Next activity prediction, a critical subtask of Predictive Process Monitoring (PPM), aims to forecast the upcoming activity of an ongoing process instance based on event logs generated by process-aware information systems, thereby enabling businesses to proactively mitigate potential risks. While recent deep sequence models have shown strong predictive capabilities, they often fail to explicitly capture the intrinsic dependencies that govern the conditions and impacts of event occurrences in business processes. To address this limitation, we propose a novel Place-Transition Embedding (PTE) framework that captures these dependencies through Petri-net–inspired token consumption and generation semantics. PTE employs a deep embedding network to simulate the evolution of markings, i.e., how each observed event drives token dynamics over places over time under our resource interpretation. In addition, we introduce a temporal scoring mechanism that ranks candidate activities by jointly considering resource feasibility and temporal urgency, improving both interpretability and predictive accuracy. Extensive experiments on six real-world datasets show that PTE achieves state-of-the-art performance for next-activity prediction. Moreover, PTE generalizes robustly under sparse training conditions, highlighting its practical applicability in real-world scenarios with limited business data.

Blockchain has gained increasing attention for managing eHealth data, yet most existing ledger designs face fundamental challenges in simultaneously enforcing strict privacy protections, enabling low-latency data queries, and supporting transparent AI-driven decision-making. This paper proposes a novel blockchain ledger architecture integrating Explainable Artificial Intelligence (XAI) through SHapley Additive exPlanations (SHAP) and statistical metadata for privacy-preserving and efficient query processing in eHealth applications. Our method introduces dynamic, expert-informed sensitivity classification and interpretable SHAP values directly embedded in block headers. The integration significantly reduces query data requirements by approximately 99.78%, maintaining patient data privacy through role-based access control and metadata-driven querying. Empirical validation in an IoMT-enabled healthcare scenario confirms substantial query efficiency, preserving privacy and transparency improvements, justifying the computational overhead associated with metadata creation. The results highlight the practical utility and robustness of our proposed blockchain architecture for rapid clinical decision-making.

Online user interactions produce privacy-sensitive data, necessitating data governance frameworks that respect user-defined privacy preferences. Nevertheless, the centralized storage and management of such preferences encounter significant interoperability challenges across different domains. By combining on-chain and off-chain storage paradigms, hybrid-storage blockchains have developed into a reliable infrastructure. Their value lies not only in ensuring secure data storage, but also in providing a practical foundation for data governance, especially in complex and cross-domain environments. In this paper, we propose LiveIndex, an index-driven hybrid-storage blockchain framework for cross-domain data governance. First, we design an on-chain data index based on distributed chameleon hash (DCH) to enable efficient off-chain data governance. This index incorporates essential elements, including metadata tags, data addresses, privacy ciphertexts, and group signatures. Second, we employ asymmetric searchable encryption (ASE) to protect privacy by converting analysis policies into search trapdoors and embedding privacy attributes into ciphertexts, thereby supporting compliant and secure data analysis. Third, we integrate proxy re-encryption (PRE) to strengthen control over cross-domain data, using re-encryption keys to ensure secure data authorization. Finally, we implement and evaluate LiveIndex on public datasets. The results show time overheads ranging from milliseconds to microseconds, demonstrating the practicality and efficiency in real-world scenarios.

The Internet of Medical Things (IoMT) has attracted the attention of many scholars because of its revolutionary impact on healthcare services. However, the sensitivity of medical data and the complexity of IoMT architectures pose critical challenges to privacy-preserving service design. This survey offers a comprehensive and novel investigation and classification of privacy and security issues in IoMT. We explore the issue from four distinct angles: privacy protection technologies, IoMT architecture, data type variations, and emerging technologies, with each section delving into more detailed analysis. Specifically, privacy protection technologies include cryptography method, Federated Learning (FL), blockchain technology and Differential Privacy (DP). We provide a more detailed classification for popular technologies such as blockchain. Regarding the IoMT architecture, we dissect the attack and defense mechanisms at the perception layer, the transmission and cloud layer, and the application layer. In terms of data types, we discuss the research on privacy protection based on the structural differences of various medical data. In the aspect of emerging technologies, we investigated the impact of four emerging technologies on privacy protection of IoMT. Our survey highlights the comprehensive and novel perspective of the characteristics of IoMT privacy protection, which will promote possible booming research in the future.

Large volumes of event logs have been accumulated by business information systems. Accompanied by that, various process discovery techniques are invented to uncover underlying business processes based on event logs. Event log sampling, recognized as one of the most effective techniques for accelerating discovery efficiency, has gained significant attention in recent days. However, achieving high performance in sampling while maintaining superior sample log quality remains a challenge for current techniques. To tackle the problem, a novel event log sampling technique, denoted as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">sigRank</i>, is introduced to improve both the sampling efficiency and the quality of the sample log by quantifying the significance of each trace. The proposed sampling technique has been implemented as a publicly available tool in the open-source process mining platform ProM. Compared with state-of-the-art techniques using 12 public event logs, we experimentally illustrate that the proposed approach can significantly accelerate sampling efficiency while guaranteeing superior sample log quality for process discovery.

Deploying ultra-reliable and low-latency communication (URLLC) service function chains (SFCs) is imperative for applications demanding stringent latency and reliability performances. In these applications, ensuring uninterrupted service hinges on establishing fault-disjoint primary and backup service function paths (SFPs). However, existing techniques for deploying SFCs fall short in optimizing latency differentials between the primary and backup SFPs, posing risks of service disruptions in critical URLLC applications like remote surgery, smart factory, and unmanned vehicle systems. In this work, we investigate pioneering techniques to efficiently optimize the primary and backup SFP latencies while minimizing their differentials. We formally formulate the problem of ultra-reliable and low-latency SFC deployment (URLLC-SD) and show its NPhardness. We develop an innovative algorithm, the Yen-based SFP Identification in Layered Graph (YANG), which optimizes the equal-weight composite latency objectives with symmetric QoS/SLA for primary and backup SFPs at the expense of runtime complexity. Through extensive simulations, we demonstrate the YANG's superiority, surpassing state-of-the-art benchmarks. In particular, YANG achieves the highest acceptance rates under specific constraints on SFP latency and latency differentials. Furthermore, our analysis reveals interesting insights into the selection of good path candidates to optimize URLLC-SD.