[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2413":3,"related-2413":63},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":18,"tags":20,"primary_topic":9,"secondary_topics":25,"classification":26,"search_phrases":43,"slug":46,"view_count":15,"doi":47,"paper":48,"created_at":62},2413,"Predictive and Conceptual Models for KPI Dashboard Integration and Multi-Vendor Risk Monitoring in Government Systems","https:\u002F\u002Fdoi.org\u002F10.56201\u002Fijcsmt.vol.12.no3.2026.pg263.294","Government institutions increasingly rely on complex digital ecosystems involving multiple technology vendors, outsourced service providers, and distributed information systems. While these environments enable scalability and operational efficiency, they also introduce significant risks related to performance monitoring, accountability, and vendor coordination. Key Performance Indicator (KPI) dashboards have emerged as essential tools for real-time monitoring and decision support; however, many government implementations remain fragmented, lacking integrated predictive capabilities and standardized risk monitoring frameworks across multiple vendors. This review paper examines existing predictive and conceptual models designed to support KPI dashboard integration and multi-vendor risk monitoring in government information systems. The study synthesizes literature on performance analytics, risk management architectures, business intelligence dashboards, and predictive modeling techniques applied to public-sector digital governance. Particular attention is given to machine learning–driven forecasting models, data integration frameworks, and conceptual governance architectures that support continuous monitoring of vendor performance, service-level compliance, and operational risks. The review further explores how integrated dashboards can consolidate heterogeneous data streams from procurement systems, contract management platforms, cybersecurity monitoring tools, and operational service metrics. By analyzing existing approaches, the paper proposes a structured conceptual model that links predictive analytics with dashboard visualization layers to enhance transparency, accountability, and proactive risk mitigation in government technology ecosystems. The findings highlight the importance of interoperable data architectures, standardized KPI frameworks, and predictive risk scoring mechanisms for improving oversight of multi-vendor environments. Ultimately, this review contributes to the development of more resilient, data-driven governance models capable of supporting complex government digital infrastructures.","政府机构日益依赖由多个技术供应商、外包服务提供商和分布式信息系统构成的复杂数字生态系统。尽管这些环境能够实现可扩展性和运营效率，但也带来了与绩效监测、问责制和供应商协调相关的重大风险。关键绩效指标（KPI）仪表盘已成为实时监测和决策支持的重要工具；然而，许多政府实施仍然较为分散，缺乏跨多个供应商的集成预测能力和标准化风险监测框架。本文综述了旨在支持政府信息系统中KPI仪表盘集成和多供应商风险监测的现有预测模型和概念模型。本研究综合了应用于公共部门数字治理的绩效分析、风险管理架构、商业智能仪表盘和预测建模技术方面的文献。研究特别关注机器学习驱动的预测模型、数据集成框架以及支持持续监测供应商绩效、服务水平合规性和运营风险的概念治理架构。本文进一步探讨了集成仪表盘如何整合来自采购系统、合同管理平台、网络安全监测工具和运营服务指标的异构数据流。通过分析现有方法，本文提出了一个结构化概念模型，将预测分析与仪表盘可视化层相连接，以增强政府技术生态系统中的透明度、问责制和主动风险缓解能力。研究结果强调了可互操作的数据架构、标准化KPI框架和预测性风险评分机制对于改善多供应商环境监督的重要性。最终，本综述有助于开发更具韧性、数据驱动的治理模型，以支持复杂的政府数字基础设施。",null,"INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND MATHEMATICAL THEORY E-ISSN","2026-09-11T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"主题为政府信息系统KPI仪表盘与多供应商风险监控，与三农、农业信息化、智慧农业、数字乡村无直接关联，相关性门槛未通过。",[19],{"name":10,"url":6},[21,22,23,24],"政府信息化","KPI仪表盘","多供应商风险","预测分析",[],{"status":27,"locked":14,"version":28,"method":29,"evidence":30},"needs_review","2026-10-22.2","rules",{"candidates":31,"agricultural_evidence":14,"reason":42},[32],{"code":33,"score":34,"supported":35,"title":36,"abstract":37,"tags":41},"I2",9,true,[],[38,39,40],"机器学习","machine learning","decision support",[],"农业相关性需要复核",[44,45],"多供应商风险 政府信息化 预测分析 KPI仪表盘","多供应商风险 政府信息化","多供应商风险政府信息化预测分析KPI仪表盘-2413","10.56201\u002Fijcsmt.vol.12.no3.2026.pg263.294",{"doi":47,"openalex_id":49,"authors":50,"venue":10,"cited_by_count":15,"oa_url":53,"card":54,"direction":60,"ingested_from":61},"W7212218080",[51],{"name":52,"orcid":9},"Peter Chibwaye Irene","https:\u002F\u002Fiiardjournals.org\u002Fget\u002FIJCSMT\u002FVOL. 12 NO. 3 2026\u002FPredictive and Conceptual Models for KPI 263-294.pdf",{"tldr":55,"method":56,"finding":57,"direction":58,"opportunity":59},"综述政府KPI仪表盘集成与多供应商风险监控的预测与概念模型。","文献综述，综合绩效分析、风险管理架构、商业智能与机器学习预测模型。","提出连接预测分析与仪表盘可视化的概念模型，强调互操作数据架构与标准化KPI。","数字乡村与农业信息化","可将政府多供应商风险监控框架迁移至农业信息化项目，研究涉农多主体数据集成与KPI预警。","智慧农业 \u002F 农业物联网","openalex","2026-09-14T23:30:09.787050Z",{"total":64,"page":64,"page_size":64,"items":65},1,[66],{"id":67,"title":68,"url":69,"summary":70,"summary_zh":71,"content":9,"source_name":72,"source_url":69,"published_at":73,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":74,"sources":76,"tags":78,"primary_topic":9,"secondary_topics":82,"classification":83,"search_phrases":92,"slug":95,"view_count":96,"doi":97,"paper":98,"created_at":118},3545,"AI-POWERED PERSONAL FINANANCIAL HEALTH AND PREDICTIVE DECISION SUPPORT DASHBOARD","https:\u002F\u002Fdoi.org\u002F10.62643\u002Fijerst.2026.v22.n3.4661","The AI-Powered Personal Financial Health and Predictive Decision Support Dashboard is an intelligent financial-management system designed to help individuals understand their financial position and make better-informed personal financial decisions. Managing income, expenses, savings, budgets, investments, and financial goals can become difficult when information is distributed across different accounts and records. The proposed dashboard brings important financial information together in a single interface. The system uses Artificial Intelligence, Machine Learning, data analytics, predictive analysis, and visualization techniques to analyze personal financial data. Users can provide information about income, expenses, savings, budgets, recurring payments, financial goals, and other relevant transactions. The system processes this information to generate a structured view of the users financial activities. A financial analytics module categorizes transactions and identifies spending patterns, income trends, recurring expenses, savings behavior, and budget utilization. The system can generate financial-health indicators based on clearly defined metrics such as spending-to-income ratio, savings rate, budget adherence, and emergency-fund progress. These indicators help users understand their current financial situation. The predictive component can analyze historical financial patterns to estimate future cash flow, upcoming expenses, and potential budget deviations. An AI-based decisionsupport layer can provide explanations and scenario analysis, such as showing how changes in monthly spending could affect savings over time. Predictions are presented as estimates based on available historical data rather than guaranteed financial outcomes. Overall, the proposed system provides a centralized dashboard for financial monitoring, trend analysis, budgeting, predictive insights, and goal tracking. It can be useful for students, employees, families, and individuals who want to organize their personal finances. Future enhancements can include secure bank-data integration, advanced anomaly detection, personalized financial education, scenario simulation, multilingual assistance, and improved forecasting models.","AI驱动的个人财务健康与预测性决策支持仪表盘是一种智能财务管理系统，旨在帮助个人了解自身财务状况并做出更为明智的个人财务决策。当信息分散在不同账户和记录中时，管理收入、支出、储蓄、预算、投资和财务目标可能变得困难。所提出的仪表盘将重要财务信息整合到一个单一界面中。该系统利用人工智能（Artificial Intelligence）、机器学习（Machine Learning）、数据分析、预测分析和可视化技术来分析个人财务数据。用户可以提供有关收入、支出、储蓄、预算、周期性付款、财务目标及其他相关交易的信息。系统处理这些信息，以生成用户财务活动的结构化视图。财务分析模块对交易进行分类，并识别支出模式、收入趋势、周期性支出、储蓄行为和预算使用情况。系统可以基于明确界定的指标生成财务健康指标，例如支出收入比、储蓄率、预算遵守情况和应急基金进展。这些指标帮助用户了解其当前财务状况。预测组件可以分析历史财务模式，以估算未来现金流、即将发生的支出和潜在预算偏差。基于AI的决策支持层可以提供解释和情景分析，例如展示月度支出变化可能如何随时间影响储蓄。预测以基于可用历史数据的估计形式呈现，而非有保证的财务结果。总体而言，所提出的系统提供了一个集中式仪表盘，用于财务监控、趋势分析、预算编制、预测性洞察和目标跟踪。它可以对学生、员工、家庭以及希望整理个人财务的个人有所帮助。未来的增强功能可以包括安全的银行数据集成、高级异常检测、个性化金融教育、情景模拟、多语言辅助以及改进的预测模型。","International Journal of Engineering Research and Science & Technology","2026-09-24T00:00:00Z",{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":75},"该论文聚焦个人财务健康与AI预测决策支持，与三农、农业信息化、智慧农业、数字乡村等主题无关，不建议进入每日精选。",[77],{"name":72,"url":69},[79,80,24,81],"金融科技","决策支持","个人理财",[],{"status":27,"locked":14,"version":28,"method":29,"evidence":84},{"candidates":85,"agricultural_evidence":14,"reason":42},[86],{"code":33,"score":87,"supported":35,"title":88,"abstract":89,"tags":91},18,[40],[38,39,90,40],"artificial intelligence",[80],[93,94],"个人理财 决策支持 金融科技 预测分析","个人理财 决策支持","个人理财决策支持金融科技预测分析-3545",3,"10.62643\u002Fijerst.2026.v22.n3.4661",{"doi":97,"openalex_id":99,"authors":100,"venue":72,"cited_by_count":15,"oa_url":111,"card":112,"direction":60,"ingested_from":61},"W7214244276",[101,103,105,107,109],{"name":102,"orcid":9},"K Madhunika",{"name":104,"orcid":9},"G Laxmi",{"name":106,"orcid":9},"J Shiva Kumar",{"name":108,"orcid":9},"M Sai Ram",{"name":110,"orcid":9},"D Madhavan","https:\u002F\u002Fijerst.org\u002Findex.php\u002Fijerst\u002Farticle\u002Fdownload\u002F4661\u002F4303",{"tldr":113,"method":114,"finding":115,"direction":116,"opportunity":117},"构建AI个人财务健康仪表盘，整合收支并预测现金流与预算偏差。","用AI、机器学习、数据分析和可视化分析个人财务数据。","系统能生成财务健康指标并预测未来现金流和预算偏差。","其他","可迁移其预测与决策支持框架至农业经营主体财务健康管理场景。","2026-09-26T23:30:12.700014Z"]