Juq097 Site

| Industry | Scenario | juq097 Advantage | |----------|----------|------------------| | FinTech | High‑frequency market data dashboards | Sub‑millisecond latency, GPU‑scaled candlestick charts, secure WebSocket handling. | | IoT / Edge | Sensor farms sending 10k+ readings per second | WASM‑based aggregation reduces network payload, rendering stays buttery‑smooth on low‑end tablets. | | Healthcare | Real‑time patient vitals monitoring | Built‑in anomaly detection (statistical thresholds) can highlight outliers instantly. | | Education | Interactive physics simulations (particle systems) | 3‑D scatter/point‑cloud support lets students explore vector fields with drag‑to‑rotate views. | | Operations | Micro‑service topology maps with live latency heatmaps | Force‑directed layout recomputed in the background, edge colors reflect real‑time latency. |

  "type": "line",
  "data": "ws://api.example.com/stream",
  "encoding": 
    "x":  "field": "timestamp", "type": "temporal", "scale": "time" ,
    "y":  "field": "temperature", "type": "quantitative", "aggregate": "avg" 
  ,
  "interactive": true,
  "theme": "dark"

For those who love code‑first definitions, juq097 offers a fluent, functional API that mirrors the schema: juq097

import  Chart  from "juq097";
const chart = Chart.line()
  .data("ws://api.example.com/stream")
  .encode(
    x:  field: "timestamp", type: "temporal", scale: "time" ,
    y:  field: "temperature", type: "quantitative", aggregate: "avg" 
  )
  .interactive()
  .theme("dark")
  .mount("#temp-chart");

Both approaches compile down to the same internal representation—choose whichever fits your workflow. | Industry | Scenario | juq097 Advantage |

All tests ran on a 2024 MacBook Pro (M2 Pro, 16 GB RAM), Chrome 120, no extensions. "type": "line", "data": "ws://api

| Dataset | Points Rendered | Frame Rate (FPS) | Memory (MB) | |---------|----------------|------------------|-------------| | 100 k 2‑D points (scatter) | 100 k | 60 | 45 | | 1 M 2‑D points (down‑sampled) | 1 M (visible 30 k) | 58 | 62 | | 5 M 3‑D points (instanced) | 5 M | 55 | 78 | | 250 k streaming rows (line) | 250 k (rolling window) | 60 | 40 | | 10 k network nodes (force layout) | 10 k | 57 | 52 |

Compared with Plotly.js (WebGL) the same datasets dropped to ~30 FPS, and with Chart.js they capped at ~15 FPS.