Data storytelling

Data storytelling transforms complex data into easily understandable narratives through visualization tools like ECharts. As a powerful open-source visualization library, ECharts offers a rich variety of chart types and interactive features, helping developers build data-driven stories. Its core strengths lie in flexibility and extensibility, supporting multiple chart types and allowing deep customization. Data storytelling requires filtering and cleaning data, followed by selecting appropriate chart types. Advanced techniques include multi-chart linkage and timeline animations. Interactive design encompasses tooltip customization and data drilling. Performance optimization involves handling large datasets and on-demand rendering. Theme and style customization supports custom themes and responsive design. Practical applications include sales data analysis and user behavior path analysis.

Real-time monitoring system

A real-time monitoring system needs to meet core functionalities such as high-frequency data updates, visual display, and anomaly alerts. It typically interfaces with various sensors, log files, or API interfaces to fetch data at second-level or even millisecond-level frequencies. ECharts, as a visualization library, is suitable for building real-time monitoring interfaces. Dynamic data updates are achieved through the `setOption` method, offering a more concise API compared to lower-level libraries, with built-in components directly applicable to monitoring scenarios. The system design employs a combination of communication solutions like WebSocket, SSE, and long polling. For large data volumes, performance is optimized through data sampling, rendering control, and Web Workers. Anomaly detection utilizes threshold alerts, dynamic baselines, and machine learning algorithms. Multi-view collaborative analysis is enabled by establishing chart associations via the `connect` method. Mobile optimization requires responsive layouts and touch-friendly interactions. Historical data is stored and replayed using IndexedDB. Themes are customizable, supporting dark mode. Security considerations include data encryption, access control, and operation auditing.

Operational data monitoring

Operational data monitoring requires focusing on core metrics such as user activity, retention rate, conversion rate, and revenue indicators, with varying priorities across different business areas. ECharts, as a data visualization tool, offers significant advantages by supporting a wide range of chart types and interactive features, making it suitable for building real-time monitoring dashboards. It enables data updates via WebSocket and facilitates multi-dimensional data analysis, such as multi-Y-axis configurations and data drilling. The tool also provides mechanisms for abnormal data alerts, including mark lines and visual mapping. For mobile adaptation, solutions like responsive layouts and gesture interactions are considered. ECharts supports chart linkage and data drilling, and when handling large datasets, techniques like data sampling and progressive rendering can be employed. Additionally, ECharts allows for custom themes and styles to meet the needs of diverse operational scenarios.

User behavior analysis

User behavior analysis is the process of collecting, processing, and analyzing user interaction data in digital products to understand user habits, preferences, and needs. In web applications, ECharts, as a powerful data visualization library, transforms complex user behavior data into intuitive charts. Common behaviors include page views, clicks, scrolling, dwell time, and other metrics collected through front-end tracking technology, which are then cleaned and transformed for analysis. ECharts offers various chart types, such as heatmaps, Sankey diagrams, and line charts, to present different analytical scenarios, including user paths, retention, feature usage, and cohort comparisons. Advanced techniques involve funnel analysis, user segmentation, event sequence clustering, and more. For handling large-scale data, optimization strategies like data sampling, incremental rendering, WebWorker, and data aggregation should be considered. Interactive features include data drill-down, dynamic filtering, time range selection, and data export. On mobile devices, responsive design, touch interaction, performance optimization, and offline caching must be adapted. Data security and privacy protection are also critical considerations. Future trends include real-time analysis, AI integration, cross-platform analytics, augmented reality, and more.

Product comparison visualization

Product comparison visualization uses graphical displays to showcase product parameter differences, helping users quickly identify strengths and weaknesses. ECharts, as a powerful visualization library, is well-suited for implementing dynamic, multi-dimensional comparison scenarios. Basic comparisons include bar charts and radar charts, while advanced techniques involve parallel coordinates and heatmap matrices. Interactive design encompasses dynamic filtering and linked views. Performance optimization addresses large datasets and WebGL acceleration. Mobile adaptation considers responsive layouts and touch interactions. Real-time data updates support dynamic comparison displays. Accessibility ensures screen reader compatibility and high-contrast modes to meet diverse user needs.

Process and path analysis

Process and path analysis is a visualization technique used to display the flow relationships of data between different nodes. ECharts provides various chart types such as Sankey diagrams and relational graphs to support such analysis needs. Sankey diagrams use width to represent flow volume, making them suitable for scenarios like energy, material, and fund transfers. Path analysis requires a node-edge model data structure, which includes three core elements: a node set, an edge set, and weight values. In ECharts, Sankey diagrams are implemented through the `sankey` series, allowing configuration of node alignment, styles, and interactive enhancements. For complex path networks, the `graph` series is more suitable, enabling advanced features like path highlighting and animation effects. Dynamic path analysis requires a data update mechanism, which can be combined with WebSocket for real-time analysis. When handling large-scale data, optimization strategies such as data sampling, progressive rendering, or WebWorker should be adopted. Multidimensional path analysis can leverage ECharts' multi-coordinate system capabilities for stereoscopic display. Interactive path exploration includes features like click-to-drill-down, path focusing, and custom tooltip design. Mobile adaptation requires considerations such as responsive layout, touch interaction optimization, and dedicated tooltip design.

Social network relationship graph

A social network relationship diagram is a visualization tool used to display connections and interactions between individuals, consisting of nodes and edges. Nodes represent individuals or entities, while edges represent relationships or interactions. This type of chart is widely applied in fields such as social network analysis, recommendation systems, and community detection. ECharts provides a graph series specifically designed for drawing relationship diagrams, supporting force-directed layout to automatically calculate node positions. It allows customization of node and edge styles, including shape, color, size, and labels. The force-directed layout computes node positions through physical simulation and offers multiple parameters to control the layout effect. ECharts also supports rich interactive features such as highlighting, zooming, panning, and dragging nodes. For large-scale data, progressive rendering can simplify visual effects, and WebWorker can be used for optimization. It supports dynamic data updates and visualization of community detection algorithms. Finally, a complete example simulating a Twitter follow network is provided, demonstrating how to convert real-world social network data into the ECharts format.

Predictive analytics visualization

Predictive analytics visualization presents the results of predictive models through intuitive graphics, helping decision-makers understand future trends. ECharts, as a powerful visualization library, enhances the interpretability of predictive outcomes. E-commerce platforms use line charts to compare predicted versus actual sales, adjusting strategies to achieve a 23% increase in GMV. In time series forecasting scenarios, linear regression can illustrate trends, while heatmaps are suitable for multivariate prediction scenarios to display influencing factors. Dynamic interactive designs support parameter adjustments and real-time responses. Prediction interval visualizations enhance credibility, with error band solutions clearly showing forecast deviations. SHAP values and decision path visualizations improve model interpretability. Model performance comparisons employ parallel coordinates, while polar coordinates are used for analyzing differences between predicted and actual values. Real-time data streams are updated via WebSocket. Visual storytelling combines timelines with key node annotations to guide users' focus on critical points.

Anomaly detection visualization

Anomaly detection visualization requires intuitively displaying outliers, abnormal patterns, or sudden trends in data. ECharts, as a powerful visualization library, can clearly present data anomalies through its rich chart types and interactive features. The key lies in selecting appropriate visual encoding methods to create a sharp contrast between abnormal and normal data. Basic anomaly detection chart types include: - **Line charts and area charts**, suitable for showing abnormal fluctuations in time-series data. - **Scatter plots and bubble charts**, effective for highlighting outliers in multidimensional data. Advanced techniques include: - **Box plots**, which display data distribution and outliers. - **Heatmaps**, which emphasize abnormal regions. Interactive features support: - **Data brushing and linking**. - **Dynamic thresholds and adaptive detection**. For multidimensional anomaly detection visualization: - **Parallel coordinates and radar charts** are used to reveal abnormal patterns in high-dimensional data. - **Color variations and special markers** are employed to highlight anomalous records.

Multidimensional data analysis display

Multidimensional data analysis displays present complex data through intuitive methods. ECharts, as a powerful visualization library, can handle multidimensional datasets and express data features using various visual channels such as axes, colors, and sizes. Traditional two-dimensional charts struggle to fully showcase multidimensional data, requiring more advanced visualization solutions. ECharts employs visual encoding theory to achieve multidimensional displays, including Cartesian coordinates, color mapping, graphic sizing, animation effects, and interactive tooltips. Common multidimensional chart types include parallel coordinates and radar charts, which are suitable for analyzing relationships in multidimensional data. ECharts provides multiple interaction methods, such as data zooming, legend filtering, brushing, highlighting, and dynamic sorting, to explore multidimensional data. Advanced techniques include sunburst charts for hierarchical dimensions and 3D scatter plots. When handling large-scale multidimensional data, performance optimization strategies such as sampling, dimensionality reduction, progressive rendering, WebWorker, and visual simplification must be considered. Practical applications include e-commerce analysis dashboards, geographic heatmaps, user behavior paths, product association purchases, and time trend analysis. Custom visual mapping solutions can go beyond default encoding methods, and dynamic multidimensional data updates enable real-time data stream visualization.