align timestamps

In distributed systems, clock asynchrony impedes fair and deterministic total ordering of events. Method: This paper proposes a probabilistic event serialization framework that replaces the deterministic “happened-before” relation with a novel “likely-happened-before” probabilistic ordering. It models per-node clock skews as time-varying probability distributions and performs Bayesian comparison of noisy timestamps to infer the most probable causal order. Crucially, it requires no strong clock synchronization assumptions and operates online. Contribution/Results: The approach enables fine-grained, statistically fair total order generation without global consensus or physical clock alignment. Experiments demonstrate accurate estimation of relative occurrence probabilities between events. The method provides a provably sound, scalable foundation—both theoretically and empirically—for achieving system-wide fairness in asynchronous environments.

This work addresses the challenge of fair event ordering in distributed systems, where clock synchronization errors complicate deterministic sequencing. The authors propose Tommy, a novel sequencer that, for the first time, integrates social choice theory into event ordering. By modeling the statistical characteristics of clock synchronization errors, Tommy probabilistically compares noisy timestamps and reformulates the ordering task as a social choice problem, yielding a partially ordered sequence. Rather than attempting to eliminate clock inaccuracies, Tommy explicitly tolerates them, effectively handling the non-transitivity inherent in probabilistic comparisons. This approach ensures fairness in a probabilistic sense while significantly outperforming baseline methods based on Spanner’s TrueTime.

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.

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.

Event logs often lack formal process models, hindering compliance checking. Method: This paper proposes a declarative, model-free approach to automatically mine and adapt best-practice constraints from multi-source reference process models. It introduces (i) automated extraction of transferable constraints based on LTL/Declare templates; (ii) a log-driven relevance scoring mechanism to dynamically select semantically matched constraint subsets; and (iii) lightweight compliance assessment via multi-model aggregation and violation detection. Contribution/Results: Experiments on real-world model repositories and event logs demonstrate that the method significantly improves detection rates of best-practice violations, reduces modeling effort, and enables cross-organizational, quantitative analysis of process health.

Network congestion induces delay jitter in time synchronization packets, significantly degrading the clock synchronization accuracy of protocols such as NTP and PTP. This work proposes a lightweight congestion marking mechanism that leverages existing unused fields in IP, PTP, or NTP headers to tag synchronization packets experiencing queuing delays on programmable switches (Tofino platform), without requiring deep packet inspection or protocol modifications, thereby preserving backward compatibility. At the receiver, statistical filtering strategies—combining minimum RTT and median delay estimates—effectively discard congestion-affected packets. Experimental results demonstrate that the proposed approach improves synchronization accuracy by over 80% in single-hop scenarios and reduces clock offset estimation error by 30%–80% in multi-hop environments, achieving up to a 90% performance gain over conventional filtering methods.

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.

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.