Expert Cache: Adaptive SPARQL Caching

• Expert Cache is an adaptive caching system designed for SPARQL endpoints that intercepts queries and uses KNN prediction to prefetch likely future requests.
• It uses graph-based query feature modeling to convert queries into template vectors, enabling effective similarity matching and prefetching for improved hit rates.
• The system implements modified exponential smoothing for cache replacement, achieving up to 52% latency reduction and a 76.65% hit rate on datasets like DBpedia.

Expert Cache

An expert cache is an adaptive caching solution designed for structured query systems—most prominently knowledge base and SPARQL query endpoints—where access latency, endpoint throughput, and repeated subquery patterns are major performance bottlenecks. Its core innovation is the combination of proxy-level query interception, graph-based query feature modeling, K-nearest-neighbor (KNN) prediction for prefetch, and modified exponential smoothing for cache replacement. The system optimizes both hit rate and expected response time, supporting production workloads on real knowledge bases such as DBpedia and LinkedGeoData (Zhang et al., 2018).

1. System Architecture and Workflow

The expert cache operates logically as a proxy between client applications and underlying SPARQL endpoints. The architecture comprises three principal components:

1. Query Router: Intercepts and processes incoming SPARQL queries. For each, it checks the cache for an identical previously-seen query. On a cache hit, the result is served immediately by the cache manager; on a miss, the router fetches results from the endpoint and updates the cache.
2. Prefetcher (Suggestion Process): Converts each query into a feature vector using a graph-edit-distance mapping against a fixed set of representative templates. A KNN predictor, trained offline, selects K similar future queries for speculative prefetching. Results from these K predictions are inserted into the cache asynchronously.
3. Cache Manager: Maintains a record-based cache with entries 〈query, result〉, each associated with a frequency estimate. Upon new insertions or repeated accesses, estimates are updated via Modified Simple Exponential Smoothing (MSES). When the cache exceeds capacity, the lowest-scoring entries are evicted.

The combined workflow involves concurrent query processing, background feature computation for suggestion, and aggressive cache warmup through prefetches (see Section 2).

2. Prefetching and Prediction via KNN Graph Modeling

Query pattern prediction is realized by embedding incoming queries into a low-dimensional feature space reflecting structural similarities:

• Each query's set of triple patterns is mapped onto template graphs, generating a vector of graph edit distances.
• For DBpedia and similar SPARQL endpoints, 18 template graphs based on the DBPSB suite yield an 18-dimensional feature vector.
• Feature vectors for all historical queries are organized in a KD-tree.
• At runtime, the current query's vector is used to retrieve its top K nearest neighbors (via Euclidean distance) from the tree, which become the predicted next queries for prefetch.

This prediction captures client (and application) locality and repetition in knowledge base queries, as shown in empirical workloads (Zhang et al., 2018).

3. Cache Structure, Hit Rate, and Replacement Policy

The cache itself is an in-memory hash map indexed by the full query string, storing both result and access metadata:

• Cache Hit Ratio: $H = \frac{C_{\text{hits}}}{C_{\text{total}}}$, where $C_{\text{hits}}$ denotes queries served directly from cache and $C_{\text{total}}$ is the total query count.
• Expected Latency: $$E[T] = H \cdot T_{\text{cache}} + (1-H) \cdot T_{\text{ep}}$$, minimizing average response time through increased hit rates.
• Modified Simple Exponential Smoothing (MSES):
  • On each access (hit or prefetch), an entry's score $E_i$ is updated as $$E_i(t) = \alpha + E_i(\text{prev}) \cdot (1-\alpha)^{t-t_{\text{prev}}}$$, decaying over time unless refreshed.
  • Eviction is performed by removing entries with the lowest $E_i$ once capacity is exceeded.

Cache size and smoothing parameters ($H$, $\alpha$) are tunable for workload and memory budgets.

4. Algorithmic Complexity and Implementation

Algorithms are designed for minimal per-query overhead:

• Query checking and cache update: $O(1)$ average time via hash lookup and in-place score update.
• Prediction and prefetch: $$O(18 \cdot \text{GED} + \log N + K \cdot T_{\text{ep}})$$ per prefetch, with $N$ the historical query count.
• Memory usage: For 1,000 queries, measured at ~7 MB cache data plus 0.45 KB MSES metadata.
• Prefetches are handled in parallel threads and only issued for uncached queries.

5. Empirical Evaluation and Performance Analysis

Experiments on DBpedia and LinkedGeoData SPARQL endpoints demonstrate:

System	Avg Exec Time	Hit Rate (DBpedia)
No cache	625 ms	N/A
ASQC	264 ms	72.63%
Expert Cache	251 ms	76.65%

• Training for template-based feature modeling is an order of magnitude faster than clustering approaches.
• Expert Cache outperforms baseline systems on both hit rate and execution latency—showing up to a 52% latency reduction compared to the non-caching baseline, and an absolute 4% higher hit rate compared to ASQC under identical hardware constraints.
• The benefit is significant under high-latency endpoint conditions and for workloads exhibiting repetitive query patterns.

Further tuning shows that modest increases in cache size or K yield rapidly diminishing returns; optimal settings target 1,000–2,000 queries and K at the “elbow” below saturation.

6. Tuning, Limitations, and Deployment Considerations

• KNN parameter K: Larger K enhances hit rate but at the cost of increased prefetch traffic and server load; optimal K achieves a hit rate within 5% of the maximum attainable.
• Smoothing constant $\alpha$: Lower $\alpha$ extends the cache's temporal horizon, retaining long-tail queries longer; higher $\alpha$ biases heavily toward recent requests.
• Scalability: The system is computationally efficient for large real-world knowledge bases. Prefetching can saturate server bandwidth if tuned aggressively.
• Trade-offs: Under excessively small cache sizes, hit rate degrades; conversely, very large caches yield diminishing hit rate returns per added storage unit.
• Application scope: The architecture generalizes to other queryable, structured knowledge bases beyond SPARQL endpoints when provided with suitable structural feature extractors.

In summary, Expert Cache leverages lightweight offline learning, structural query analysis, and adaptive cache replacement to significantly improve query throughput and responsiveness in knowledge base applications, with modest resource demands appropriate for real-world deployment (Zhang et al., 2018).