Social

Story Protocol

# Story Protocol Active addresses (daily) Methodology

## 1. Overview

Daily active addresses measures the number of unique addresses that initiated at least one transaction on the Story blockchain within a 24-hour period. This metric serves as a key indicator of user engagement and network adoption for Story, a blockchain focused on intellectual property and creative content. The calculation uses HyperLogLog approximation to efficiently count distinct addresses while maintaining accuracy across different dimensional breakdowns.

## 2. Data sources

- **Story blockchain transaction records**: Complete transaction data from the Story blockchain containing timestamp information, transaction hashes, and sender addresses (`from_address`)
- **Chain availability configuration**: Data availability windows and filtering rules to ensure data quality and completeness

## 3. Step-by-step calculation

### Component A: User identification and classification
- **Purpose**: Extract and classify transaction senders as active users with standardized metadata
- **Logic**: The system processes Story blockchain transaction records to identify unique users through their transaction activity. Each transaction sender (`from_address`) is classified as an active user with standardized blockchain metadata. The process captures the block timestamp, transaction hash, and sender address while enriching each record with protocol classification data including chain identifier, market sector classification, and business segment categorization.
- **Key fields**: `block_timestamp` for timing, `transaction_hash` for transaction identification, `from_address` as the unique user identifier, `chain` set to 'story', `market_sector_id` as 'blockchains_l2', `business_segment_id` as 'blockchain', and `user_type` as 'Transaction sender'

### Component B: Daily user aggregation with HyperLogLog
- **Purpose**: Calculate daily distinct active users using probabilistic counting for scalability
- **Logic**: Transaction timestamps are truncated to daily boundaries to group activity by calendar day. For each day, the system creates HyperLogLog sketches of unique sender addresses using precision level 15, which provides approximately 99.9% accuracy while enabling efficient memory usage for large datasets. The HyperLogLog approach allows the system to handle millions of addresses without memory constraints while maintaining near-exact distinct counts.
- **Key fields**: `timestamp` truncated to day granularity, `user_dau` containing the unique address identifier, and `hll` containing the HyperLogLog sketch

### Component C: Dimensional breakdown aggregation
- **Purpose**: Aggregate user counts across multiple analytical dimensions using HyperLogLog merging
- **Logic**: The daily HyperLogLog sketches are merged and aggregated across four distinct dimensional breakdowns. The project-level breakdown (`project_dbt_tag`) provides overall Story ecosystem metrics by merging all user sketches regardless of other attributes. The chain-level breakdown provides metrics specific to the Story chain. User type breakdown aggregates by transaction sender classification. Product-level breakdown handles any product-specific segmentation, though this remains null for Story's current implementation. Each breakdown uses HyperLogLog partial merging followed by count extraction to produce final distinct user counts.
- **Key fields**: `dimensions` indicating breakdown type, `chain_id` for chain-specific metrics, `product_id` for product segmentation, `user_type` for user classification, and `user_dau` containing the final distinct count

### Final aggregation
- **Combining components**: The system filters the dimensional aggregations to include only 'chain' and 'project_dbt_tag' dimensions, which provide chain-specific and overall project-level active address metrics respectively
- **Currency conversion**: Not applicable as this metric measures address counts rather than monetary values
- **Time aggregation**: Data is aggregated to daily boundaries using timestamp truncation to day granularity, with each day representing a complete 24-hour period in UTC
- **Final calculation**: The `user_dau` field is renamed to `active_addresses_daily` to match the metric naming convention. Missing days are filled with zero values to ensure continuous time series data. The final metric represents the count of unique addresses that sent at least one transaction on each given day, calculated using HyperLogLog approximation for scalability and performance.

*This methodology was automatically generated by Token Terminal's methodology analyzer.*

Unique active addresses over a fixed 24-hour window.

Story ProtocolIP

Active addresses (daily)

Active addresses (daily)

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