Collators aggregate parachain transactions block candidates and producing state transition proofs for validators based on those blocks.

Heima is a specialized identity and data verification blockchain powered by Polkadot Relay chain.

HEI holders own and govern the protocol. Heima’s investors include LD Capital, Digital Renaissance, Signum Capital, and others.

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Blockchains (L2)

Heima

# Heima Active users (monthly) Methodology

## 1. Overview

Monthly Active Users (MAU) measures the number of unique users who have performed any transaction activity on the Heima network within a rolling 30-day window. This metric provides insights into user engagement and platform adoption by tracking distinct addresses that have initiated transactions on either the native Heima substrate chain or the Heima EVM-compatible layer. The calculation uses HyperLogLog data structures for efficient approximate counting across multiple dimensional breakdowns.

## 2. Data sources

The calculation relies on the following raw blockchain data sources:

- **Heima blockchain extrinsics**: Native substrate transactions containing user addresses (`id` field) and block timestamps from the Heima parachain
- **Heima EVM ethereum pallet events**: EVM-compatible transaction events from the ethereum pallet with method `Executed`, containing user addresses in JSON event data and block timestamps
- **Heima blockchain timestamps**: Block-level timestamp data for both native and EVM transaction activity
- **User address identifiers**: Unique address identifiers from both substrate-native and EVM-compatible transaction formats

## 3. Step-by-step calculation

### Component A: User activity extraction
- **Purpose**: Identifies all user addresses that initiated transactions on Heima networks
- **Logic**: Combines two data streams: native Heima extrinsics using the `id` field as user address, and Heima EVM events from the ethereum pallet's `Executed` method where user addresses are extracted from the first element of JSON event data. All users are classified as "Transaction sender" type under the "blockchain" business segment and "blockchains_l2" market sector. Chain identifiers distinguish between "heima" (native) and "heimaevm" (EVM-compatible) activity
- **Key fields**: `block_timestamp` for timing, `user_address` for unique identification, `chain` for network distinction, `user_type` for classification

### Component B: Daily active user aggregation  
- **Purpose**: Calculates unique daily active users by removing duplicates within each day
- **Logic**: Groups user activity by truncating `block_timestamp` to day boundaries using timestamp truncation. Applies distinct operation on `user_address` within each day to ensure each user is counted only once per day regardless of transaction volume. Maintains dimensional attributes including chain, user type, and product identifiers. Implements incremental processing by filtering to recent dates when updating existing data
- **Key fields**: `timestamp` (day-truncated), `user_dau` (distinct user address), `chain`, `user_type`, `product_id`

### Component C: HyperLogLog conversion and window preparation
- **Purpose**: Converts daily user counts to HyperLogLog structures and prepares rolling window data
- **Logic**: Initializes HyperLogLog data structures from daily active user data using precision parameter 15 for approximate distinct counting. Creates a complete datespine spanning from the earliest available data to the current day to ensure no gaps in time series. Generates rolling 30-day windows by joining each target date with a 29-day lookback period, creating associations between each day and all previous days within the rolling window
- **Key fields**: `hll` (HyperLogLog structure), `timestamp` (target date), `joinday` (constituent dates in window)

### Component D: Rolling window aggregation
- **Purpose**: Aggregates unique users across each 30-day rolling window
- **Logic**: Joins the window framework with daily HyperLogLog data to collect user activity for each 30-day period. Merges HyperLogLog structures across all days within each window using `hll_count.merge_partial()` to combine approximate distinct counts. Extracts final unique user counts from merged HyperLogLog using `hll_count.extract()` to produce the MAU value for each window. Maintains all dimensional attributes throughout the aggregation process
- **Key fields**: `timestamp` (window end date), `hll` (aggregated HyperLogLog), dimensional breakdowns

### Component E: Dimensional breakdown creation
- **Purpose**: Creates multiple analytical perspectives of the MAU metric
- **Logic**: Generates four separate dimensional views by grouping and aggregating the windowed data: project-level aggregation (all chains combined), chain-level breakdown (separate values per chain), user type-level breakdown (separate values per user classification), and product-level breakdown (separate values per product). Each breakdown nullifies non-relevant dimensions while preserving the target dimension for analysis. All breakdowns use the same HyperLogLog merging and extraction logic for consistent counting
- **Key fields**: `dimensions` (breakdown type), `chain_id`, `product_id`, `user_type`, `user_mau` (final count)

### Final aggregation
- **Combining components**: All dimensional breakdowns are combined using union operations to create a comprehensive dataset with multiple analytical perspectives in a single output table
- **Currency conversion**: Not applicable for user count metrics
- **Time aggregation**: Uses rolling 30-day windows updated daily, with each day representing the unique user count for that day plus the previous 29 days
- **Final calculation**: The `user_mau` field represents the approximate count of unique users who performed transactions within the 30-day window ending on the specified timestamp, calculated by merging and extracting HyperLogLog structures across the rolling period

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

Unique revenue-generating users over a rolling 30-day window.

HeimaHEI

Active users (monthly)

Active users (monthly)

Competitive landscape

Chain breakdown