TempoQL: Temporal Query Languages

• TempoQL is a family of temporal query languages and frameworks that extend SQL and domain-specific languages to support complex, time-based queries.
• They utilize formal grammars and temporal operators like window predicates and bitemporal logic to enable precise reasoning over structured time data.
• Applied in healthcare, temporal databases, resource scheduling, and deep learning, TempoQL emphasizes readability, reproducibility, and cross-system portability.

TempoQL denotes a family of temporal query languages, frameworks, and systems emerging across database, machine learning, and health informatics research. The term encompasses proposals for SQL temporal extensions, language-integrated query calculi, resource scheduling SLO languages, ontology-driven temporal DLs, modern domain-specific languages for time-centric EHR data, and deep learning architectures for temporal reasoning. Major representatives include the Python-based EHR temporal query DSL (Ma et al., 12 Nov 2025), SQL3 temporal extensions (Mkaouar et al., 2011), SLO deployment in multi-tenant resource management (Tan et al., 2015), ontology-based temporal description logics (Artale et al., 2013), temporal LINQ/D-SQL for general temporal databases (Fowler et al., 2022), and neural temporal query networks for multivariate forecasting (Lin et al., 19 May 2025). These threads are unified by their aim to enable precise, expressive, and efficient temporal reasoning over time-structured data, often with strong emphases on readability, portability, formal semantics, and integration with heterogeneous storage backends and query infrastructures.

1. Motivations and Application Settings

TempoQL systems target domains where temporal semantics are critical to query expressivity and reproducibility:

• Healthcare/EHR analytics: High heterogeneity across OMOP, MIMIC-IV, MEDS, and local standards; cohort definitions require complex, portable temporal logic; precise event and window compositions are essential for clinical ML (Ma et al., 12 Nov 2025).
• Temporal databases: Need for concise manipulation/querying of valid and transaction time; standard SQL’s lack of built-in support led to development of extensible query languages and temporal relational algebra (Mkaouar et al., 2011, Fowler et al., 2022).
• Multi-tenant database systems: Resource managers (e.g., YARN, Mesos) lacking native SLO support; declarative SLO languages for performance objectives, compiled into quantitative metrics for self-tuning optimizers (Tan et al., 2015).
• Temporal knowledge representation: OBDA (ontology-based data access) over time-stamped facts, via temporal extensions to DL-Lite (TQL/TempoQL) supporting first-order rewritability for conjunctive queries (Artale et al., 2013).
• Multivariate time series forecasting: Modeling stable global and instance-level temporal correlations using temporal query networks (TQNet), combining temporal query attention with efficient deep architectures (Lin et al., 19 May 2025).

The broad objective is to lower technical barriers, rigorously encode temporal relationships, and support model-building and analysis across diverse, evolving data ecosystems.

2. Syntax, Semantics, and Language Features

Core Grammar and Temporal Constructs

TempoQL instances share formal grammars—either as EBNF for domain-specific languages, custom SQL3 extensions, or embedded language-integrated query calculi.

• EHR-centric DSL (Ma et al., 12 Nov 2025): Features a precise, human-readable grammar supporting:
  • Data-element queries: {"Platelet; scope=Lab"}
  • Arithmetic/logical/aggregate expressions: rolling, timed, event-centered
  • Window specification: from #now - 1 day to #now every 1 day
  • Logical composition: conjunctions/disjunctions/negation in Boolean predicates
  • Temporal alignment: event PRECEDES, WITHIN, rolling/anchor-based intervals
• SQL3 extension (Mkaouar et al., 2011): Introduces orthogonal tempo-operators (e.g., HISTORY, PAST, FUTURE, @DATE, BETWEEN, WHEN, SINCE/BEFORE/AFTER), temporal grouping (GRANULE), and update primitives (TAG ON, CORRECT, soft delete + VACUUM).
• Resource SLO specification (Tan et al., 2015): BNF grammar for SLO statements:
  • SLO ON BI: AvgResponseTime() < 120s SLACK(0.1) PRIORITY(5);
  • Metrics: avg response time, deadline miss %, utilization, throughput, fairness
• Temporal DLs (Artale et al., 2013): Concept and role constructors for time-stamped ABox atoms and TBox inclusions, supporting past/future operators ($\Diamond_{p}$, $\Diamond_{f}$), rigid/persistent/instantaneous notions.
• LINQ-style temporal query (Fowler et al., 2022): Typed syntax for transaction/valid time, explicit row periods, sequenced/nonsequenced modifications, and normal forms for sequenced joins.

Semantics

• Temporal operators manipulate explicit time domains—valid time (when a fact holds) and transaction time (when a fact is stored), as bitemporal intervals (Mkaouar et al., 2011, Fowler et al., 2022).
• Windowed predicates evaluate aggregates over explicit or rolling intervals.
• Event alignment and time anchoring are natively expressed (e.g., “exists A after B,” “within 90 days of event C”).
• Non-destructive updates and provenance for data corrections and soft/hard deletion.

3. Architecture, Portability, and Execution

Portability Layers

• Schema mapping and abstraction: DSLs translate logical data element queries into backend-specific SQL/ORM or columnar operations, via a JSON/YAML “dataset specification” defining table/column mappings and code joins (Ma et al., 12 Nov 2025).
• Language-integration: λ_TLINQ/λ_VLINQ (Fowler et al., 2022) and similar calculi enable host language embedding, compiling temporal operations to core LINQ structures (and ultimately SQL), ensuring RDBMS independence.
• Cross-standard deployment: Code and queries are backend-agnostic—identical logic runs on OMOP, MIMIC-IV, eICU, Parquet, and custom schemas, provided the mapping is configured (Ma et al., 12 Nov 2025).

Execution Pipeline

• Parsing: DSL → AST
• Logical planning: Transformation into data retrieval and transformation DAG
• Physical planning: Backend-specific implementation (SQLAlchemy, Pandas, etc.)
• Execution: Data retrieval, filtering, aggregation, imputation
• Profiling: Real-time feedback on query volumes, missingness, value distributions

Performance

Empirical benchmarks demonstrate:

• Millisecond to sub-second query execution for 1K–50K EHR stays and millions of rows (Ma et al., 12 Nov 2025).
• Complexity driven by $O(n \log n)$ sorting and $O(n)$ window/reduction per trajectory ($n$ = number of events).
• Performance comparable or superior to equivalent BigQuery SQL for most aggregation types.

4. Representative Use Cases and Applications

Healthcare and Life Sciences (Ma et al., 12 Nov 2025)

• Cohort extraction: Expresses nuanced temporal inclusion/exclusion (e.g., “no prior AKI within 90d before first semaglutide RX”).
• Feature aggregation: Supports rolling window, carry-forward, imputation, and discretization logic uncommon in traditional cohort builders.
• Cross-institution generalization: Queries are portable across OMOP, MIMIC-IV, eICU, and site-specific data.

Example:

(exists aki_outcome from first_rx to first_rx + 90 days) where not exists aki_outcome before first_rx with first_rx as ( first starttime(semaglutide_rx) from #mintime to #maxtime with semaglutide_rx as {name in ("semaglutide","Ozempic"); scope=Drug_exposure} and aki_outcome as {SNOMED; id=438949} )

Database Systems (Mkaouar et al., 2011, Fowler et al., 2022)

• Native temporal SQL3 querying: Clean time-specification in selection, join, and aggregate clauses.
• Non-destructive evolution: Corrects or retags valid-time intervals without physical overwrite.
• Bitemporal queries: Transparent manipulation of valid + transaction time.

Resource Management (Tan et al., 2015)

• Declarative SLO layers for multi-tenant RMs, directly encoding quantitative performance objectives.
• Pareto-optimal tuning: Statement compilation to vector-valued SLO constraints, optimized via stochastic gradient descent in the PALD framework.

Ontology and Temporal Reasoning (Artale et al., 2013)

• Temporal OBDA: Temporal DLs enable first-order rewritability of CQ answering over valid-time EHR or administrative records.
• Schema evolution and reasoning: Models rigid, persistent, and event-based dynamics at the TBox/ABox level.

Deep Temporal Query Networks (Lin et al., 19 May 2025)

• Stable attention for MTSF: Global dataset-level correlations via periodic query vectors, local sample-level with projected keys/values, driving state-of-the-art forecasting on high-dimensional time series.

5. Practical Considerations: Authoring, Reproducibility, and User Interfaces

• Human-readability: EHR-centric TempoQL is pseudo-natural, structured for both technical and clinical inspection. Notably, queries are exported/stored directly in modeling code repositories for provenance.
• Interactive interfaces: Built-in notebook UIs expose (1) auto-completion, (2) result profiling, (3) shortcut insertion for “data elements,” and (4) LLM-assisted authoring, debugging, and explanation through function-calling (Ma et al., 12 Nov 2025).
• Porting/logical versioning: TempoQL queries, stored in JSON/Markdown, are modular and easily version-controlled for reproducibility audits.
• LLM integration: In-context examples and detailed prompts power generative LLMs (e.g., Gemini 2.5 Pro) to recommend/adapt/clarify queries, search concepts, and synthesize TempoQL from natural language instructions.
• Error handling: Built-in LLM feedback and diagnostic tools aid non-expert users in refining queries in real-time, lowering the expertise threshold for high-fidelity temporal cohort specification.

6. Limitations and Future Research

• Backend limitations: Most current implementations perform local (Pandas/NumPy) aggregations, with plans for more aggressive SQL pushdown to minimize data transfer cost (Ma et al., 12 Nov 2025).
• User studies: While initial adoption is strong, rigorous clinical and multi-institutional usability/accuracy validation is ongoing.
• Extension to unstructured data: Integration of clinical notes and other unstructured sources via IE pipelines is an open area.
• Higher-dimensional scalability: For deep learning temporal query networks, scaling to very high-dimensional ($C \gg 1000$) settings demands further architectural innovations (sparse attention, low-rank modeling) (Lin et al., 19 May 2025).
• Theoretical boundaries: Certain temporal DL extensions (e.g., temporal operators on right of inclusions, next/previous-time) break first-order rewritability or increase complexity (Artale et al., 2013).
• Future capabilities: Bitemporal support, richer aggregation models, advanced grouping, and flattening of nested temporal results are active research directions across all flavors of TempoQL.

7. Comparative Positioning and Impact

TempoQL systems are distinguished by their focus on:

• Unified temporal abstraction: Orthogonal temporal constructs decouple time logic from physical schema or backend, whether in SQL3, resource scheduling domains, EHR/healthcare applications, or deep neural forecasting.
• Formal semantics: Explicit grammar and translation pipelines ensure correctness, composability, and reproducibility—often with accompanying metatheoretical results (type soundness, translation correctness, FO-rewritability).
• Human-centric design: Contrasted with both raw SQL (verbose, error-prone for complex time logic) and GUIs (expressivity-limited), TempoQL enables domain experts to write, validate, critique, and port queries and analytical pipelines—integral to open, reproducible science.
• Operational robustness: In resource management, the SLO-TempoQL loop achieves max-min fairness and Pareto-optimal deployments under process noise and shifting demand. In temporal cohort engineering, continuous feedback, explainability, and versioning minimize analytic drift.
• Cross-domain extensibility: The underlying principles of TempoQL generalize: methodical temporal logic improves temporal query and analysis quality in domains as disparate as high-frequency financial data, bioinformatics, medical informatics, and industrial ML.

TempoQL thus serves as an archetype for temporal query languages striving for the dual ideals of expressive power and operational usability, foundational to modern data management, health informatics, and time-sensitive analytics (Ma et al., 12 Nov 2025, Mkaouar et al., 2011, Tan et al., 2015, Artale et al., 2013, Fowler et al., 2022, Lin et al., 19 May 2025).