Distributed Query Engine

The engine parses a declarative query (e.g., SQL) into a directed acyclic graph (DAG) of logical operators. A cost-based optimizer then generates a distributed execution plan, splitting the work into sub-queries that can run independently on each remote node. This step handles predicate pushdown, join reordering, and aggregation splitting to minimize data movement.

Each sub-query is dispatched to a federated data node and executed locally against the institution's private database. The raw patient-level data never leaves the site. Only anonymized intermediate results—such as aggregate counts, summary statistics, or model gradients—are returned. This architecture enforces the data minimization principle required by HIPAA and GDPR.

The engine collects partial results from all participating nodes and applies a secure aggregation protocol to combine them. For statistical queries, this may involve summing counts or merging contingency tables. For federated learning, it uses algorithms like Federated Averaging (FedAvg) to weight and combine model updates. Cryptographic techniques such as secure multi-party computation (SMPC) can ensure the aggregator cannot inspect individual contributions.

Clinical data across institutions is rarely independent and identically distributed (non-IID). A robust engine accounts for site-specific schema mappings (e.g., different EHR systems), inconsistent coding practices (SNOMED CT vs. ICD-10), and varying population demographics. It uses data harmonization layers like the OMOP Common Data Model to translate local formats into a unified query interface.

In a distributed network, nodes can go offline, time out, or return errors. The engine implements retry logic with exponential backoff, speculative execution of slow tasks, and graceful degradation. If a site fails mid-query, the system can either exclude its partial results (with appropriate statistical adjustments) or wait for recovery, ensuring the overall analytical job completes reliably.

Even aggregate statistics can leak information through differencing attacks. A production-grade engine injects calibrated Laplacian or Gaussian noise into query results before assembly. The privacy budget (ε) is tracked across all queries against a dataset, and the engine automatically rejects queries that would exceed the configured threshold, providing mathematical guarantees against patient re-identification.

The architectural pattern that sends a single analytical query to multiple autonomous source databases, executes it locally at each site, and returns the combined result set without physically moving the underlying data. This is the foundational mechanism that a distributed query engine implements.

• Push-down computation: Logic is sent to the data, not vice versa
• Local execution: Each site runs the query against its own patient records
• Result assembly: The engine harmonizes partial counts and statistics into a global answer

A machine-executable algorithm that identifies a cohort of patients with a specific clinical condition from electronic health records using structured codes, laboratory results, and medication orders. The distributed query engine decomposes this phenotype into inclusion criteria and executes it across all nodes.

• Rule-based logic: Combines diagnosis codes, lab thresholds, and drug exposures
• Temporal constraints: Specifies time windows between qualifying events
• Portability: Must produce identical cohort definitions regardless of local data representation

A cryptographic method that allows a central server to compute the sum of model updates or statistics from multiple clients while ensuring that individual contributions remain private and unreadable. In the context of a distributed query engine, this protocol protects the intermediate contingency table counts returned from each site.

• Input privacy: The aggregator learns only the final sum, not individual site counts
• Threshold enforcement: Prevents inference attacks on small cell sizes
• Complementary to DP: Often combined with differential privacy noise addition

A computational system that statistically combines the results of independent studies to produce a single, more precise estimate of treatment effect. When a distributed query engine returns summary statistics from each site, the meta-analysis engine applies inverse variance weighting to pool effect sizes and generates a forest plot for visual assessment of heterogeneity.

• I-squared statistic: Quantifies the proportion of total variation due to heterogeneity
• Fixed vs. random effects: Chooses the appropriate pooling model based on heterogeneity assessment
• Cochran's Q test: Tests the null hypothesis that all studies share a common effect size

A legally binding contract that governs the terms, conditions, and permitted uses of a limited or de-identified dataset shared between institutions for research purposes. The distributed query engine operates within the constraints defined by these agreements, ensuring that only aggregate statistics—never patient-level data—cross institutional boundaries.

• Permitted queries: Defines which analytical operations are allowed
• Minimum cell size: Suppresses results from cohorts smaller than a threshold (e.g., < 10 patients)
• Audit trail: Requires logging of all queries for compliance review