ConclusionsA usability score of 81 implies very good usability for the 2 modules, whereas a usability score of 72 still indicates acceptable usability for the third module. We identified 24 issues that serve as starting points for further development. Our method proved to be effective and efficient in terms of effort and outcome. It can be adapted to evaluate apps within the medical informatics field and potentially beyond.

SumGNN: multi-typed drug interaction prediction via efficient knowledge graph summarization.

Thanks to the increasing availability of drug-drug interactions (DDI) datasets and large biomedical knowledge graphs (KGs), accurate detection of adverse DDI using machine learning models becomes possible. However, it remains largely an open problem how to effectively utilize large and noisy biomedical KG for DDI detection. Due to its sheer size and amount of noise in KGs, it is often less beneficial to directly integrate KGs with other smaller but higher quality data (e.g. experimental data). Most of existing approaches ignore KGs altogether. Some tries to directly integrate KGs with other data via graph neural networks with limited success. Furthermore most previous works focus on binary DDI prediction whereas the multi-typed DDI pharmacological effect prediction is more meaningful but harder task. To fill the gaps, we propose a new method SumGNN: knowledge summarization graph neural network, which is enabled by a subgraph extraction module that can efficiently anchor on relevant subgraphs from a KG, a self-attention based subgraph summarization scheme to generate reasoning path within the subgraph, and a multi-channel knowledge and data integration module that utilizes massive external biomedical knowledge for significantly improved multi-typed DDI predictions. SumGNN outperforms the best baseline by up to 5.54%, and performance gain is particularly significant in low data relation types. In addition, SumGNN provides interpretable prediction via the generated reasoning paths for each prediction. The code is available in Supplementary Material. Supplementary data are available at Bioinformatics online.

Geological disaster monitoring and early warning system based on big data analysis

The existing geological disaster monitoring and early warning system has the problems of large monitoring errors and low accuracy. This paper designs a geological disaster monitoring and early warning system based on big data analysis. The geological disaster monitoring network module monitors the geological deformation and disaster inducing factors in real time through sensors and transmits them to the large-capacity data remote wireless transmission network module in the multi-dimensional heterogeneous monitoring data integration module through the Beidou satellite communication technology. This module uses a multi-sensor data fusion algorithm based on the D-S evidence theory and fuzzy mathematics to realize the integration of geological multi-dimensional heterogeneous monitoring data. Then, it uses cloud computing-based geological abnormal data mining method to mine abnormal geological information and transmit it to the early warning information release module. Geological disaster warning information will be released in the form of text messages and emails. Through verification, the system has high accuracy in monitoring and early warning of different types of geological disasters and provides a basis for timely and scientific deployment of geological disasters.

A Framework Uniting Ontology-Based Geodata Integration and Geovisual Analytics

In a variety of applications relying on geospatial data, getting insights into heterogeneous geodata sources is crucial for decision making, but often challenging. The reason is that it typically requires combining information coming from different sources via data integration techniques, and then making sense out of the combined data via sophisticated analysis methods. To address this challenge we rely on two well-established research areas: data integration and geovisual analytics, and propose to adopt an ontology-based approach to decouple the challenges of data access and analytics. Our framework consists of two modules centered around an ontology: (1) an ontology-based data integration (OBDI) module, in which mappings specify the relationship between the underlying data and a domain ontology; (2) a geovisual analytics (GeoVA) module, designed for the exploration of the integrated data, by explicitly making use of standard ontologies. In this framework, ontologies play a central role by providing a coherent view over the heterogeneous data, and by acting as a mediator for visual analysis tasks. We test our framework in a scenario for the investigation of the spatiotemporal patterns of meteorological and traffic data from several open data sources. Initial studies show that our approach is feasible for the exploration and understanding of heterogeneous geospatial data.

Design of Heterogeneous Data Integration and Sharing System for Coastal International Trade

Geng, D., 2020. Design of heterogeneous data integration and sharing system for coastal international trade. In: Yang, Y.; Mi, C.; Zhao, L., and Lam, S. (eds.), Global Topics and New Trends in Coastal Research: Port, Coastal and Ocean Engineering. Journal of Coastal Research, Special Issue No. 103, pp. 718–721. Coconut Creek (Florida), ISSN 0749-0208.After the establishment of several free trade zones and cross-border e-commerce comprehensive pilot zones along the coast of China, the volume of China's international trade has grown rapidly, but at the same time, the trade situation has become increasingly complex. In order to develop the international trade business better, the scientific formulation and timely adjustment of trade policy need to be carried out. Policy making and adjustment need to be based on the integration and sharing of massive data. In order to solve the problem that the performance of traditional heterogeneous data integration and sharing system is insufficient, a coastal international trade heterogeneous data integration and sharing system based on middleware and XML is designed. This system introduces the key technologies of system design, which are middleware and XML. Then a four-tier architecture based on middleware and XML is designed, and then the heterogeneous data integration module is analyzed, including mediator, wrapper and Web Services management. Finally, the system is implemented and tested. The results show that the system is superior to three traditional heterogeneous data integration and sharing systems in four aspects: query speed, accuracy, integration and whether it supports data structure, which proves that the performance of the system is stronger.

Data-Driven Prediction System of Dynamic People-Flow in Large Urban Network Using Cellular Probe Data

Cellular probe data, which is collected by cellular network operators, has emerged as a critical data source for human-trace inference in large-scale urban areas. However, because cellular probe data of individual mobile phone users is temporally and spatially sparse (unlike GPS data), few studies predicted people-flow using cellular probe data in real-time. In addition, it is hard to validate the prediction method at a large scale. This paper proposed a data-driven method for dynamic people-flow prediction, which contains four models. The first model is a cellular probe data preprocessing module, which removes the inaccurate and duplicated records of cellular data. The second module is a grid-based data transformation and data integration module, which is proposed to integrate multiple data sources, including transportation network data, point-of-interest data, and people movement inferred from real-time cellular probe data. The third module is a trip-chain based human-daily-trajectory generation module, which provides the base dataset for data-driven model validation. The fourth module is for dynamic people-flow prediction, which is developed based on an online inferring machine-learning model (random forest). The feasibility of dynamic people-flow prediction using real-time cellular probe data is investigated. The experimental result shows that the proposed people-flow prediction system could provide prediction precision of 76.8% and 70% for outbound and inbound people, respectively. This is much higher than the single-feature model, which provides prediction precision around 50%.

생산현장의 유연성 및 다양성을 지원하기 위한 설비정보 수집 시스템의 설계

The processes for manufacturing a product differ depending on the characteristics of the product, and the information used or generated by the processes also varies. To implement a flexible and configurable Manufacturing Execution System (MES), a Data Acquisition System (DAS) that takes into consideration the characteristics of the manufacturing system is required. In this study, we design an information acquisition system that can process the information on equipments of a shop floor in real-time and that is adaptive to the changes in the shop floor. The system has a data parser module for flexible processing of the equipment status, a data mapper module to link the equipment status with a manufacturing process, and an SOA-based data integration module to transmit the processed information to other information systems such as MES and ERP. From the results of pilot study, its maintenance is easy even if new equipment or new manufacturing processes are adopted or if the equipments are rearranged.

An incident information management framework based on data integration, data mining, and multi-criteria decision making

An effective incident information management system needs to deal with several challenges. It must support heterogeneous distributed incident data, allow decision makers (DMs) to detect anomalies and extract useful knowledge, assist DMs in evaluating the risks and selecting an appropriate alternative during an incident, and provide differentiated services to satisfy the requirements of different incident management phases. To address these challenges, this paper proposes an incident information management framework that consists of three major components. The first component is a high-level data integration module in which heterogeneous data sources are integrated and presented in a uniform format. The second component is a data mining module that uses data mining methods to identify useful patterns and presents a process to provide differentiated services for pre-incident and post-incident information management. The third component is a multi-criteria decision-making (MCDM) module that utilizes MCDM methods to assess the current situation, find the satisfactory solutions, and take appropriate responses in a timely manner. To validate the proposed framework, this paper conducts a case study on agrometeorological disasters that occurred in China between 1997 and 2001. The case study demonstrates that the combination of data mining and MCDM methods can provide objective and comprehensive assessments of incident risks.

CHOICE OF THE PROJECT DESIGN DATA INTEGRATION MODULE SORTING ALGORITHM OF A COMPUTER-AIDED DESIGN

Investigation of computer-aided design data module integration algorithms. The analysis of data sorting algorithms was realized. Algorithms for tasks sorting of project data integration module were suggested.