Event-Driven Data Infra for Aave V3

Updated 20 December 2025

Event-Driven Data Infrastructure for Aave V3 is a system that organizes blockchain events and cross-chain transactions into unified records for precise, reproducible analysis.
It employs structured schemas and extraction pipelines—like XChainWatcher and XChainDataGen—to normalize data across diverse chains and protocols.
The infrastructure supports real-time forensic and security applications by integrating finality models, global ordering, and quantitative metrics across DeFi ecosystems.

Cross-chain event-level datasets are structured, reproducible records of blockchain events and synthesized cross-chain transactions (“cctx”) spanning multiple chains, interoperability protocols, and asset types. These datasets enable quantitative, forensic, and systemic analysis across DeFi, interoperability, and security domains by providing atomic event logs, transactional metadata, and logically linked cross-chain actions under clear schemas and finality models.

1. Definitions, Core Concepts, and Data Models

A cross-chain event-level dataset consists of decoded on-chain event logs and transaction metadata, often enriched via contract ABIs and price oracles, and organized into either raw event tables or unified cross-chain transaction (“cctx”) records. Each record encapsulates atomic swaps, deposits, withdrawals, mint/burn actions, or protocol-specific events mapped across different chains. Precise schema definitions, metadata fields, linkages, and data normalization underpin these resources.

Canonical components include:

Chain/Bridge coverage: Ethereum, Arbitrum, Optimism, Polygon, Solana, Avalanche, Base, Ronin, Moonbeam, Gnosis, Near, BSC, etc.; >45 bridge protocols (e.g., Nomad, Ronin, Stargate, Axelar, Wormhole, Celer) (Augusto et al., 2 Oct 2024, Mancino et al., 24 Oct 2025, Augusto et al., 17 Mar 2025).
CCTX schema: Links source/destination chain IDs, transaction hashes, event signatures, asset addresses and amounts, sender/recipient, lifecycle status, finality guarantees (Augusto et al., 17 Mar 2025).
Raw event types: ERC-20 transfers, DEX swaps, bridge deposits/withdrawals, protocol-specific events (Aave’s Supply, Borrow, Repay, LiquidationCall, FlashLoan, etc.) (Fan et al., 12 Dec 2025).
Datalog representation: Each event is a fact with explicit field (name, type) and semantics; absence of an expected fact encodes failure/incompleteness (Augusto et al., 2 Oct 2024).

2. Schema Structures and Extraction Pipelines

Schema organization varies by framework:

Fact Type	Fields / Description
sc_deposit	tx_hash, chain_id, event_idx, from, to, amount
sc_token_deposited	tx_hash, event_idx, deposit_id, beneficiary, dst_token, orig_token, dst_chain_id, std, amount
tc_token_deposited	tx_hash, event_idx, deposit_id, beneficiary, dst_token, amount
erc20_transfer	tx_hash, chain_id, event_idx, contract, from, to, amount

13 fact types cover all bridge-control, token-mapping, finality, and transaction metadata.

Field	Type/Description
cctx_id	String (unique cross-chain ID)
protocol	Bridge identifier
source_chain_id	Int
source_tx_hash	String
source_block_number	Int
source_event_signature	String
token_address_src	String
amount_src	Decimal(38,18)
destination_chain_id	Int
recipient_address	String
token_address_dst	String
amount_dst	Decimal(38,18)
status	String

Pipelines perform raw event extraction (JSON-RPC, ABI decoding), sharding, and pairing. Examples follow a two-stage procedure: first, ingest and decode events; second, bridge-specific generators perform logical pairing by depositId/messageId or signature (Augusto et al., 17 Mar 2025).

Cross-chain arbitrage datasets structure every swap and bridge event as a node in a directed acyclic event graph G=(V,E). Each event includes event_id, tx_hash, chain_id, block_number, timestamp, log_index, event_type, token_in/out, amounts, addresses, and metadata.

ConneX’s system emphasizes semantic quintuple extraction (amount, token, destination, counterpart chain, timestamp) via LLM-driven pruning across per-category field sets, further refined by examiner validation steps. Output aligns paired transaction instances (source/destination) with explicit field mappings and validation checks.

3. Finality, Ordering, and Data Integrity

Dataset integrity and cross-chain linkage rely on finality models:

Hard finality: Block-level confirmations (e.g., Ethereum: 780 s, BSC: 15 s) for reliable, reorg-safe records.
Soft/optimistic finality: Fewer block confirmations or timeouts (configurable per chain/property) for lower-latency but less robust linkage (Augusto et al., 17 Mar 2025). Logic engines integrate checks to validate mapping fidelity.
Global time ordering: Fields include timestamp_utc, chain_id, block_number, tx_index (the global_time_key (Mancino et al., 24 Oct 2025)), and deterministic intra-block log_index ordering. Event tables and derived graphs can be strictly ordered for tracing, forensics, and MEV pathfinding.

Deduplication and reorg-repair mechanisms are required to eliminate zombie events and ensure only finalized logs are included. Best practices include indexation on (tx_hash, log_index), sharding event tables by date and chain, and versioning config/ABI artifacts for scientific rigor (Augusto et al., 17 Mar 2025, Fan et al., 12 Dec 2025).

4. Quantitative Metrics, Analytical Formulae, and Example Access Patterns

Aggregated metrics computed on event-level datasets include:

Total transfer volume: $V = \sum_{i=1}^N v_i$ (sum of locked/minted and unlocked/released values across events) (Augusto et al., 2 Oct 2024, Augusto et al., 17 Mar 2025).
Average transaction size: $\bar v = V/N$ .
Lock–release ratio: $R_{lock} = (\sum \mathrm{locks})/(\sum \mathrm{releases})$ .
Failure rate: $F = (\#\ \text{incomplete cctx})/(\#\ \text{total cctx})$ .
Latencies: $L = \frac{1}{N}\sum_{i=1}^N [t^{(i)}_{\mathrm{dst\_finality}} - t^{(i)}_{\mathrm{src\_init}}]$ (Augusto et al., 17 Mar 2025, Augusto et al., 2 Oct 2024).

Example queries and usage patterns:

Python / pandas, PyArrow:

import pandas as pd
df = pd.read_parquet("xchain_cctx.parquet")
sel = (df.source_chain=="Ethereum") & (df.dest_chain=="Moonbeam")
sel &= (df.token_symbol=="USDC") & (df.timestamp < "2022-06-01")
subset = df[sel]

SQL:

SELECT *
  FROM cctx
 WHERE source_chain = 'Ethereum'
   AND dest_chain   = 'Moonbeam'
   AND token_symbol = 'USDC'
   AND timestamp BETWEEN '2022-01-01' AND '2022-07-31';

Aave cross-chain migration (SQL):

WITH eth_withdraw AS (
  SELECT user, block_timestamp AS t1, amount_usd 
  FROM Withdraw WHERE chain='ethereum'
), arb_supply AS (
  SELECT user, block_timestamp AS t2, amount_usd 
  FROM Supply WHERE chain='arbitrum'
)
SELECT e.user, e.t1, a.t2, e.amount_usd AS withdrew, a.amount_usd AS deposited
  FROM eth_withdraw e
  JOIN arb_supply a ON e.user = a.user
  AND a.t2 BETWEEN e.t1 AND e.t1 + INTERVAL '1 day';

Metrics and visualizations are used for forensic analyses (attack detection, anomalous flows), user-behavior studies, capital migration, liquidation cascades, MEV opportunity scanning, and comparative protocol efficiency (Augusto et al., 2 Oct 2024, Fan et al., 12 Dec 2025, Mancino et al., 24 Oct 2025).

5. Security, Forensics, and Specialized Applications

Cross-chain event datasets underpin several research domains:

Attack and anomaly detection: XChainWatcher identified $611M Ronin/$190M Nomad breaches, unintended cctxs, failed exploit attempts, and unreleased funds by rule-based analysis of Datalog facts (Augusto et al., 2 Oct 2024).
Forensic fund tracing and compliance: ConneX enabled tracking illicit flows (e.g., $1M hack flows) and identified round-trip laundering via paired event linkage, achieving F1=0.9746 in cross-bridge pairing (Liang et al., 3 Nov 2025).
Multihop MEV search: Bunny Hops shows sequence-dependent cross-chain arbitrage paths are rare. Their pipeline computes $>2.4$B events, reconstructs arbitrage event graphs, and enforces actor/time/value continuity (Mancino et al., 24 Oct 2025).
DeFi analytics: Aave event-driven datasets enable study of capital flows, systemic risk, liquidation cascades, and supply/borrow migration across six chains, with strict ordering and USD normalization (Fan et al., 12 Dec 2025).

Security models are parameterized by bridge protocol (multisig, fraud-proof, intent-based), asset types (ERC-20/native only, no NFTs), and proof semantics (ZK, Merkle, fraud-proof abstracted unless modeled directly). Systems may lack coverage of off-chain flows, intent aggregation, or proof verification, requiring continued methodological advance (Augusto et al., 2 Oct 2024, Augusto et al., 17 Mar 2025).

6. Extensibility, Best Practices, and Open Problems

Current datasets often restrict to selected bridges, EVM chains, and fungible tokens. Recommended practices for extension include:

Config-driven modular pipelines: Use YAML artifacts for per-chain RPC, ABI, event signature mapping, bridge logic pairing modules; maintain a pluggable registry for ingesting new chains/bridges (Augusto et al., 17 Mar 2025, Mancino et al., 24 Oct 2025).
Partitioning and sharding: Partition event/cctx tables by date and chain for efficient queries; use Parquet or ORC for high-throughput analytics storage (Mancino et al., 24 Oct 2025, Fan et al., 12 Dec 2025).
Parallel extraction and confirmation-aware repair: CPU-parallel RPC calls, dynamic batch sizing, regular finality sweeps for reorg resilience, versioning of configs/ABIs, and open containerization for reproducibility (Augusto et al., 17 Mar 2025, Fan et al., 12 Dec 2025).
Coverage gaps: Asset types (no ERC-721), bridge diversity (non-included protocols), abstracted off-chain proofs, and intermediary effects remain open areas for dataset enrichment.

A plausible implication is that, as streaming ingestion and protocol variety increase, standardized, open cross-chain event-level datasets will be central to reproducible research in security analysis, interoperability benchmarking, MEV dynamics, and systemic risk (Augusto et al., 2 Oct 2024, Liang et al., 3 Nov 2025, Augusto et al., 17 Mar 2025, Mancino et al., 24 Oct 2025, Fan et al., 12 Dec 2025).