The end-to-end automated design of machine learning (ML) pipelines significantly reduces the workload for data scientists and democratizes ML for non-experts. Evolutionary algorithm (EA)-based automated ML (AutoML) systems, a prominent category of AutoML, often face inefficiencies due to the costly fitness evaluation of candidate ML pipelines. Although surrogate models have been employed to approximate the true performance of pipelines more quickly, a key challenge remains in effectively bridging the semantic gap between the heterogeneous features of datasets and pipelines. To address this issue, we propose ADELA, a novel accompanying surrogate-based optimization strategy that accelerates EA-based AutoML while retaining the performance of the resulting pipelines. ADELA operates in two phases: Offline, leveraging a high-quality curated pipeline corpus to meta-learn an accompanying surrogate model; and Online, selecting the accompanying pipeline and using the learned model to predict the performance of evaluation pipelines instead of executing them. The accompanying mechanism effectively mitigates the semantic gap between datasets and pipelines, enabling ADELA to reduce computation times by an average of 73.66% while retaining 98.78% of the final pipeline performance, as demonstrated in extensive experimental evaluations.

The pursuit of personalized outfit recommendation, tailored to individual user preferences, has emerged as a central focus in research. Despite the propositions of various outfit recommendation methods, the modeling of fashion styles within outfits, a pivotal criterion for user selection, has been largely overlooked. Fashion styles are intricately embedded in multi-modal information, such as images and text. Unfortunately, current outfit recommendation methods predominantly focus on a single modality or employ simplistic fusion techniques, thus failing to capture the intricate interplay between different fashion data modalities, resulting in an inability to model fashion styles effectively. In this work, we devise a novel Personalized Outfit Recommender with Style-Guided Multi-Modal Feature Fusion scheme, denoted as POSM, aiming to maximize the utilization of multi-modal features in outfit recommendation. In particular, this scheme consists of three key components: modality-aware outfit modeling, feature separation network, and style mapping component. Extensive experiments conducted on the benchmark datasets validate the superiority of POSM over state-of-the-art methods.

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.

It is time-consuming and knowledge-intensive for scientists to find practical workflows from the massive number of scientific workflow models. Currently, the retrieval approaches are mainly based on text matching between natural language queries and the descriptions of candidate workflows. Notably, the workflow structure also provides essential semantics, but the challenge lies in effectively matching these two pieces of heterogeneous information. To address this issue, we propose a Proxy-based Scientific workflow retrieval approach, ProSwats, which selects a workflow as the Proxy for each text query to bridge the gap between textual and structural semantics. ProSwats consists of two stages: workflow pre-selection based on text similarity and workflow ranking based on a matching degree prediction model. The textual and structural features are integrated by the proxy in this model, which is used to predict and rank the degree of semantic matching between the user query and candidate workflows. Crucially, ProSwats incorporates a confidence-aware learning mechanism to adapt to the varying reliability of proxies, enhancing generalizability. Extensive experimental results on two real-world datasets demonstrate that ProSwats outperforms state-of-the-art methods with statistical significance.

In business processes, anomalies are prevalent, arising from diverse factors, such as software malfunctions and operator errors. Detecting these anomalies is imperative, as it significantly influences not only the financial well-being of a business but also the dependability of event logs for subsequent analysis. However, existing deep business process anomaly detection approaches either lack effective temporal dependency modeling between events or encounter challenges associated with gradient vanishing. In this paper, we introduce COMB, which stands for an interConnected transfOrmers-based autoencoder for Multi-perspective Business process anomaly detection. Considering the interdependent nature of multi-perspectives, COMB leverages multiple parallel and interconnected transformers, facilitated by the inclusion of aggregation layers. These layers serve as integration points for information from various perspectives. Notably, considering how control flow influences other perspectives, COMB incorporates innovative mask adapters to enhance its detection performance. Furthermore, we propose a novel method for calculating anomaly scores, which effectively mitigates the influence of varying numbers of potential attribute values. Our extensive experimental evaluation encompasses both synthetic and real-life logs, and the results clearly demonstrate that COMB outperforms state-of-the-art methods in both trace-level and attribute-level anomaly detection.

Summary Finding similar scientific workflow modules that can substitute essential components of the privacy workflows from public repositories to create a personalized workflow is growing in popularity. This is a cost‐effective and error‐free strategy for the scientific community. Currently, module alignment approaches heavily depend on syntactic information such as modules' names and types. However, the contextual semantic information of modules, which encompasses inputs, outputs, and datalinks connecting modules, can also convey their functions. Unfortunately, this information is scarcely utilized during the module alignment process. In this work, we propose a module alignment method based on neighbor information for scientific workflow (MANSOR). Specifically, we present a rule‐based attribute similarity computation approach for calculating initial module‐pair similarity in two workflows. The relation similarity is then employed to iteratively fine‐tune the matching degree with uncertainty of module pairs by considering the contextual semantics until reaching a steady state. After pairwise comparison of workflows, the module alignment results are determined based on their module‐pair similarity. Experimental results on the human‐curated corpus of ratings indicate that MANSOR outperforms the existing state‐of‐the‐art approaches with statistical significance.

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

Finding scientific workflow models that can be reused or repurposed from public repositories is becoming popular in the scientific community. Currently, the retrieval approaches for workflow models are mainly based on text matching between queries and the descriptions of models. However, the structure information of these models, which includes inputs, outputs, data processing modules, and datalinks connecting modules, expresses their functions in a more detailed manner, yet this information is not used in the model retrieval process. Therefore, we propose a two-stage framework for Scientific WOrkflow Retrieval by learning Textual and Structural semantics (SWORTS). The framework comprises a workflow pre-selection step and a workflow ranking step. Specifically, we use text similarity approaches to quickly identify candidate workflow models in the first step. Then, a hierarchy-based matching degree prediction model, which takes both textual and structural features into account, is trained to predict and rank the degree of semantic matching between requirement specifications and candidate workflows. The experiment results on real-world datasets demonstrate that SWORTS achieves the best performance with relatively balanced effectiveness and efficiency among state-of-the-art methods.

In process-aware information systems (PAISs), anomalies are ubiquitous, having a number of different underlying causes, such as software malfunctions or operator errors. The presence of anomalies not only has an enormous impact on the economic well-being of the business, but also interferes with our ability to mine useful information from event logs. In this article, we propose GRASPED, aGRU-AE Network based multi-perSpective business ProcEss anomalyDetection model. GRASPED can detect anomalies not only from the control flow but also from the data perspective of a business process. GRASPED is based on an autoencoder (AE) with gated recurrent units (GRU) which is trained in an unsupervised fashion (i.e., does not require any labeling of the data). In addition, GRASPED introduces the teacher forcing method as well as the attention mechanism to improve its detection performance. GRASPED does not require training on a clean log (i.e., it can be trained and perform anomaly detection directly on logs containing anomalies). We conduct extensive experiments on synthetic logs as well as real-life logs. The experiment results show that GRASPED outperforms the state-of-the-art methods for both trace-level and attribute-level anomaly detection.