Vector Databases & Search
MCGI: Manifold-Consistent Graph Indexing for Billion-Scale Disk-Resident Vector Search
MCGI proposes a geometry-aware and disk-resident indexing method that leverages Local Intrinsic Dimensionality (LID) to dynamically adapt search strategies. It improves throughput by 5.8x on high-dimensional data and reduces query latency by 3x on billion-scale datasets, addressing the Euclidean-Geodesic mismatch in high-dimensional vector search.
Executive Impact Overview
MCGI's manifold-aware approach translates directly into superior performance and efficiency for enterprise-scale vector search, especially in challenging high-dimensional scenarios.
5.8x
Throughput Increase (GIST1M)
3x
Query Latency Reduction (SIFT1B)
375 QPS
Recall@95% on GIST1M
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
MCGI leverages Local Intrinsic Dimensionality (LID) to understand the local geometric complexity of data. This allows the system to dynamically adapt search strategies, optimizing resource allocation based on how 'flat' or 'curved' the data manifold is in different regions. This adaptive approach is crucial for maintaining performance across varying data distributions, especially in high-dimensional spaces where the Euclidean metric can be misleading.
The system intelligently adjusts the graph topology by modulating the pruning parameter (α) during index construction. In low-LID regions, a larger α permits aggressive pruning and direct connections. In high-LID regions, a smaller α enforces stricter connectivity to preserve manifold fidelity, preventing the search from diverging from the true geodesic path. This ensures that the graph remains navigable and topologically sound, even under dynamic conditions.
MCGI demonstrates superior performance on large-scale, disk-resident datasets by reducing excessive backtracking and disk I/O, which are common bottlenecks in high-dimensional vector search. By aligning the search with the data's intrinsic geometry, it achieves significantly higher throughput and lower query latency compared to state-of-the-art baselines like DiskANN, validating its robustness in diverse production environments.
5.8x
Throughput increase on high-dimensional GIST1M dataset vs. DiskANN at 95% recall.
Enterprise Process Flow
Geometric Calibration (LID Estimation)
→
Z-score Normalization
→
LID to Pruning Parameter Mapping (Logistic Function)
→
Graph Refinement (Iterative Edge Updates)
→
Connectivity Preservation (RNG & EMST)
| Feature | Traditional Methods | MCGI (This Work) |
|---|---|---|
| Dimensionality Handling | Degrades in high-dim |
|
| Search Strategy | Static parameters |
|
| Connectivity Guarantee | Potential fractures |
|
| Performance (High-Dim) | Inefficient I/O |
|
Billion-Scale Latency Reduction
On the billion-scale SIFT1B dataset, MCGI demonstrated a significant reduction in query latency by 3x compared to state-of-the-art DiskANN, at high recall targets. This efficiency stems from its manifold-aware pruning strategy, which optimizes graph topology to minimize redundant disk accesses and computational bottlenecks, making it suitable for demanding production environments.
Calculate Your Potential AI Search ROI
Estimate the efficiency gains and cost savings your enterprise could achieve by adopting manifold-consistent vector search.
Estimated Annual Savings
$0
Annual Hours Reclaimed
0
Your Path to Manifold-Consistent AI Search
A tailored roadmap for integrating MCGI's advanced capabilities into your existing data infrastructure.
Phase 1: Discovery & Data Profiling
Assess current search infrastructure, identify key datasets, and conduct initial LID estimations to understand data geometry.
Phase 2: Pilot Implementation & Optimization
Deploy MCGI on a subset of data, fine-tune pruning parameters based on manifold insights, and benchmark performance.
Phase 3: Production Rollout & Monitoring
Integrate MCGI into full production, establish continuous monitoring of search quality and resource utilization, and scale across distributed systems.
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