BONC: Consolidating the Foundation of City Data and Improving Data Application Capabilities
Smart City construction is driving urban industry development and creating urban vitality. City managers have reached a consensus on communicating, decision-making, managing, and innovating with data. They believe that a systematic and powerful urban big data center is a necessity in building a new type of Smart City with deep insights, efficient governance, industry prosperity, and enhanced public welfare.
New technologies — 5G, Internet of Things (IoT), big data, cloud computing, and Artificial Intelligence (AI) — are gaining popularity. This trend contributes to more convenient data acquisition methods, diverse data collection approaches, higher data reliability and security requirements, and various data sharing and application approaches. To keep pace, the urban big data center — featuring multi-department aggregation, cross-department sharing, and multi-layer application — needs to perform systematic construction and operations in the following aspects:
• Data resource planning: In addition to the sharing of government data and geographical data, it must consider the collection and aggregation of IoT, video networks, and Internet data, and continuously optimize the entire data architecture.
• Platform architecture construction: Consider storage flexibility during mass data collection, as well as processing engines and integration modes of different types of data, such as the real-time IoT stream data processing engine and the geographical big data processing engine.
• Data governance and control: Implement End-to-End (E2E) integrated management of data sources, warehouses, products, and services, as well as catalog and metadata management. Specifically, this covers the standards layer, data layer, metadata layer, data resource catalog, data mapping rule, quality audit rule, data handling process, and data tag management.
• Data analysis and mining: Focus on application objectives and themes to flexibly support ‘drag-and-drop’ data modeling capabilities.
• Data openness and sharing: Provide multiple data interfaces and data openness capabilities, including resource catalogs, data tags, data indicators, and themed applications. Smart Cities must also focus on opening up service interfaces (Application Programming Interfaces) and WeChat city services.
• Data services: When data center capabilities are used across departments and layers, Smart Cities must provide cloud-based data governance, analysis, mining, and data service provisioning capabilities.
Massive amounts of heterogeneous urban big data involve multiple departments, regions, and sources. Huawei connects Huawei Horizon Digital Platform to BONC’s government data sharing platform and basic geographic information sharing platform, enabling the two parties. This enhances data resource planning, cloud-based data governance, big data analysis and mining, and diverse data product and service capabilities, which has been verified in the IOC and government big data projects.
Based on the cloud, Huawei Horizon Digital Platform integrates new ICT and various types of data to connect the physical and digital worlds. The digital platform is the core of the digital foundation and enables data aggregation, data intelligence, and data-based operations.
• Data convergence: Integrates heterogeneous data from multiple sources to unify data and build a unified data foundation. Provides data planning and model building services oriented to industry scenarios to realize data value mining and sharing.
• Service collaboration: Streamlines applications and implements connection and collaboration across service systems, regions, and clouds through the Real-Time Open Multi-Cloud Agile (ROMA) platform. Achieves capability convergence, collaboration, and sharing through service-based ICT capabilities and service orchestration.
• Agile innovation: Offers an efficient application development environment and builds a development service platform for agile business development through ABC. Introduces innovative common applications, integrates and optimizes partners’ super applications, and invites partners to join the marketplace to form an innovative ecosystem.
• Inclusive AI and security: Provides inclusive AI services based on its full-stack, all-scenario AI capabilities. Device-cloud synergy and AI are used to defend against attacks and eliminate threats, ensuring reliable security. Multi-cloud management and disaster recovery backup support business continuity.
The joint solution has the following features:
• Embraces Huawei Horizon Digital Platform’s strengths in big data infrastructure, video cloud, and IoT platforms, in accordance with the characteristics of government and urban big data. Invokes BONC’s big data governance, management, analysis, and mining, as well as Business Intelligence (BI) tools to improve platform architecture and data governance and application capabilities.
• Adheres to the ‘data lake’ concept. Based on data governance engineering of the central database, it streamlines the relationship among the aggregation database, central database, basic database, specialized database, and shared database, to strengthen E2E urban data governance capabilities.
• Simplifies the management interfaces of major customers such as big data bureaus and data resource centers. Generates one unified diagram for big data operations management and monitoring, one diagram for data assets, and regularly produces data operation reports and data quality reports at different levels of the data warehouse, allowing users to intuitively manage and control complex data center operations.
• Expands big data analysis and mining capabilities. Builds data models and big data analysis models in drag-and-drop mode based on application requirements. Integrates data and algorithm models to meet decision-making and analysis requirements.
• Capitalizes on Huawei Horizon Digital Platform’s cloud capabilities to fulfill data governance requirements of cities and government departments and support cloud-based data governance, data cleansing, and governance audits.
• Supports data sharing among multiple departments, and zero-code data service release. Centrally manages data interface services in data marts, enhancing data service supply capabilities.
The construction of an urban big data center is a complex, long-term process that requires sustainability. Starting with the building of a data resource planning system, the project needs to plan the whole process from data aggregation to data supply, covering data source, collection, exchange, governance, mining, service, and application. In addition, the planning of the data source, data resource, platform, and application layers should be considered.
• Data source layer: The government data sharing platform and basic geographic public information service platform are two important sources of government data. This layer also accesses structured data of video networks, real-time stream data of city monitoring and security IoT networks, Internet public opinion data, mobile Internet location data, and consumption data. In addition, applications for urban fields should support the planning and accessing of new types of data sources.
• Data resource layer: Consider the classified/hierarchical planning and design of the data source, aggregation database, central database, basic database, specialized database, and shared database. Plan the data entity, metadata database, data resource catalog, indicator database, and tag database.
• Data platform layer: Based on the data access environment and conditions, centrally plan the big data infrastructure platform, Geographic Information System (GIS) data processing engine, video data structure engine, IoT data access and processing engine, unified data collection platform, urban big data governance platform, big data analysis and mining platform, and comprehensive urban big data display and BI platform.
• Data application layer: Plan and construct the indicator library, tag library, model library, and data service mart to support four applications: data sharing, data query and authentication, themed big data application, and data product mart (portal).
The urban big data center accesses complex and diverse types of data. To ensure that trustworthy data and services are offered to government departments, enterprises, and people, the center should establish an E2E data governance and management system spanning from data collection to data supply. The following aspects need to be considered: standards and specifications system, data governance system, data governance engineering, and data management.
• Standards and specifications system establishment: Standards and specifications are the premise of constructing an urban big data center. A set of standards, specifications, and management regulations is required, in compliance with national and industry standards. The process for creating the specification includes streamlining, development, verification, review, release, and update. It involves data source interface specifications, metadata specifications, data resource catalog specifications, database design specifications, data governance rules, and data service interface specifications. Data management mechanisms and regulations are also included.
• Data governance system establishment: Establish a comprehensive data governance and management system for all data managed by the city big data center, to enable data model standardization, relationship clarification, processing visualization, quality measurement, and service automation. Using metadata management tools as the core, a data governance system is constructed to manage metadata, resource catalogs, data handling processes, and data work orders in a closed-loop.
• Data governance engineering service: To ensure data from the urban big data center is high-quality and authoritative, each government department governs its own data and checks its quality. The whole process begins with defining the data scope based on service requirements, analyzing the data source access environment and informatization environment, and evaluating original data quality and detecting issues. The next step is to specify data quality audit rules, data integration processing rules, and their mapping relationships. Then the service rules are converted into technical rules and processes, and they then execute data processing and audit tasks. The next task is to monitor task execution results, and analyze and assess issues. Problematic data is then sent back to government departments for improvement. Government departments optimize data handling processes based on issues detected and audit reports. Finally, data service products are developed to meet data sharing and openness requirements — and released with ‘zero code.’
• Data management system establishment and improvement: The role-based management view is provided for data managers, data handlers, and department users. Data managers need a unified diagram that displays data exchange and aggregation, data processing, quality audit, work orders, overall running status of data services and release, and center-wide data asset reports. For data handlers, the unified view should present data handling process reports, quality audit reports, metadata-based data object query, basic information, handling process, lineage, and quality reports of data objects, as well as data service interface status. For government department users, the management view should contain the data source interface catalog, subscribed data service interface catalog, cloud-based data governance platform, and data service mart.
Big data technologies are widely applied to analyze and predict urban issues, as well as assess a city’s operating status and policy effects. For example, mobile location data can help monitor, analyze, and predict urban foot traffic and logistics; land and real estate price data is used to predict and evaluate the economic vitality of urban real estate; and environmental monitoring data supports urban environment quality analysis and assessment. Considering these analysis, prediction, mining, and application requirements in urban management and governance, cities should build an on-demand big data analysis and mining system.
Focusing on urban and government applications, the system should provide data analysis, mining, and modeling capabilities such as data statistics, indicator analysis, tag analysis, tag profiling, model exploration, algorithm application, themed analysis, and analysis reports. The system construction project consists of three aspects: indicator and model library construction, cloud-based analysis and mining platform (AI platform) construction, as well as model visualization and application.
• Indicator and model library construction: Construct the indicator library, tag library, algorithm library, model library, themed application template library, and analysis report template library based on urban management and government application themes.
• Cloud-based analysis and mining platform: Provide a cloud tenant service mode for government departments and users at all levels, allowing them to use operators, algorithms, modeling, calculation, and visualization tools and capabilities on the analysis and mining platform. The platform supports visualized analysis model creation in drag-and-drop mode, and provides basic, collaborative, and closed-loop modeling and process management regulations. The regulations apply to data preparation, access, and processing, as well as model creation, training, evaluation, inference, deployment, go-online, and cloudification services.
• Model visualization and application: Visualized model orchestration enables users to manage and control the process of creating segment-based or exploratory models, and visualizes model analysis results using charts, dashboards, maps, and heat maps.
Portal-based data sharing and service interfaces should be provided by the urban big data center. Moreover, multiple important data services are interconnected, such as the city app service, themed large-screen display service, and authorized credit data query service. Therefore, building a cloud-based, systematic data service portal based on Huawei Horizon Digital Platform is the key to data center vitality and sustainability. This involves a unified data service portal and background management of the data service mart.
• Unified data service portal: Provide government departments, enterprises, and residents with a multi-level and multi-way data service portal involving data service portals, data service apps, and WeChat official accounts. This unified portal offers the following functions: data exchange and download, data service interfaces, tag-based data sharing, themed large-screen display, connection to city apps, and authorized credit data query service.
• Data service mart: Implement integrated background management for flexible data services, including data query, data service and product interface registration, service management, resource scheduling, service usage monitoring, as well as data service measurement and charging.
Huawei and BONC enable each other using Horizon and big data capabilities. Together, they develop joint solutions, install a big-data-based city ‘brain’ in the IOC, and support government big data platform projects by offering data engineering services such as data collection, central database construction, full-process data governance, and themed large-screen data interfaces. The joint solution has been deployed in more than 20 projects across Beijing, Shanghai, Shenzhen, Tianjin, and Shandong.
Huawei is committed to building a prosperous digital economy ecosystem and supporting Smart City construction in China. As a strong partner of Huawei’s ‘Robust Ecosystem Program,’ BONC has collaborated extensively with Huawei. The two parties have worked together to innovate and to enable each other in fields such as Smart City and big data. Together, they will make a great contribution to China’s Smart City construction — changing people’s lives with data and creating a better future