Temporal Semi-Structured Data Format

• Temporal semi-structured data formats are a representation that explicitly captures time-evolving information within flexible, often hierarchical or flat schemas.
• The methodology employs concrete views with minimal temporal annotations and normalized interval data to reduce redundancy and facilitate precise query evaluation.
• Applications span temporal databases, biosensor analytics, session-based modeling, and visual data profiling, enhancing operational and scientific data interpretation.

Temporal semi-structured data formats are a foundational concept in data systems, analytics, and machine learning, enabling explicit representation, storage, querying, and reasoning over data that varies across time within loosely structured or hierarchical schemas (such as key-value sets, semi-structured tables, event logs, JSON, and XML). These formats are essential for handling use cases in temporal databases, longitudinal analytics, digital health, information retrieval, machine learning on log data, and temporal question answering, where both the values and the structural aspects of the data may evolve over time.

1. Core Design Principles and Model Structures

Temporal semi-structured data formats address the intersection between the requirements of temporal data modeling and the flexibility of semi-structured representation. The logical temporal relational data model (Mahmood et al., 2010) formalizes this intersection by classifying entities as temporal or non-temporal and attributes as time-varying, non-time-varying, or partially time-varying. The model isolates temporal attributes—such as a salary that changes over time—within dedicated temporal entities, while minimizing redundancy and preserving first normal form (1NF). Temporal relationships are explicitly separated from non-temporal ones, facilitating clear modeling of evolving data components.

Formally, a temporal relation schema is expressed as

$$TR = \langle \{A_1, A_2, \ldots, A_n, \text{activation\_start}, \text{activation\_end}, \text{updatetime}\}, K \rangle$$

where $K$ is the primary key, $A_i$ are data attributes, and the three additional attributes track when a tuple is valid and updated. Time is modeled as a discrete, totally ordered set $T \cong \mathbb{N}$, allowing flexible granularity (e.g., ticks, seconds, days). Tuples in temporal relations are thus annotated with time intervals, enabling precise reconstruction of historical states.

This minimal temporal annotation paradigm fits semi-structured settings, where only those data fragments that demonstrably change over time are augmented with explicit temporal markers, preserving schema simplicity and reducing redundancy. The approach enables seamless mapping between flat relational, hierarchical (e.g., XML), and nested (e.g., JSON) semi-structured representations.

2. Temporal Data Representation and Data Exchange

Temporal data formats can be represented at two semantic levels: the abstract view (as an infinite sequence of complete data snapshots for each time point) and the concrete view (as time-interval–annotated facts). The concrete view is operationally tractable, aggregating repeated facts with time interval annotations to produce a compact, implementable format (Golshanara et al., 2016). For instance, a record such as "Ada worked at IBM" over 2010–2013 is stored in the concrete view as (Ada, IBM, [2010, 2014)), rather than repeating the fact for each year.

The transformation and alignment between concrete and abstract representations are critical. Concrete views must be normalized: overlapping intervals are fragmented such that for any dependency, time variables act as constants in dependency-checking and query evaluation procedures. This enables robust enforcement of dependencies, such as tuple-generating dependencies (tgds) and equality-generating dependencies (egds), in temporal data exchange workflows. A concrete chase (c-chase) procedure is employed, ensuring that enforcement of source-target schema mappings in the temporal context mirrors what would be obtained in the ideal, infinite-snapshot abstract semantics.

Interval-annotated nulls (e.g., $N^{[s, e)}$) are introduced to capture incomplete or existentially quantified data whose identity is uncertain, propagating uncertainty explicitly over time intervals.

3. Practical Data Structures, Metadata, and Storage Layouts

Practical implementations of temporal semi-structured formats encapsulate numerical time series, multi-channel data, and associated metadata. The TSDF (Time Series Data Format) standard (Claes et al., 2022) exemplifies this approach for biosensor and physiological data:

• Numerical Data: Stored in raw binary files for efficiency; numeric arrays are multiplexed by row, where each row contains synchronized multi-channel sensor readings for a specific timestamp.
• Timestamps: Also stored as raw binary arrays (e.g., 32-bit unsigned integers), using relative, absolute, or difference encoding. For uniform sampling, only an initial time and frequency need to be stored.
• Metadata: A human- and machine-readable JSON file delineates all technical (e.g., data type, units, bit-width) and contextual (e.g., subject_id, device_id, start/end time in ISO8601) descriptors necessary to reconstruct and interpret the data. The hierarchical structure allows inheritance and overrides, supporting both flat and deeply nested temporal data.

This architecture ensures storage efficiency, flexibility, and random access capabilities, making it suitable for high-throughput health, IoT, and monitoring applications.

4. Querying, Normalization, and Temporal Reasoning

Query answering over temporal semi-structured data necessitates normalization procedures that fragment facts to ensure precise interval alignment. For conjunctive queries, every time-annotated tuple is expanded so that logical dependency enforcement can treat intervals as atomic constants (Golshanara et al., 2016). Naïve evaluation on the normalized, concrete instance—where interval-annotated nulls are temporarily replaced by fresh constants—recovers all certain answers, aligning with the abstract view semantics.

Temporal question answering in semi-structured tables requires models to perform arithmetic over date fields, order events, and infer durations and relationships both when the temporal data is explicit (e.g., date columns) and when it is implicit (e.g., ordinal or event-driven structures without explicit time indices). Datasets such as TempTabQA (Gupta et al., 2023) and TRANSIENTTABLES (Shankarampeta et al., 2 Apr 2025) expose the persistent gap between LLMs and human temporal reasoning, particularly for tasks requiring multi-step or implicit inference.

Recent approaches to improve LLMs’ temporal reasoning in such settings include:

• Structured prompting algorithms (e.g., C.L.E.A.R: Comprehend, Locate, Examine, Analyze, Resolve) (Deng et al., 22 Jul 2024) that encourage evidence-based, multi-step reasoning and grounding in tabular context.
• Indirect supervision via auxiliary pre-training on temporal reasoning datasets, which has been shown to yield significant gains over zero-shot or prompt-only methods.

Task decomposition, such as explicitly structuring temporal reasoning into sub-tasks (Information Retrieval, Information Extraction, Analytical Reasoning), further improves LLM performance (Shankarampeta et al., 2 Apr 2025).

5. Temporal Semi-Structured Formats in Data Mining and Machine Learning

The integration of temporal order within semi-structured representations supports a spectrum of data mining and ML tasks. For example, in the CERES framework (Feng et al., 2022), session data is modeled as an ordered graph of items (queries and products) with temporal (order) and semantic (attribute) edges. Temporal position embeddings and graph neural networks allow models to capture long-range, evolving interdependencies—critical for recommendation, search, and entity linking tasks.

In machine learning pipelines, tidy temporal data structures such as the "tsibble" in R (1901.10257) offer a standardized data frame format where an explicit time index and a "key" for repeated units (e.g., subject, country) are core schema elements. This structure generalizes across Irregular time intervals, mixed variable types, and heterogeneous event types, enabling fluent, reproducible transformations and end-to-end workflows for modeling and forecasting.

6. Visualization, Profiling, and Human-in-the-Loop Analytics

Modern visual analytics tools such as StructVizor (Huang et al., 9 Mar 2025) apply automated schema profiling, dynamic programming–based field alignment, and clustering to temporally ordered semi-structured text (such as logs). Temporal records are parsed and aligned to extract fields (timestamps, event types, codes), clustered by similarity using Hamming and Levenshtein-based measures, and mined for structural patterns (e.g., recurrent timestamp formats). The interface supports direct manipulation (drag-and-drop, in situ format conversion) and visually encodes positional and frequency information (area charts, heatmaps), facilitating rapid insight generation and easing data wrangling compared to traditional, script-based approaches.

Empirical user studies demonstrate that such interactive profiling significantly reduces analyst workload and accelerates sensemaking over large, irregular temporal textual datasets.

7. Applications and Broader Implications

Temporal semi-structured data formats enable rigorous representation and reasoning over time-evolving data in diverse application domains:

• Temporal databases: Auditing, history tracking, and compliance in financial and insurance records; healthcare patient records where diagnosis and treatment evolve temporally (Mahmood et al., 2010).
• Session-based modeling: User behavior tracking, e-commerce session analysis, personalization engines (Feng et al., 2022).
• Biosensor and IoT analytics: High-frequency physiological monitoring, sensor fusion, and cross-paper exchange via standardized formats (Claes et al., 2022).
• Temporal question answering: Factual inference and event sequence understanding from web-scale infoboxes (Gupta et al., 2023, Shankarampeta et al., 2 Apr 2025).
• Interactive data wrangling: Log analysis, anomaly detection, and operational monitoring using profile-based visual analytics (Huang et al., 9 Mar 2025).

The alignment of temporal semantics within semi-structured schemas—grounded by minimal and targeted temporal attributes, efficient normalization and querying algorithms, and robust metadata standards—remains a central pillar for scalable, reproducible scientific and operational data systems.

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This synthesis draws exclusively on the details, formalisms, and experimental findings presented in primary literature (Mahmood et al., 2010, Golshanara et al., 2016, 1901.10257, Feng et al., 2022, Claes et al., 2022, V et al., 2023, Gupta et al., 2023, Deng et al., 22 Jul 2024, Rückstieß et al., 23 Dec 2024, Huang et al., 9 Mar 2025, Shankarampeta et al., 2 Apr 2025).