LOD (Level of Detail) Data

LOD systems are built on a hierarchical structure of data layers. The raw layer contains granular, unprocessed data (e.g., individual transactions). The aggregated layer contains pre-computed summaries (e.g., hourly volume, daily active addresses). The indexed layer provides optimized structures for fast lookups of specific data points.

The primary purpose of LOD is to drastically reduce query latency and computational cost. Instead of scanning petabytes of raw on-chain data for every request, queries are routed to the most appropriate, pre-computed aggregate layer. This enables:

• Sub-second responses for common dashboard metrics.
• Cost-effective analysis by avoiding full historical scans.
• Scalability to support thousands of concurrent users.

A core application of LOD is organizing blockchain data into standardized time intervals. Data is rolled up from blocks into consistent windows:

• Raw: Per-block data.
• Level 1: Minute or hour-level summaries.
• Level 2: Daily or weekly summaries.
• Level N: Monthly or yearly aggregates. This allows analysts to seamlessly zoom in and out on metrics like transaction volume, gas fees, or active addresses over any timeframe.

Aggregations in LOD systems are deterministic and verifiable. The summarized data in higher layers is derived from the underlying raw data using immutable logic. This ensures that the 24-hour trading volume reported at the daily layer can be cryptographically traced back to the constituent transactions, maintaining data integrity and auditability.

LOD data is foundational for real-time analytics platforms. A dashboard displaying Total Value Locked (TVL), protocol revenue, or user growth charts relies on pre-aggregated LOD tables. When a user selects a 30-day view, the query hits a daily aggregate table, not 30 days worth of raw blocks, enabling instant visualization and interaction.

Raw on-chain data is the complete, immutable ledger stored by nodes (e.g., every transaction in a block). LOD data is a processed derivative optimized for read performance.

• Purpose: Raw data for validation and full history; LOD for analysis and queries.
• Structure: Raw data is sequential (block-by-block); LOD is dimensional (organized by time, address, contract).
• Access: Querying raw data is slow and expensive; LOD enables fast, complex queries.

This is the most common application, where LOD models are swapped based on camera distance to maintain high frame rates. Key techniques include:

• Geometry LODs: Reducing polygon count for distant objects.
• Texture LODs (Mipmapping): Using lower-resolution textures for objects farther away.
• Shader LODs: Simplifying shader complexity for performance.

Engines like Unreal Engine and Unity have built-in LOD systems to manage this automatically.

Massive geospatial datasets use LOD principles for scalable visualization and analysis.

• Digital Elevation Models (DEMs): Terrain data is stored at multiple resolutions (e.g., 1m, 10m, 100m) for zoom-level-appropriate rendering.
• 3D City Models: Buildings and infrastructure are represented with varying detail for city planning and simulation.
• Web Mapping Services (WMS/WMTS): Serve pre-rendered map tiles at different zoom levels, a form of LOD for 2D maps.

Platforms like Google Earth and CesiumJS rely heavily on this concept.

LOD is critical for managing complex assemblies with thousands of parts.

• Assembly Navigation: Engineers view a simplified bounding-box representation of sub-assemblies for easier navigation, then drill down to detailed components.
• Collaboration & Review: Sharing lightweight, simplified models with stakeholders who don't need manufacturing-level detail.
• Simulation & Analysis: Running simulations (e.g., Finite Element Analysis) often uses a simplified LOD mesh to reduce computational cost during initial iterations.

Handling massive datasets from scientific instruments or computational models.

• Computational Fluid Dynamics (CFD): Results are visualized with coarser meshes for overviews and finer meshes for analyzing specific regions of interest.
• Medical Imaging (e.g., MRI/CT Scans): Volumetric data can be rendered at lower resolution for real-time manipulation, with the ability to load full detail for diagnosis.
• Astrophysics & Cosmology: Galaxy simulations use particle-based LOD, where distant clusters are represented by single meta-particles.

Governed by standards like Level of Development (LOD) from the BIM Forum, which defines the reliability of model elements at different project stages.

• LOD 100: Conceptual massing model.
• LOD 200: Generic systems with approximate quantities.
• LOD 300: Specific elements with precise geometry and data.
• LOD 350 & 400: Detailed fabrication and assembly information.

This structured LOD framework ensures clarity in model usage for design, costing, and construction.

A network-centric application where LOD enables efficient data transmission.

• 3D Web Streaming: Platforms like Sketchfab stream a low-polygon version first, then progressively enhance detail.
• Point Cloud Visualization: Systems like Potree dynamically load billions of LiDAR points by streaming only the points needed for the current viewport and zoom level.
• Basis Universal Texture Compression: A real-world example where a single compressed texture file contains multiple LODs, which the GPU selects at runtime.

This minimizes initial load times and bandwidth usage.

The foundational process of organizing raw blockchain data into structured, queryable formats. Indexers parse on-chain transactions and events to create searchable databases, which is a prerequisite for generating LOD data. Key methods include:

• Event-based indexing: Capturing smart contract logs.
• State-based indexing: Tracking changes to contract storage.
• Transaction indexing: Parsing raw transaction calldata and receipts.

Refers to the level of detail or aggregation in a dataset. In blockchain analytics, granularity exists on a spectrum:

• High Granularity (Fine-grained): Raw transaction logs, individual transfer events, exact gas used. Equivalent to the most detailed LOD level.
• Low Granularity (Coarse-grained): Daily volume aggregates, wallet balances at a snapshot, weekly active addresses. Represents a higher, summarized LOD level. Choosing the right granularity balances query performance with analytical depth.

A category of data processing that facilitates complex analytical queries, often using multidimensional models (cubes). LOD data structures are inherently suited for OLAP operations like:

• Roll-up: Aggregating data to a higher LOD (e.g., hourly → daily trades).
• Drill-down: Navigating to a lower, more detailed LOD.
• Slice and Dice: Viewing data from different dimensions (e.g., by token, by protocol). This enables analysts to explore blockchain metrics interactively.

The process of summarizing data points over specific time windows, a core technique for creating higher LOD views. Common aggregations in DeFi and on-chain analysis include:

• Rolling sums/averages: Like 24-hour trading volume or 7-day moving average fee revenue.
• Snapshot aggregates: Total Value Locked (TVL) or unique active wallets at the end of each day.
• Candlestick data: A financial LOD format aggregating Open, High, Low, Close (OHLC) prices for a period.

A database object that contains the results of a precomputed query. They are a critical implementation pattern for serving LOD data efficiently. Instead of aggregating billions of rows on-demand, a materialized view stores the aggregated (higher LOD) result, which is periodically refreshed. This trades off real-time latency for massive query speed improvements, making dashboards and historical analysis feasible.