docs: add activity tracking methodology research

Research document covering industry approaches for activity tracking: - Exponential Moving Average (EMA) with half-life decay - Hubstaff's hour-based approach - Daily target method (8h=100%) - GitHub contribution graph methodology Validates current EMA implementation aligns with industry standards.
2026-03-07 21:50:28 +00:00 · 2026-01-22 01:45:18 +01:00
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 78. **Task - Activity sampler as independent JupyterHub service**: Converted from lazy-start to boot-time service<br>
    **Result**: **Problem**: Activity sampler only started when someone viewed Activity page - not truly independent. **Solution**: Created standalone `activity_sampler.py` service that runs as JupyterHub managed service (like idle-culler). Uses JupyterHub REST API with aiohttp to fetch user activity data. Configured with `activity-sampler-role` having `list:users`, `read:users:activity`, `read:servers` scopes. Service starts automatically on JupyterHub boot, runs continuously with 5-second initial delay. Records samples to same SQLite database (`/data/activity_samples.sqlite`). Removed lazy-start code from ActivityDataHandler. Added aiohttp to Dockerfile dependencies. Renamed env var to `JUPYTERHUB_ACTIVITYMON_SAMPLE_INTERVAL` for consistency
+
+79. **Task - Activity tracking methodology research**: Documented industry approaches for activity scoring<br>
+    **Result**: Created `docs/activity-tracking-methodology.md` covering: (1) Exponential Moving Average with decay - our current approach using half-life parameterization, (2) Time-Window Percentage (Hubstaff) - active seconds per 10-min window with 60-80% typical for development, (3) Daily Target (8h=100%) - maps to work expectations, (4) GitHub Contribution Graph - threshold-based intensity levels. Research confirms our EMA approach is industry-standard. Key insight from Hubstaff: 100% activity is unrealistic, typical ranges 30-80% depending on role
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+# Activity Tracking Methodology Research
+
+## Current Implementation
+
+Our current approach uses **exponential decay scoring**:
+- Samples collected every 10 minutes (configurable)
+- Each sample marked active/inactive based on `last_activity` within threshold
+- Score calculated as weighted ratio: `weighted_active / weighted_total`
+- Weight formula: `weight = exp(-λ × age_hours)` where `λ = ln(2) / half_life`
+- Default half-life: 24 hours (activity from yesterday worth 50%)
+
+## Industry Approaches
+
+### 1. Exponential Moving Average (EMA) / Time-Decay Systems
+
+**How it works:**
+- Recent events weighted more heavily than older ones
+- Decay factor (α) determines how quickly old data loses relevance
+- Example: α=0.5 per day means yesterday's activity worth 50%, two days ago worth 25%
+
+**Half-life parameterization:**
+- More intuitive than raw decay factor
+- "Activity has a 24-hour half-life" is clearer than "α=0.5"
+- Our implementation already uses this approach
+
+**Pros:**
+- Memory-efficient (no need to store all historical data)
+- Naturally handles irregular sampling intervals
+- Smooths out noise/outliers
+
+**Cons:**
+- Older activity never fully disappears (asymptotic to zero)
+- May not match user intuition of "weekly activity"
+
+**Reference:** [Exponential Moving Averages at Scale](https://odsc.com/blog/exponential-moving-averages-at-scale-building-smart-time-decay-systems/)
+
+---
+
+### 2. Time-Window Activity Percentage (Hubstaff approach)
+
+**How it works:**
+- Fixed time window (e.g., 10 minutes)
+- Count active seconds / total seconds = activity %
+- Aggregate over day/week as average of windows
+
+**Hubstaff's formula:**
+```
+Active seconds / 600 = activity rate % (per 10-min segment)
+```
+
+**Key insight from Hubstaff:**
+> "Depending on someone's job and daily tasks, activity rates will vary widely. People with 75% scores and those with 25% scores can often times both be working productively."
+
+**Typical benchmarks:**
+- Data entry/development: 60-80% keyboard/mouse activity
+- Research/meetings: 30-50% activity
+- 100% is unrealistic for any role
+
+**Pros:**
+- Simple to understand
+- Direct mapping to "how active was I today"
+
+**Cons:**
+- Doesn't capture quality of work
+- Penalizes reading, thinking, meetings
+
+**Reference:** [Hubstaff Activity Calculation](https://support.hubstaff.com/how-are-activity-levels-calculated/)
+
+---
+
+### 3. Productivity Categorization (RescueTime approach)
+
+**How it works:**
+- Applications/websites pre-categorized by productivity score (-2 to +2)
+- Time spent in each category weighted and summed
+- Daily productivity score = weighted sum / total time
+
+**Categories:**
+- Very Productive (+2): IDE, documentation
+- Productive (+1): Email, spreadsheets
+- Neutral (0): Uncategorized
+- Distracting (-1): News sites
+- Very Distracting (-2): Social media, games
+
+**Pros:**
+- Captures quality of activity, not just presence
+- Customizable per user/role
+
+**Cons:**
+- Requires app categorization (complex to implement)
+- Subjective classification
+- Not applicable to JupyterLab (all activity is "productive")
+
+**Reference:** [RescueTime Methodology](https://www.rescuetime.com/)
+
+---
+
+### 4. GitHub Contribution Graph (Threshold-based intensity)
+
+**How it works:**
+- Count contributions per day (commits, PRs, issues)
+- Map counts to 4-5 intensity levels
+- Levels based on percentiles of user's own activity
+
+**Typical thresholds:**
+```javascript
+// Example from implementations
+thresholds: [0, 10, 20, 30]  // contributions per day
+colors: ['#ebedf0', '#9be9a8', '#40c463', '#30a14e', '#216e39']
+```
+
+**Key insight:**
+- Relative to user's own history (not absolute)
+- Someone with 5 commits/day max sees different scale than 50 commits/day
+
+**Pros:**
+- Visual, intuitive
+- Adapts to user's activity patterns
+
+**Cons:**
+- Binary daily view (no intra-day granularity)
+- Doesn't show decay/trend
+
+---
+
+### 5. Daily Target Approach (8h = 100%)
+
+**How it works:**
+- Define expected activity hours per day (e.g., 8h)
+- Actual active hours / expected hours = daily score
+- Cap at 100% or allow overtime bonus
+
+**Formula:**
+```
+Daily score = min(1.0, active_hours / 8.0) × 100
+Weekly score = avg(daily_scores)
+```
+
+**Pros:**
+- Maps directly to work expectations
+- Easy to explain to users
+
+**Cons:**
+- Assumes consistent work schedule
+- Doesn't account for part-time, weekends
+- JupyterHub users may have variable schedules
+
+---
+
+## Recommendations for JupyterHub Activity Monitor
+
+### Option A: Keep Current (EMA with decay)
+
+Our current implementation is actually well-designed for the use case:
+
+| Aspect | Current Implementation |
+|--------|------------------------|
+| Sampling | Every 10 min (configurable) |
+| Active threshold | 60 min since last_activity |
+| Decay | 24-hour half-life |
+| Score range | 0-100% |
+| Visualization | 5-segment bar with color coding |
+
+**Suggested improvements:**
+1. Add tooltip showing actual score percentage
+2. Consider longer half-life (48-72h) for less frequent users
+3. Document what the score represents
+
+### Option B: Hybrid Daily + Decay
+
+Combine daily activity percentage with decay:
+
+```python
+# Daily activity: hours active today / 8 hours (capped at 100%)
+daily_score = min(1.0, active_hours_today / 8.0)
+
+# Apply decay to historical daily scores
+weekly_score = sum(daily_score[i] * exp(-λ * i) for i in range(7)) / 7
+```
+
+**Benefits:**
+- More intuitive "8h = full day" concept
+- Still decays older activity
+
+### Option C: Simplified Presence-Based
+
+For JupyterLab, activity mostly means "server running + recent kernel activity":
+
+| Status | Points/day |
+|--------|------------|
+| Offline | 0 |
+| Online, idle > 1h | 0.25 |
+| Online, idle 15m-1h | 0.5 |
+| Online, active < 15m | 1.0 |
+
+Weekly score = sum of daily points / 7
+
+---
+
+## Decision Points
+
+1. **What does "100% activity" mean for JupyterHub users?**
+   - Option: Active during all sampled periods in retention window
+   - Option: 8 hours of activity per day
+   - Option: Relative to user's own historical average
+
+2. **How fast should old activity decay?**
+   - Current: 24-hour half-life (aggressive decay)
+   - Alternative: 72-hour half-life (more stable)
+   - Alternative: 7-day half-life (weekly trend)
+
+3. **Should weekends count differently?**
+   - Current: All days weighted equally
+   - Alternative: Exclude weekends from expected activity
+
+---
+
+## Sources
+
+- [Exponential Moving Averages at Scale (ODSC)](https://odsc.com/blog/exponential-moving-averages-at-scale-building-smart-time-decay-systems/)
+- [Exponential Smoothing (Wikipedia)](https://en.wikipedia.org/wiki/Exponential_smoothing)
+- [Hubstaff Activity Calculation](https://support.hubstaff.com/how-are-activity-levels-calculated/)
+- [How Time is Calculated in Hubstaff](https://support.hubstaff.com/how-is-time-tracked-and-calculated-in-hubstaff/)
+- [RescueTime](https://www.rescuetime.com/)
+- [EWMA Formula (Corporate Finance Institute)](https://corporatefinanceinstitute.com/resources/career-map/sell-side/capital-markets/exponentially-weighted-moving-average-ewma/)
+- [Developer Productivity Metrics (Axify)](https://axify.io/blog/developer-productivity-metrics)