temporal alignment

This work addresses the challenge of accurately identifying clinically meaningful high-order composite events—such as disease onset—from timestamped raw data. The authors propose a logic-rule-based framework that constructs high-order events by combining existence and termination conditions of atomic events, augmented with consistency constraints and a repair mechanism to eliminate invalid combinations. Innovatively integrating logical rules, temporal reasoning, and constraint optimization, the approach identifies—for the first time—a computationally tractable fragment that balances expressive power with polynomial-time data complexity. Implemented via answer set programming, the core inference module demonstrates feasibility in a lung cancer case study, yielding results highly consistent with clinical expert judgments and highlighting its potential for healthcare and other temporal data analysis applications.

Communication research has long faced a methodological tension between static analytical approaches and the inherently dynamic nature of diffusion processes, hindering fine-grained temporal modeling of digital traces. To address this, we propose “hyper-longitudinal analysis”—the first systematic, user-level dynamic diffusion framework centered on raw, unaggregated time series, preserving full temporal structure without dimensional reduction. Integrating six computational paradigms—sequence analysis, process mining, language modeling, temporal pattern discovery, trajectory clustering, and behavioral modeling—we establish a theory-guided, computationally synergistic pipeline. Validated on 1.26 million donation-related digital traces from 309 users, our approach uncovers three key empirical regularities: (1) recurrent periodicity in sharing behavior, (2) path-dependent propagation mechanisms, and (3) heterogeneous individual-level temporal patterns. The framework provides a scalable, generalizable methodological foundation for dynamic communication modeling.

This study addresses the challenges of low matching accuracy and poor computational efficiency in map-matching low-frequency GPS trajectories within dense road networks. To this end, the authors propose an enhanced spatiotemporal trajectory matching method that integrates a dynamic buffer mechanism, an adaptive observation probability model, an improved temporal scoring function, and a path inference strategy based on historical behavioral patterns. Notably, the approach achieves high-quality matching without requiring ground-truth annotations. Experimental evaluation on real-world trajectory data from Milan demonstrates the superiority of the proposed method, with significant improvements across multiple metrics compared to existing algorithms, while simultaneously maintaining high computational efficiency and accurate path reconstruction.

This study addresses the reliability degradation of event timelines in digital forensics due to user-initiated timestamp tampering on live systems. Conducting a qualitative user study with advanced students, we employ trace analysis, timestamp dependency modeling, and second-order trace resolution path induction to systematically uncover the “cognitive–technical” coupling barriers inherent in timestamp manipulation—a first-of-its-kind investigation. We propose a reliability assessment framework grounded in trace knowledge depth and modification feasibility, identifying core determinants of tampering success—including temporal trace recognition capability and kernel- or filesystem-level constraints. The framework provides empirically validated criteria for time-based evidentiary trustworthiness grading, enabling more accurate and robust forensic timeline reconstruction. Results demonstrate significant improvements in both precision and resilience of event reassembly under adversarial timestamp modification scenarios.

High-precision online estimation algorithms for robotics are highly sensitive to sensor timestamp accuracy; however, existing synchronization solutions struggle to simultaneously achieve real-time operation, low cost, and high temporal precision. To address this, we propose a real-time, trigger-based time synchronization system built on commodity hardware. Our approach employs a hardware-triggered mechanism to jointly schedule heterogeneous sensors operating at different frequencies, and integrates an enhanced clock synchronization protocol with nanosecond-resolution timestamping to ensure precise coordination between sensors and the onboard computer. Crucially, the system eliminates reliance on expensive dedicated timing hardware, thereby substantially mitigating the impact of timing errors on online estimation. Experimental evaluation on a physical robot platform demonstrates sub-microsecond synchronization accuracy, along with significant improvements in both estimation robustness and real-time performance.

For large-scale discrete-event systems, analyzing state-transition event time series (STE-ts) suffers from reliance on distortion-prone resampling and difficulty in jointly modeling transition timing and state dwell duration. To address this, we propose the Selective Time Hamming (STH) distance, which directly encodes event occurrence times and state residence durations without resampling. STH unifies Hamming distance and Jaccard similarity within a single metric and supports multi-state focused matching. It preserves computational efficiency while significantly improving pattern recognition accuracy. Experiments on synthetic and real-world datasets demonstrate that STH achieves an average 2.3× speedup over baseline methods and improves clustering and anomaly detection F1-scores by 12.6%.

Existing data discovery methods largely overlook the temporal dynamics of data relationships, particularly hindering timeliness-aware retrieval when explicit temporal metadata is absent. To address this, we propose “temporal-effective data discovery”—a novel paradigm introducing the first time-integrated data discovery system. Its core comprises four techniques: version discovery, temporal lineage inference, change-log synthesis, and time-aware querying. Crucially, the system models semantic evolution of data over time without relying on original timestamps, enabling dynamic lineage tracing and version-level precise retrieval. Experimental evaluation on enterprise and public data lakes demonstrates significant improvements in accuracy and practicality for timeliness-sensitive queries. Our work establishes a new research direction in data discovery centered on temporal evolution, advancing both theoretical foundations and real-world applicability.

This work addresses the challenge of users struggling to pinpoint specific moments in meeting discussions based solely on content. To overcome this, the paper proposes a novel approach that reframes timestamp prediction as a constrained candidate selection task. Instead of directly generating timestamps, large language models such as Mistral-7B-Instruct are guided to select the most relevant segment from a set of retrieved, timestamped meeting excerpts, thereby avoiding unsupported or invalid predictions. Integrating retrieval-augmented generation (RAG) with a constrained selection mechanism, the method demonstrates significant improvements on a dataset of 200 municipal meetings and 420 queries: Recall@5 increases from 31.9% to 50.0%, mean absolute error decreases to 761 seconds, and the number of valid outputs rises from 373 to 419, substantially enhancing both accuracy and reliability in temporal localization.

Existing generative models for synthetic temporal tabular data often produce temporally inconsistent outputs, such as time reversals, repetitions, or implausible trajectories, yet conventional evaluation methods fail to detect these issues due to their neglect of the temporal dimension. This work proposes the first systematic evaluation framework tailored for temporal tabular data, which dynamically selects assessment criteria based on four key properties: temporal representation, sampling regularity, trajectory dependency, and pattern structure. The framework holistically evaluates timestamp validity, cross-sectional structural coherence, entity-level dynamic evolution, and time-varying relationships. By elevating both utility and privacy assessments from static records to the trajectory level, it reveals—across 13 real-world datasets—that traditional evaluations significantly diverge from temporally aware results, with failure modes closely tied to model architecture, thereby underscoring the necessity of explicitly modeling the temporal axis.

Existing approaches struggle to effectively model heterogeneous tensor streams comprising both categorical and continuous attributes, and often fail to capture the temporal dynamics of group anomalies due to timestamp discretization. This work proposes HeteroComp, a novel method that, for the first time, unifies the modeling of heterogeneous tensor streams without discretizing timestamps or attributes. By employing a Gaussian process prior, HeteroComp jointly characterizes the distribution of continuous attributes and their temporal evolution, enabling direct probabilistic density estimation. This framework continuously refines latent components that represent underlying groups and their dynamics. Evaluated on real-world datasets, HeteroComp significantly outperforms state-of-the-art methods in group anomaly detection accuracy while maintaining computational complexity independent of the data stream length.