Detecting anomalies in business processes is imperative for achieving operational success, especially as multiple participants increasingly engage in collaborative efforts to complete processes, i.e., collaborative business processes, amidst rapid economic development. However, existing business process anomaly detection approaches are typically designed for centralized training, making them impractical for collaborative business processes that require stringent privacy measures. In this paper, we introduce a feDerated Unsupervised AnomaLy detection framework for collaborative business processes, named DUAL. DUAL enables participants to collaboratively detect anomalies via a third-party coordinator, which constructs a global view from the hidden representations of participants' local sub-traces without exposing the sub-traces themselves. To further strengthen privacy protection, DUAL injects differential privacy noise into the transmitted hidden representations. To mitigate the global-local discrepancy, we introduce novel event execution errors which indicate whether the client who is supposed to execute the reconstructed event is consistent with the observed one. Extensive experiments demonstrate that DUAL effectively detects anomalies in collaborative processes while preserving privacy, achieving performance comparable to a centralized setting that requires access to participants' raw sub-traces.

Detecting anomalies in business processes is crucial for ensuring operational success. While many existing methods rely on statistical frequency to detect anomalies, it's important to note that infrequent behavior doesn't necessarily imply undesirability. To address this challenge, detecting anomalies from a semantic viewpoint proves to be a more effective approach. However, current semantic anomaly detection methods treat a trace (i.e., process instance) as multiple event pairs, disrupting long-distance dependencies. In this paper, we introduce DABL, a novel approach for detecting semantic anomalies in business processes using large language models (LLMs). We collect 143,137 real-world process models from various domains. By generating normal traces through the playout of these process models and simulating both ordering and exclusion anomalies, we fine-tune Llama 2 using the resulting log. Through extensive experiments, we demonstrate that DABL surpasses existing state-of-the-art semantic anomaly detection methods in terms of both generalization ability and learning of given processes. Users can directly apply DABL to detect semantic anomalies in their own datasets without the need for additional training. Furthermore, DABL offers the ability to interpret anomalies' causes in natural language, providing valuable insights into the detected anomalies.

Event sequence anomaly detection has garnered considerable attention in research, encompassing applications such as identifying anomalies in system logs, anomalous transaction users, and so on. Yet, prevailing anomaly detection methods often rely solely on local data for training, potentially leading to imperfect detection performance. In this article, we introduce a personalized Federated anomaly detection framework for discrete event Sequences, named FeadSeq. Specifically, we propose a separate architecture for sequence reconstruction networks (SEPRE) which partitions the network into two parts: a shared part and a standalone part, better suited for federated learning schemes. In tandem, we propose a novel partial shared federated learning scheme that employs a mask strategy to alleviate communication overhead and produce personalized local models to address the statistical heterogeneity of data among clients. This scheme dictates that a subset of weights is communicated between clients and servers for collaborative training, while the remaining weights are trained exclusively locally. To evaluate the effectiveness of FeadSeq, we conduct extensive experiments on both system logs and business process event logs. The results affirm the superiority of FeadSeq over existing personalized federated learning algorithms, showcasing not only improved performance but also reduced communication overhead.

Effective management of business processes is crucial for organizational success. However, despite meticulous design and implementation, anomalies are inevitable and can result in inefficiencies, delays, or even significant financial losses. Numerous methods for detecting anomalies in business processes have been proposed recently. However, there is no comprehensive benchmark to evaluate these methods. Consequently, the relative merits of each method remain unclear due to differences in their experimental setup, choice of datasets and evaluation measures. In this paper, we present a systematic literature review and taxonomy of business process anomaly detection methods. Additionally, we select at least one method from each category, resulting in 16 methods that are cross-benchmarked against 32 synthetic logs and 19 real-life logs from different industry domains. Our analysis provides insights into the strengths and weaknesses of different anomaly detection methods. Ultimately, our findings can help researchers and practitioners in the field of process mining make informed decisions when selecting and applying anomaly detection methods to real-life business scenarios. Finally, some future directions are discussed in order to promote the evolution of business process anomaly detection.

The ability to detect anomalies in business processes is crucial for achieving success in business operations. While unsupervised anomaly detection approaches have gained popularity in recent years due to their label-free nature, in some cases, a limited number of labelled anomalies can be provided and using them can improve the performance of anomaly detection. To address this issue, we propose a novel framework for anomaly detection that uses a pre-trained autoencoder to extract feature representations of traces. An anomaly score generator based on a multi-layer perceptron is utilized to evaluate the extracted features. The entire framework is trained using a joint loss that ensures the generated anomaly scores satisfy a specific distribution without compromising the autoencoder's ability to reconstruct normal traces. The feature encoder is fine-tuned to provide insights into the cause of anomalies. Additionally, we design a novel technique for calculating anomaly scores to mitigate the effects of varying numbers of potential attribute values. We conduct extensive experiments on both synthetic and real-life logs, and our results demonstrate that our proposed method, WAKE, outperforms state-of-the-art unsupervised deep business process anomaly detection methods by a significant margin. Additionally, it outperforms other weakly supervised anomaly detection methods as well

With the development of cloud computing and the advent of the Web 2.0 era, composing a set of Web services as a service process is becoming a common practice to provide more functional services. However, a service process involves multiple service invocations over the network, which incurs a huge time cost and could become a bottleneck to performance. To accelerate its execution, we propose an engine-side cache-block-based service process caching strategy (CBPCS). It is based on, and derives its advantages from, three key ideas. First, the invocation of Web services embodies semantics, which enables the application of semantic-based caching. Second, cache blocks are identified from a service process, and each block is equipped with a separate cache so that the time overhead of service invocation and caching can be minimized. Third, a replacement strategy is introduced taking into account time and space factors to manage the space allocation for a process with multiple caches. The algorithms and methods used in CBPCS are introduced in detail. Moreover, how CBPCS can be applied to multiple service process models is also investigated. Finally, CBPCS is validated via comparison experiments, which shows the considerable improvements of CBPCS over other strategies.

ABSTRACT Designing and implementing an interoperable and flexible service process collaboration strategy is one of key issues for business to business integrations. To better support service process collaboration, an event view model is proposed, which is composed of a set of event types and their dependency relationships. It provides a general and flexible way to define a public view of a service process model and serves as the basis for defining service process collaboration protocols. In the paper, the basic concepts and a system framework for event‐based service process collaboration are first introduced. The definitions of event and the dependency relationships among event types are then presented. Especially, how to identify dependency relationships among composite event types is studied in detail. After discussing the definition of event view and its specifying approach, a procedure for transforming a BPEL process model into an event model and deriving dependencies among events is given. Finally, a case study is presented, and some implementation issues for defining and publishing an event view are discussed. Copyright © 2013 John Wiley & Sons, Ltd.

Agent technology has gained increasing popularity in service oriented architecture (SOA) because of its features of autonomy, initiative, interactivity, persistency and adaptability. There are already a plenty of implementations which integrate SOA with multi-agent systems (MAS). The ability of learning is a significant feature of MAS. This paper proposes a learning model of the service-oriented MAS for the service composition problem. It adopts the principle of reinforcement learning and is based on the Markov game and Q-learning. The reward of the learning procedure is determined by the QoS parameters such as responding time and cost. The mechanism of multi-agent leaning for service composition is introduced. The results of experiments and case study show that our multi-agent learning approach can reach convergence efficiently and it can also accelerate the service composition process based on the knowledge continuously learned from past composition experiences.

The capability of an intelligent services system is based on a set of services processes, which are often organized into and managed by a process library. In order to react to a outside requirement, the system takes one of the approaches of using a predefined process directly, composing existing process models or generating a new process model based on a search algorithm on demand. Therefore, to decide which process models should be stored and how to update process models within the library is very important to improve the efficiency and lower the space cost of the system. A suffix tree-based optimization approach is proposed, which is based on a tree-structured representation. The algorithm, its complexity analysis and experiments are presented.

With the popularity of Web services, a set of Web services with similar functions but different qualities can be found. Since services are often be composed through a service process model, the way to compose them affects the whole quality of the process model itself. In this paper, a quality optimization method for the service process model is proposed, which takes the structure of service process into account. Based on the proposed quality model, a genetic algorithm is proposed to optimize the service process model.