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 assigRank, 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.

Due to the distributed nature of federated learning (FL), the vulnerability of the global model and the need for coordination among many client devices pose significant challenges. As a promising decentralized, scalable and secure solution, blockchain-based FL methods have attracted widespread attention in recent years. However, traditional consensus mechanisms designed for Proof of Work (PoW) similar to blockchain incur substantial resource consumption and compromise the efficiency of FL, particularly when participating devices are wireless and resource-limited. To address asynchronous client participation and data heterogeneity in FL, while limiting the additional resource overhead introduced by blockchain, we propose the Directed Acyclic Graph-based Asynchronous Federated Learning (DAG-AFL) framework. We develop a tip selection algorithm that considers temporal freshness, node reachability and model accuracy, with a DAG-based trusted verification strategy. Extensive experiments on 3 benchmarking datasets against eight state- of-the-art approaches demonstrate thatDAG-AFL significantly improves training efficiency and model accuracy by 22.7% and 6.5% on average, respectively.

More and more business applications are crossing organization boundaries and typically involves a set of interactive organizations, known as cross-organization business process management. By taking as input the distributed event logs of each organization, cross-organization process mining techniques can reconstruct the underlying business process model to help process comprehension and improvements. Unfortunately, existing process mining techniques completely ignore the privacy issue, i.e., the privacy of the event log and business process model is not guaranteed. To cope with this challenge, this paper proposes a privacy-preserving cross-organizational business process mining framework based on blockchain and cryptography. Specifically, it mainly includes three steps: (1) each organization builds its private business process model, interaction messages, and collaborative tasks from its event log; (2) collaborative public process model for each organization is generated based on blockchain using privacy security intersection (PSI) cryptography algorithms to ensure the privacy of each organization; and (3) each organization combines its private business process model with relevant public process models, to obtain an organization-specific collaborative business process model. Using four public cross-organization datasets, the privacy-preserving ability and application of the proposed technique is demonstrated.

In order to achieve high availability and low storage costs in distributed storage systems, erasure code is widely used instead of replication. Compared to replication, erasure code can reduce storage costs, but also brings higher repair costs. There are currently many repair algorithms to reduce the block reconstruction time of single block failure. However, applying the existing methods to multi-block failures may lead to unbalanced network traffic, unnecessary network transfers, and network congestion at data collection node during the repair process, which can not make full use of the bandwidth between nodes.To solve this problem, we propose a novel repair algorithm called Partial Merge Repair (PMR) for multi-block failures, which is a scheduling algorithm that considers network load between nodes and combines multiple failed blocks to recover together. It first divides all surviving nodes into different groups, and then the data collection nodes within the group collect the data needed to repair multiple blocks through cross merging. Finally, the data collection node sends the collected blocks to the repair node to complete the repair. Our study presents a formal definition and proof of network transfer time in the modeled repair process of PMR, highlighting its superior efficiency compared to existing methods in homogeneous environments.We implement a prototype of PMR to evaluate its performance. The experimental results indicate that compared to existing repair technologies, PMR improves repair throughput by 28%-256% for various scenes.

With the increasing scale of data and user demands for low latency, the development of large-scale clusters has become a trend. To ensure high availability of data in data clusters, XOR-based erasure code fault-tolerant technologies are widely used due to their low storage and computational overhead. Meanwhile, as the scale of clusters ranges from hundreds to thousands, the probability of multiple node failures is not negligible. This can lead to serious consequences, such as data loss, and should be recovered as soon as possible. However, codes such as RDP and EVENODD can easily lead to network congestion when recovering in the event of concurrent failures, making it challenging to recover quickly.To address this issue, we propose a novel network transfer plan search algorithm, Greedy Row-Diagonal Parity Search or GRS for short. GRS optimally allocates the network traffic generated during the repair process by greedily utilizing idle bandwidth and leveraging the commutative property of XOR operations, ensuring a more even distribution of traffic across the cluster network, which improves the repair throughput.We build a prototype in a distributed erasure-coded cluster and conduct experiment evaluation. The experimental results indicate that, compared to existing repair optimization methods, GRS improves repair throughput by 230%-880%.

Summary Event log sampling has emerged as a key research focus in the field of process mining, aiming to enhance the efficiency of various process mining tasks, including model discovery, conformance checking, and process prediction. However, current log sampling techniques often fail to ensure high‐quality sample logs. This paper introduces a novel framework to support efficient event log sampling without compromising the quality of the sample log compared to the original one. The approach revolves around the consideration of directly‐follows relation (DFR) among business tasks as the fundamental behavior unit of an event log. By ensuring the DFR equivalence between the original and sample logs, the proposed technique addresses the challenge of sample log quality from the model discovery point of view. The framework is instantiated by seven distinct sampling strategies each has its own specialty and is fully implemented in the open‐source process mining tool platform ProM. To validate its effectiveness, we conducted a comprehensive experimental evaluation using 12 publicly available real‐life event logs against state‐of‐the‐art sampling techniques. The results clearly demonstrate that our technique significantly improves model discovery efficiency while upholding high quality of the discovered models.

Healthcare plays an increasingly essential role in our daily life. Modern Hospital Information Systems (HISs) record and store detailed medical treatment process information for all patients as event logs. By taking event logs as input, process mining techniques have been widely applied to extract valuable insights to improve medical treatment processes and deliver better healthcare services. However, considering the complexity of collaborations among different medical departments, existing model discovery techniques cannot be applied directly. To handle this limitation, this paper proposes a novel approach to support the discovery of Cross-department Collaborative Healthcare Process (CCHP) models from medical event logs. Specifically, an extension of classical Petri Nets with message and resource attributes is first introduced to formalize CCHPs. Then, a novel discovery algorithm is proposed to discover Intra-department Healthcare Process (IHP) models. Next, collaboration patterns among medical departments are formalized and corresponding discovery algorithms are given on that basis. Finally, a global CCHP model is obtained by integrating all discovered collaboration patterns and IHP models. By using four public medical event logs, we quantitatively compare our approach with the state-of-the-art process mining techniques in terms of model quality, and our experimental results demonstrate that the proposed approach can discover more accurate healthcare process models.Note to Practitioners—The recorded medical event logs by HISs can be used to extract valuable insights for the analysis of healthcare processes. However, existing process model discovery techniques cannot be applied for the analysis directly due to the complex collaborations among different medical departments of a hospital. This paper introduces a novel approach for cross-department collaborative healthcare process model discovery from medical event logs. All proposed techniques are fully implemented and publicly available. Using four public medical event logs, we show the applicability and advantages of our approach against existing ones. The proposed techniques are applicable to the model discovery and behavior understanding of real-life operational healthcare processes.