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Understanding Partnering Strategies in the Low-Code Platform Ecosystem 58

Understanding Partnering Strategies in the Low-Code Platform...

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58th Hawaii international conference on System Sciences, HICSS 2025

作者： Naqvi, Syed Asad Ali Zimmer, Markus P. Drews, Paul Basole, Rahul C. Accenture Ecosystem Leuphana University of Lüneburg Germany Accenture Data & AI

ISBN: (纸本)9780998133188

Low-code platforms (LCPs) allow the development, testing, and deployment of software applications with minimal manual coding by using pre-built software components and visual design tools. LCPs frequently enter into technology partnerships to offer extensive capabilities required for software development. In this study, we identify and characterize these technology partnerships to understand and develop the structure of the LCP ecosystem. We model the LCP ecosystem and identify nine types of technology partnerships. In doing so, we advance our understanding in three key areas: (1) provide insight into the evolving LCP ecosystem, (2) reveal that LCP vendors use various technological partnerships and strategies across different capability areas with varying partner network sizes and capability compositions, and (3) demonstrate the value of ecosystem visualization. Collectively, our findings can help LCP users and developers understand the foundational, competitive, and niche partnership strategies and enable LCP vendors to innovate and collaborate effectively. © 2025 IEEE Computer Society. All rights reserved.

关键词： visualization

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data Quality in Laboratory Indicators and Tuberculosis Control: A Case Study from the State of Sao Paulo 16

Data Quality in Laboratory Indicators and Tuberculosis Contr...

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16th international conference on ENTERprise Information Systems, CENTERIS 2024 - 12th international conference on Project MANagement, ProjMAN 2024 - 14th international conference on Health and Social Care Information Systems and Technologies, HCist 2024

作者： Annelli, Guilherme Lourenço Bernardi, Filipe Baiochi, João Francisco Lima, Vinícius Scarpa, Nicolas Silva, Érica Chimara Alves, Domingos Ribeirão Preto Medical School University of São Paulo Ribeirão Preto Brazil University Center of Barão de Mauá Ribeirão Preto Brazil Institute Adolfo Lutz Center São Paulo Brazil

Tuberculosis remains the leading cause of death from infectious bacterial diseases globally, as recognized by the World Health Organization. This study explores the implementation of digital health strategies aimed at improving data quality in laboratory indicators and TB control within São Paulo, Brazil. It identifies opportunities for enhancing laboratory processes, data integrity, and disease management. A socio-Technical approach was employed, fostering collaboration between system users and technical developers. data were collected via the REDCap platform and evaluated on dimensions of accuracy, completeness, consistency, and timeliness. Analytical tools included Python libraries, the REDbox framework, and Apache Superset for data analysis, verification, validation, and visualization. The study involved 167 local laboratories from 13 Regional Laboratory Centers in São Paulo State. This research underscores the critical role of data quality in health surveillance and effective TB control. Future efforts should focus on enhancing training programs, improving system usability, and leveraging advanced data analytics tools to support ongoing improvements in TB control. © 2025 The Author(s).

关键词： data quality

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Deep Multi-time visualization and analytics for E-commerce Platform 4th

Deep Multi-time Visualization and Analytics for E-commerce P...

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4th international conference on Advanced Network Technologies and Intelligent Computing-ANTIC-Annual

作者： Bhatnagar, Jay R. Iyer, Ravi Prakash Rawat, Krishna Tripathi, Ankit Bhatnagar, Akhil Goyal, Dhruv GEU Univ Dehra Dun 248002 Uttarakhand India Krea Univ Sricity 517646 Andhra Pradesh India IT FUZONe Syst Noida 110096 Uttar Pradesh India

ISBN: (纸本)9783031837821;9783031837838

The Online Rating visualization (ORV) is a widely used tool along with text reviews that offer business intelligence about product performance to prospective e-buyers. The rapid growth in e-business environment has led to an increasing trend to host products and related analytics for prospective consumers in a manner that is informative, credible, and fair. Surprisingly, there is not much prior research done in making the ORV productive and informative. In this paper, we formulate a deep-time resolved view of buyer sentiment analysis called as DBRV (Deep-Bin Rating-Vote visualization) based on equi-length time slices of ratings-votes. We also propose a novel performance measure called Popularity index (P) which combines ratings-votes meaningfully in time differential manner to depict discriminant power of the DBRV. Simulations performed using real data of ratings-votes for iPhone 12 and Earbuds with 4139 and 8497 reviews respectively, shows that DBRV offers more informative insights about consumer experience than average ratings-votes. The proposed DBRV-1 depicts time-binning of ratings-votes distribution thereby illustrating its information resolved into timebins. Likewise, DBRV-2 powered by P conveys higher intelligence about perceived product performance than existing ORV. Finally, we also formulate a more effective and novel user-interactive dashboard, shown for iPhone 12 and Earbuds, that offers a promising frontend analytics for prospective customers.

关键词： E-commerce Ratings Consumer analytics Deep-Time Binning Popularity index

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Second international conference on Big data, Computational Intelligence, and Applications, BDCIA 2024

Second International Conference on Big Data, Computational I...

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2nd international conference on Big data, Computational Intelligence, and Applications, BDCIA 2024

ISBN: (纸本)9781510689053

The proceedings contain 161 papers. The topics discussed include: intelligent connected vehicle data security based on blockchain combining zero-knowledge proof and post-quantum cryptography;highway service area traffic forecasting based on multi-domain mixed temporal data pre-training;analysis on the distribution of the economy hotel guest flow in Wuhan based on the data of guest's check-in time;visual analysis of Liaoning province tourism spots data based on python in English context;application and research of particle swarm optimization algorithm in cloud data backup;construction and realization of modern Chinese vocabulary base driven by big data;research on financial data trend prediction based on deep learning;and development of a Python-based comprehensive employment data visualization platform.

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Automating Aeronautical Information Management: A Geospatial Big data Approach in India

Automating Aeronautical Information Management: A Geospatial...

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2025 Integrated Communications, Navigation and Surveillance conference, ICNS 2025

作者： Lal, Arvind V. Satish, S.V. Rohilla, Yogendra Mittal, Sandeep Bhuvanamitra, S. SatSure Analytics India Private Limited Bangalore India Airports Authority of India New Delhi India

ISBN: (纸本)9798331534738

The Indian National Electronic Terrain Repository for Aviation, or iNetra, is a comprehensive geospatial big data platform designed to revolutionize aeronautical data management for the Airports Authority of India (AAI), which is the airport developer and sole Air Navigation Service Provider in India. SatSure developed this multi-module system, which helps transition from traditional paper-based processes to an automated, digital workflow aligned with international Civil Aviation Organization (ICAO) standards and recommended practices. The platform integrates advanced technologies, including high-resolution satellite imagery, PostgreSQL database management, and Geographic Information System (GIS) functionalities, for efficient data acquisition, processing, storage, and visualization. Some of the key modules include Electronic Terrain and Obstacle data (eTOD), Aerodrome Mapping database (AMDB), and Electronic Charting (eCharting), deployed across five international airports in India. iNetra addresses critical challenges in aeronautical data management by providing dynamic, time-stamped, and validated data ingestion capabilities. The global standards which the system upholds are supported by strong data security protocols, such as a 32-bit Cyclic Redundancy Check. Seamless data exchange, removal of redundant operations, and the support for transforming an Aeronautical Information Service into an Aeronautical Information Management and thus make iNetra mark a technological frontier in the evolution of aviation information management in India. © 2025 IEEE.

关键词： Information management

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Construction of Fusion visualization Platform for Meteorological Station and Monitoring Application Scenarios 3rd

Construction of Fusion Visualization Platform for Meteorolog...

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3rd international conference on Machine Learning, Cloud Computing and Intelligent Mining, MLCCIM 2024

作者： He, Tingting Jiang, Yuantong Hao, Panlin Zheng, Faling He, Yuanrong School of Cultural Industries and Tourism Xiamen University of Technology Xiamen China College of Computer and Information Engineering Xiamen University of Technology Xiamen China Technology Co. Ltd. Xiamen China

ISBN: (纸本)9789819624676

With the continuous development of real-world 3D, the visualization of multi-source real 3D data fusion is significant for digital twins. Based on SketchUp, this study aims to utilize advanced laser point cloud scanning technology to conduct component-level realistic 3D modeling and to construct a high-precision movable meteorological station-based realistic 3D model. Meanwhile, by utilizing the WebGL architecture of a real-time 3D model visualization platform, the meteorological station model is integrated into the large model of the monitoring scene, achieving functions such as browsing, attribute querying, rotating, scaling, dismantling, explosion, and more flexible interaction and spatial analysis of the movable meteorological station 3D model. The real-time 3D model of this study can not only provide intuitive and comprehensive spatial information support for the design, deployment, and operation of meteorological stations, but also provide a visual foundation for environmental monitoring, disaster response, and other fields. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： data fusion

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Developing Self-Adaptive data analytics Models for Personalized visual Insights in Education

Developing Self-Adaptive Data Analytics Models for Personali...

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international conference on Inventive Computation Technologies (ICICT)

作者： S. Sangeetha P. Sumathi Sakthidevi. I Akshaya. C G. Priyanga P. Vanathi Dept of IT Adhiyamaan College of Engineering Hosur India Dept of CSE Sri Sairam college of engineering Bengaluru India Dept of CSE Adhiyamaan college of Engineering Hosur India Dept of CSE M.Kumarasamy College of Engg Karur India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

Educational systems are undergoing a transformation to meet the diverse learning needs of students. Traditional one-size-fits-all teaching approaches often fail to address individual differences in learning styles, pace, and preferences. The integration of data analytics and visualization techniques offers opportunities to enhance personalization in education. By leveraging advanced machine learning and visualization models, tailored learning experiences can be designed, leading to improved engagement and performance. This research introduces the visualization-Driven Sequential Temporal analytics Model (VISTEM), a novel hybrid algorithm that combines t-Distributed Stochastic Neighbour Embedding (t-SNE) and Long Short-Term Memory (LSTM) networks. VISTEM is designed to analyze sequential student learning patterns and provide adaptive, personalized visual insights to enhance the educational experience. The proposed model integrates real-time data analysis with advanced visualization techniques, enabling educators and stakeholders to identify knowledge gaps, predict performance, and tailor learning strategies. To validate its effectiveness, VISTEM is compared against existing models such as Recurrent Neural Networks, Principal Component Analysis and K-Means Clustering using simulation metrics such as prediction accuracy, training time and engagement level prediction. Simulation results demonstrate that VISTEM outperforms traditional algorithms in terms of sequential pattern detection, real-time visualization responsiveness, and interpretability. The integration of interactive visual elements provides enhanced insights into student behaviour, facilitating data-driven decision-making in education. This study underscores the potential of hybrid algorithms in creating scalable, efficient, and adaptive data analytics systems tailored for modern educational environments.

关键词： Analytical models Adaptation models visualization data analysis Machine learning algorithms data visualization Machine learning Predictive models Prediction algorithms Real-time systems

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CRICKETANALYZER: A SMART CAMERA-BASED SYSTEM FOR ACCURATE BALL SPEED DETECTION IN CRICKET

CRICKETANALYZER: A SMART CAMERA-BASED SYSTEM FOR ACCURATE BA...

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2025 international conference on Energy, Power, Environment, Control and Computing, ICEPECC 2025

作者： Hussain, Tassadaq Haider, Amna Haris, Muhammad Center for Ai and Big Data Namal University Mianwali Pakistan Pakistan Supercomputing UCERD Islamabad Pakistan

ISBN: (纸本)9781837243235

Accurate and real-time speed detection of moving objects, such as sports balls, is crucial for sports analytics and performance evaluation. Traditional systems, such as radar guns, are used to detect object speed, but they face issues such as high costs, limited flexibility, and dependency on controlled environments. In this work, we introduce an advanced visual camera-based ball speed detection application, CricketAnalyzer. CricketAnalyzer uses the Yolov11 model for object detection and real-time motion analysis. The CricketAnalyzer identifies the ball and calculates its velocity in various scenarios. The CricketAnalyzer application is deployed on the PakSupercomputing cluster, offering a cost-effective, scalable, and highly accurate solution. It also serves as a potential tool for sports analytics and broader use cases. © The Institution of Engineering & Technology 2025.

关键词： COMPUTER VISION CRICKET ANALYZER PERFORMANCE EVALUATION SPEED DETECTION

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Designing Actionable and Interpretable analytics Indicators for Improving Feedback in AI-Based Systems 17

Designing Actionable and Interpretable Analytics Indicators ...

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17th international conference on Computer Supported Education, CSEDU 2025

作者： Félix, Esther De Oliveira, Elaine Ramos, Ilmara M.M. Perez-Sanagustin, Mar Villalobos, Esteban Hilliger, Isabel Mello, Rafael Ferreira Broisin, Julien Universite de Toulouse IRIT CNRS France Universidade Federal do Amazonas Brazil Pontificia Universidad Católica de Chile Chile CESAR Brazil

ISBN: (纸本)9789897587467

In AI-based educational systems, transparency and understandability are particularly important to ensure reliable human-AI interaction. This paper contributes to the ongoing research on developing analytics for AI-based educational systems by delivering feedback throughout indicators that learners can easily interpret and act upon during their studies. Specifically, this paper introduces a mixed methods study that examines the types of indicators that ought to be incorporated into the feedback offered by an AI-based system designed to help students develop competencies in programming. Building upon prior work in Human-Centered Design, the card sorting technique was used to collect both qualitative and quantitative data from 31 Computer Science students. We created 16 cards that presented students with different indicators to explain the reasoning behind the system’s decisions and feedback. These indicators were displayed in different formats (visual and textual representations;temporal vs. non-temporal and social vs. non-social reference frames). Our goal was to discover the most interpretable and actionable method for delivering feedback to learners. Our study found low consensus among students. Overall, students found indicators based on social comparison to be less actionable and interpretable compared to those without;and textual indicators were perceived as less actionable and interpretable than visual ones. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： User centered design

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6th international conference on Cyber Security Intelligence and analytics, CSIA 2024

6th International Conference on Cyber Security Intelligence ...

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6th international conference on Cyber Security Intelligence and analytics, CSIA 2024

ISBN: (纸本)9783031882869

The proceedings contain 56 papers. The special focus in this conference is on Cyber Security Intelligence and analytics. The topics include: Comparative Study of ARIMA Model and Long Short Term Memory Network (LSTM) in Economic Management;the Application and Empirical Study of Causality in the Theory Graph;research on Machining and Simulation Optimization System of Automobile Steering Knuckle Based on Advanced Algorithm;network Partitioning and Demand Characterization for Management of Urban Low Voltage Power Distribution Systems;automation and data Aggregation Algorithm for Low Code Development in Power Grid Mobile Report Generation;big data Analysis and Smart Grid Security Event Monitoring and Response in the Power Internet of Things;Construction of an Online Autonomous Learning Model Based on Artificial Intelligence ChatGPT;Application of PMS Graphic Intelligent Recognition and Analysis Based on Contact Diagram Automatic Generation Technology;application of Genetic Algorithm in Reasonableness Evaluation of Environmental Design Space Layout;construction of an Intelligent Recommendation Model for Digital Media Content Based on Artificial Intelligence;research on Link Selection and Allocation for IoT Localization Systems Based on an Improved Ant Colony Algorithm;cross-Domain Sharing and Privacy Protection Method of Fused Multi-source data in visual Internet of Things;construction of Cold Chain Logistics and Distribution Site Selection System Based on Multi-objective Optimization Model;hole Detection Algorithm Based on Channel Fusion Siamese Network;intelligent database Triggers Enable Advanced Analysis of data Recorded in Audit Logs;improvement of Principal Component Analysis Algorithm and Its Simulation Experiment;OLAP Technology Financial Statistics Information Platform Based on Big data Analysis.

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