IoTeX is the blockchain platform for Real-World AI. Since 2017, its foundational infrastructure has delivered verified, real-time data from the real world to AI systems and decentralized applications. Powering 100+ projects and 40M devices across mobility, robotics, energy, health, and more, IoTeX enables developers to build next-gen AI models and applications that deliver real-world impact. The IoTeX tech stack provides the data, identity, and verification layers to fulfill the surging demand for real-world data in the new AI era. Trusted by partners like Google, Samsung, IEEE, ARM, and Nordic Semiconductor, IoTeX is uniquely positioned to capture the multi-trillion-dollar AI and data economy.

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# IoTeX Active addresses (daily) Methodology

## 1. Overview

IoTeX active addresses daily measures the number of unique addresses that initiated at least one transaction on the IoTeX blockchain each day. This metric serves as a key indicator of network activity and user engagement by tracking distinct addresses that actively participate in network transactions. The calculation uses probabilistic counting methods (HyperLogLog) to efficiently handle large-scale address deduplication across multiple dimensional breakdowns.

## 2. Data sources

- **IoTeX transaction records**: Raw transaction data from the IoTeX blockchain containing transaction metadata, sender addresses, timestamps, and transaction hashes
- **Blockchain transaction logs**: Complete historical record of IoTeX network transactions with block-level timestamps for daily aggregation boundaries

## 3. Step-by-step calculation

### Component A: Transaction sender extraction
- **Purpose**: Identifies all addresses that initiated transactions on IoTeX blockchain
- **Logic**: Extracts transaction sender addresses from IoTeX blockchain transaction records. Each transaction record contains a `from_address` field representing the address that initiated the transaction. Addresses are normalized to lowercase format to ensure consistent deduplication. Each sender is classified with metadata including chain identifier ('iotex'), market sector ('blockchains_l1'), business segment ('blockchain'), and user type ('Transaction sender'). Transaction hash and block timestamp are preserved for downstream processing.
- **Key fields**: `from_address` (normalized to lowercase as `user_address`), `block_timestamp`, `transaction_hash`, chain and classification metadata

### Component B: Daily user record creation  
- **Purpose**: Creates daily unique user activity records by temporal aggregation
- **Logic**: Groups the transaction sender data by day using timestamp truncation to day boundaries. For each unique combination of day, chain, user type, and address, creates a distinct record. This eliminates intraday duplicates where the same address appears in multiple transactions within a single day. The result is a dataset where each record represents one unique address being active on a specific day.
- **Key fields**: `timestamp` (truncated to day), `timestamp_grain` ('day'), `user_address` (renamed to `user_dau`), chain and classification metadata

### Component C: HyperLogLog aggregation with dimensional breakdowns
- **Purpose**: Applies probabilistic counting algorithm to efficiently count distinct addresses across multiple dimensions
- **Logic**: Uses HyperLogLog (HLL) algorithm to create probabilistic sketches for distinct address counting. For each day and dimensional combination, initializes HLL sketches with precision parameter 15. Creates four separate dimensional breakdowns: project-level aggregation (all IoTeX activity), chain-level aggregation (by individual chain), user-type-level aggregation (by transaction sender type), and product-level aggregation (currently null for IoTeX). Each breakdown groups the daily HLL sketches and merges them using `hll_count.merge_partial`, then extracts the final distinct count using `hll_count.extract`.
- **Key fields**: `timestamp`, `dimensions` (breakdown type), `chain_id`, `product_id`, `user_type`, `user_dau` (distinct address count)

### Final aggregation
- **Combining components**: Filters the multi-dimensional HLL results to only include 'chain' and 'project_dbt_tag' dimensions, which represent chain-level and project-level active address counts respectively
- **Metric transformation**: Renames the `user_dau` field to `active_addresses_daily` to match the target metric definition, maintaining the same distinct address count values
- **Time aggregation**: Applies finalize metric macro with 'fill_zeros' strategy to ensure complete daily time series with zero values for days with no activity
- **Final calculation**: The final metric value represents the number of unique addresses that initiated at least one transaction on IoTeX blockchain for each day, calculated using probabilistic HyperLogLog counting for scalability

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

Unique active addresses over a fixed 24-hour window.

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