Article
Computer Science, Hardware & Architecture
Xiaoxiao Sun, Siqing Yang, Chenying Zhao, Dongjin Yu
Summary: This paper proposes an automatic and interpretable compliance checking approach for design-time business processes. The approach combines deep learning and natural language processing to extract semantic information from regulatory documents for compliance checking. The effectiveness of this approach is validated on two real-world datasets.
JOURNAL OF SUPERCOMPUTING
(2023)
Article
Computer Science, Information Systems
Cristina Cabanillas, Manuel Resinas, Antonio Ruiz-Cortes
Summary: Business process compliance is essential for ensuring that an organization's processes are designed and executed according to rules. Existing approaches for compliance checking are often limited to specific types of rules, phases of the BPM lifecycle, or information systems. This research introduces a conceptual framework that uses mashups for rule specification and checking, offering advantages like open-ended rule types design and integration with organizational information systems.
IEEE TRANSACTIONS ON SERVICES COMPUTING
(2022)
Article
Computer Science, Information Systems
Dongming Guo, Erling Onstein, Angela Daniela la Rosa
Summary: Automated Compliance Checking (ACC) is crucial in the Architecture, Engineering and Construction (AEC) industry, however, it still involves manual operations and high costs. A semantic approach is proposed to automate the whole ACC process, extracting rule terms and logic relationships from text regulatory documents using NLP, mapping them to BIM data keywords for generating SPARQL queries, providing flexible and effective rule checking for BIM data.
Article
Computer Science, Hardware & Architecture
Leonardo Silva Rosa, Thanner Soares Silva, Marcelo Fantinato, Lucineia Heloisa Thom
Summary: This paper proposes a user-interactive visual approach to support process comprehension by identifying and annotating core BPMN 2.0 elements in process descriptions. The approach showed promising results in a survey experiment, with 88% of users indicating positive results regarding its usefulness in assisting the process modeling phase. Additionally, a process modeling case study demonstrated a 77% precision in comparison to the original process model.
COMPUTER STANDARDS & INTERFACES
(2022)
Review
Computer Science, Information Systems
Mohammad Imran, Suraya Hamid, Maizatul Akmar Ismail
Summary: This systematic literature review focuses on the potential of process mining techniques for enhancing process audits. By analyzing research articles from reputable scholarly literature indexing databases, the review reveals that integrating process mining into the auditing landscape introduces automation, transparency, and efficiency, overcoming the limitations of traditional process audits. The findings provide valuable insights into the benefits and challenges of process mining-based audits.
Article
Computer Science, Artificial Intelligence
Sergej Levich, Bernhard Lutz, Dirk Neumann
Summary: Predictive process monitoring (PPM) improves business process efficiency by predicting various aspects such as process outcome and next event. This study aims to enhance PPM research by incorporating external context information, specifically digital documents. The proposed approach processes digital documents using automated visual and textual feature extraction, and the evaluation shows significant performance improvements in predicting damage type, next event, and time until the next event.
DECISION SUPPORT SYSTEMS
(2023)
Article
Construction & Building Technology
Yilun Zhou, Jianjun She, Yixuan Huang, Lingzhi Li, Lei Zhang, Jiashu Zhang
Summary: In this paper, a natural language processing-based semantic framework is proposed for automated compliance checking in building information modeling. Semantic-rich information is extracted from safety regulations and analyzed to generate ontology and rule classification. The framework's practicability and feasibility are verified through a case study in China, achieving high recall and precision rates.
Article
Psychology, Multidisciplinary
Maria Balaet, Danielle L. L. Kurtin, Dragos C. C. Gruia, Annalaura Lerede, Darije Custovic, William Trender, Amy E. E. Jolly, Peter J. J. Hellyer, Adam Hampshire
Summary: By analyzing survey responses from tens of thousands of members of the UK public at three time points, we find that scepticism towards the government and mainstream media's narrative, particularly regarding the justification for safety guidelines, significantly predicts non-compliance. However, free text topic modelling reveals a diverse range of opinions, including skepticism about government competence and self-interest, as well as full-blown conspiracy theories, which vary in prevalence according to sociodemographic variables.
FRONTIERS IN PSYCHOLOGY
(2023)
Article
Computer Science, Information Systems
Christoph Kecht, Andreas Egger, Wolfgang Kratsch, Maximilian Roeglinger
Summary: Chatbots help businesses respond efficiently to repetitive requests. The ability of chatbots to learn and follow business processes is essential for their organizational adoption. This study presents an approach that quantifies chatbots' ability to learn business processes, using standardized process mining metrics. By training chatbots on a dataset of customer service conversations and comparing their executed steps against a normative process model, our approach supports the evaluation and adoption of chatbots in practice.
INFORMATION SYSTEMS
(2023)
Article
Construction & Building Technology
Zhe Zheng, Yu-Cheng Zhou, Xin-Zheng Lu, Jia-Rui Lin
Summary: This study proposes a knowledge-informed framework based on natural language processing to enhance automated rule checking. By establishing an ontology to represent domain knowledge and introducing semantic alignment and conflict resolution, the semantic gaps between design models and regulatory texts are filled. Experimental results show that the framework and methods achieve an accuracy of 90.1% and are 5 times faster than manual interpretation by domain experts.
AUTOMATION IN CONSTRUCTION
(2022)
Article
Biology
Jiajie Tan, Jinlong Hu, Shoubin Dong
Summary: This paper proposes a method to incorporate external knowledge into a dense retrieval model to improve the effectiveness of biomedical retrieval based on pre-trained language models. Experimental results demonstrate that the proposed method outperforms existing methods and has relatively low query latency.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Computer Science, Artificial Intelligence
Ruichuan Zhang, Nora El-Gohary
Summary: This paper proposes the concept of intelligent building code to address the challenges in the information extraction and transformation processes of automated compliance checking systems. By connecting natural-language requirements with computer-understandable semantic information, intelligent code is generated to improve the accuracy and comprehensibility of compliance checking.
ADVANCED ENGINEERING INFORMATICS
(2022)
Article
Automation & Control Systems
Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, Parker Schuh, Kensen Shi, Sasha Tsvyashchenko, Joshua Maynez, Abhishek Rao, Parker Barnes, Yi Tay, Noam Shazeer, Vinodkumar Prabhakaran, Emily Reif, Nan Du, Ben Hutchinson, Reiner Pope, James Bradbury, Jacob Austin, Michael Isard, Guy Gur-Ari, Pengcheng Yin, Toju Duke, Anselm Levskaya, Sanjay Ghemawat, Sunipa Dev, Henryk Michalewski, Xavier Garcia, Vedant Misra, Kevin Robinson, Liam Fedus, Denny Zhou, Daphne Ippolito, David Luan, Hyeontaek Lim, Barret Zoph, Alexander Spiridonov, Ryan Sepassi, David Dohan, Shivani Agrawal, Mark Omernick, Andrew M. Dai, Thanumalayan Sankaranarayana Pillai, Marie Pellat, Aitor Lewkowycz, Erica Moreira, Rewon Child, Oleksandr Polozov, Katherine Lee, Zongwei Zhou, Xuezhi Wang, Brennan Saeta, Mark Diaz, Orhan Firat, Michele Catasta, Jason Wei, Kathy Meier-Hellstern, Douglas Eck, Jeff Dean, Slav Petrov, Noah Fiedel
Summary: Large language models have shown remarkable performance in various natural language tasks through few-shot learning. This article introduces a 540 billion parameter Transformer language model called PaLM, trained using Pathways, a new ML system. The study demonstrates the benefits of scaling and shows state-of-the-art few-shot learning results on language understanding and generation benchmarks. The PaLM model achieves breakthrough performance on multi-step reasoning tasks and even outperforms human performance on the BIG-bench benchmark. The article also discusses the model's capabilities in multilingual tasks and source code generation, as well as addresses ethical considerations and potential mitigation strategies.
JOURNAL OF MACHINE LEARNING RESEARCH
(2023)
Article
Computer Science, Artificial Intelligence
Peng Yan, Linjing Li, Daniel Zeng
Summary: Inspired by quantum phenomena in human language understanding, a Quantum Probability-inspired Graph Attention Network (QPGAT) is proposed to model complex and graphical text interaction, showing competitive performance in emotion-cause pair extraction and joint dialog act recognition tasks.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
S. Anbukkarasi, S. Varadhaganapathy
Summary: Grammar checking is an important application of Natural Language Processing, but there is a lack of grammar checkers for Tamil language. This paper proposes a hybrid approach that combines neural network and rule-based methods to develop a Tamil grammar checker, addressing issues such as spell checking, consonant errors, long component letter errors, and subject-verb agreement errors.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Computer Science, Interdisciplinary Applications
Jorge Munoz-Gama, Niels Martin, Carlos Fernandez-Llatas, Owen A. Johnson, Marcos Sepulveda, Emmanuel Helm, Victor Galvez-Yanjari, Eric Rojas, Antonio Martinez-Millana, Davide Aloini, Ilaria Angela Amantea, Robert Andrews, Michael Arias, Iris Beerepoot, Elisabetta Benevento, Andrea Burattin, Daniel Capurro, Josep Carmona, Marco Comuzzi, Benjamin Dalmas, Rene de la Fuente, Chiara Di Francescomarino, Claudio Di Ciccio, Roberto Gatta, Chiara Ghidini, Fernanda Gonzalez-Lopez, Gema Ibanez-Sanchez, Hilda B. Klasky, Angelina Prima Kurniati, Xixi Lu, Felix Mannhardt, Ronny Mans, Mar Marcos, Renata Medeiros de Carvalho, Marco Pegoraro, Simon K. Poon, Luise Pufahl, Hajo A. Reijers, Simon Remy, Stefanie Rinderle-Ma, Lucia Sacchi, Fernando Seoane, Minseok Song, Alessandro Stefanini, Emilio Sulis, Arthur H. M. ter Hofstede, Pieter J. Toussaint, Vicente Traver, Zoe Valero-Ramon, Inge van de Weerd, Wil M. P. van der Aalst, Rob Vanwersch, Mathias Weske, Moe Thandar Wynn, Francesca Zerbato
Summary: Process mining techniques are not widely used in healthcare beyond targeted case studies, and there is a need for further research and improvement to consider the characteristics of healthcare processes.
JOURNAL OF BIOMEDICAL INFORMATICS
(2022)
Article
Computer Science, Information Systems
Diana Sola, Han van der Aa, Christian Meilicke, Heiner Stuckenschmidt
Summary: Business process modeling is crucial in organizations, but creating consistent and complete process models is challenging. This paper proposes a rule-based and semantic-aware recommendation approach to improve the quality of activity label recommendations.
INFORMATION SYSTEMS
(2022)
Article
Computer Science, Interdisciplinary Applications
Iris Beerepoot, Claudio Di Ciccio, Hajo A. Reijers, Stefanie Rinderle-Ma, Wasana Bandara, Andrea Burattin, Diego Calvanese, Tianwa Chen, Izack Cohen, Benoit Depaire, Gemma Di Federico, Marlon Dumas, Christopher van Dun, Tobias Fehrer, Dominik A. Fischer, Avigdor Gal, Marta Indulska, Vatche Isahagian, Christopher Klinkmueller, Wolfgang Kratsch, Henrik Leopold, Amy Van Looy, Hugo Lopez, Sanja Lukumbuzya, Jan Mendling, Lara Meyers, Linda Moder, Marco Montali, Vinod Muthusamy, Manfred Reichert, Yara Rizk, Michael Rosemann, Maximilian Roeglinger, Shazia Sadiq, Ronny Seiger, Tijs Slaats, Mantas Simkus, Ida Asadi Someh, Barbara Weber, Ingo Weber, Mathias Weske, Francesca Zerbato
Summary: This paper provides an overview of the major research problems in the field of Business Process Management. These challenges have been identified through an open call to the community, discussed and refined in a workshop, and described in detail in this paper with motivations for further investigation. This overview aims to inspire both novice and advanced scholars interested in innovative ideas for analyzing, designing, and managing work processes using information technology.
COMPUTERS IN INDUSTRY
(2023)
Article
Computer Science, Software Engineering
Bernhard Schaefer, Han van der Aa, Henrik Leopold, Heiner Stuckenschmidt
Summary: Process models are crucial for capturing business requirements and facilitating the development of process-oriented applications. While initially sketched on a whiteboard or paper, transforming these sketches into digital counterparts is essential for further processing using modeling and analysis tools. Existing sketch recognition approaches are limited, thus this paper introduces Sketch2Process, an accurate and advanced approach for recognizing process models captured using BPMN.
IEEE TRANSACTIONS ON SOFTWARE ENGINEERING
(2023)
Article
Computer Science, Information Systems
Jan Martijn E. M. van der Werf, Artem Polyvyanyy, Bart R. van Wensveen, Matthieu Brinkhuis, Hajo A. Reijers
Summary: A process discovery algorithm aims to construct a precise, general, and simple process model that accurately represents the real-world process stored in event data. However, existing algorithms often neglect the relationship between input and output quality, leading to a lack of guarantee for better quality models with better quality input data. This paper calls for a more rigorous design of process discovery algorithms that include properties connecting input and output qualities. Four incremental maturity stages for process discovery algorithms and concrete guidelines for formulating relevant properties and experimental validation are presented.
INFORMATION SYSTEMS
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Jelmer J. Koorn, Xixi Lu, Henrik Leopold, Niels Martin, Sam Verboven, Hajo A. Reijers
Summary: This paper proposes a novel relation mining approach for healthcare processes that explicitly considers confounding variables and transparently communicates their effects to the user. Through evaluation experiments, the applicability and importance of this approach are demonstrated in healthcare decision making and causal model estimation.
2022 4TH INTERNATIONAL CONFERENCE ON PROCESS MINING (ICPM 2022)
(2022)
Proceedings Paper
Computer Science, Information Systems
Francesca Zerbato, Jelmer J. Koorn, Iris Beerepoot, Barbara Weber, Hajo A. Reijers
Summary: This paper presents the results of an interview study on question development in process mining, providing insights from expert interviewees and six recommendations to enhance existing methodologies. Concrete examples of how process mining analyses can support question formulation and refinement are also presented.
ENTERPRISE DESIGN, OPERATIONS, AND COMPUTING, EDOC 2022
(2022)
Proceedings Paper
Business
Adrian Rebmann, Jana-Rebecca Rehse, Han van der Aa
Summary: This paper introduces a method for transforming classical event logs into object-centric event logs. The transformation can solve the issue of hidden relationships between objects in classical event logs. By combining semantic analysis, data profiling, and control-flow-based relation extraction techniques, object-related information in flat event data can be automatically uncovered and transformed into object-centric event logs.
BUSINESS PROCESS MANAGEMENT (BPM 2022)
(2022)
Proceedings Paper
Computer Science, Information Systems
Suhwan Lee, Xixi Lu, Hajo A. Reijer
Summary: This paper investigates online event anomaly detection using next-activity prediction methods. The probabilities of next-activities are predicted using ML and deep models, and events predicted unlikely are considered as anomalies. The evaluation shows that the proposed ML model-based method tends to outperform the deep model-based method and classical unsupervised approaches in detecting anomalous events.
RESEARCH CHALLENGES IN INFORMATION SCIENCE
(2022)
Proceedings Paper
Business
A. Martinez-Rojas, A. Jimenez-Ramirez, J. G. Enriquez, H. A. Reijers
Summary: Robotic Process Automation (RPA) is a way to automate repetitive tasks. Task Mining approaches can be used to discover human actions in carrying out tasks. However, existing approaches have difficulty dealing with humans who follow hidden rules to perform tasks differently. This paper proposes a new Task Mining framework that extracts features from UI logs and screen captures, and uses supervised machine learning algorithms to generate decision models for variable human actions.
BUSINESS PROCESS MANAGEMENT (BPM 2022)
(2022)
Proceedings Paper
Computer Science, Information Systems
Banu Aysolmaz, Farida Nur Cayhani, Hajo A. Reijers
Summary: Conceptual models are crucial in information systems engineering, but understanding process models can be challenging. This study used dual coding theory and cognitive theory of multimedia learning to conduct an experiment and found that providing additional information through auditory channel may positively impact process model comprehension, depending on the type of model elements explained.
ADVANCED INFORMATION SYSTEMS ENGINEERING (CAISE 2022)
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Ruben Post, Iris Beerepoot, Xixi Lu, Stijn Kas, Sebastiaan Wiewel, Angelique Koopman, Hajo Reijers
Summary: Process mining can help auditors retrieve crucial information about transactions. We propose an approach that identifies unusual transactions and updates anomaly scores based on auditors' domain knowledge. Evaluation results indicate that the approach has the potential to support auditors' decision-making process.
PROCESS MINING WORKSHOPS, ICPM 2021
(2022)
Proceedings Paper
Business
Vinicius Stein Dani, Henrik Leopold, Jan Martijn E. M. van der Werf, Xixi Lu, Iris Beerepoot, Jelmer J. Koorn, Hajo A. Reijers
Summary: This paper establishes a taxonomy of human tasks in event log extraction through structured literature review and qualitative data coding, which can facilitate future automation efforts and the development of process mining methodologies.
BUSINESS PROCESS MANAGEMENT WORKSHOPS, BPM 2021
(2022)
Proceedings Paper
Business
Vinicius Stein Dani, Mahendrawathi Er, Jelmer J. Koorn, Jan Martijn E. M. van der Werf, Henrik Leopold, Hajo A. Reijers
Summary: Pair programming is a technique where two programmers work together, offering benefits such as improved code quality, faster task completion, and increased participant satisfaction. However, contrary to pair programming, pair modeling does not statistically improve the quality of process models, but participants are highly satisfied with pair modeling tasks. Therefore, instructors or managers may consider using pair setup for training purposes.
BUSINESS PROCESS MANAGEMENT WORKSHOPS, BPM 2021
(2022)
Proceedings Paper
Business
Diana Sola, Christian Meilicke, Han van der Aa, Heiner Stuckenschmidt
Summary: This paper investigates different approaches to apply embedding- and rule-based knowledge graph completion methods and compares them to methods specifically designed for activity recommendation.
BUSINESS PROCESS MANAGEMENT WORKSHOPS, BPM 2021
(2022)
Article
Computer Science, Information Systems
Alessio Cecconi, Luca Barbaro, Claudio Di Ciccio, Arik Senderovich
Summary: This paper introduces a framework for designing probabilistic measures for declarative process specifications, which can assess the degree of compliance between process data and specifications. Through experiments, the applicability of the approach for various process mining tasks is demonstrated.
INFORMATION SYSTEMS
(2024)
Article
Computer Science, Information Systems
Mahei Manhai Li, Philipp Reinhard, Christoph Peters, Sarah Oeste-Reiss, Jan Marco Leimeister
Summary: This article introduces a novel human-in-the-loop (HIL) design for ITSM support ticket recommendations by incorporating a value co-creation perspective. The design incentivizes ITSM agents to provide labels during their everyday ticket-handling procedures, and the evaluation shows that recommendations after label improvement have increased user ratings.
INFORMATION SYSTEMS
(2024)
Article
Computer Science, Information Systems
Anton Yeshchenko, Jan Mendling
Summary: This paper presents the development of event sequence data analysis techniques in different fields and proposes an integrated framework to facilitate collaboration and research synergy across various domains.
INFORMATION SYSTEMS
(2024)
Article
Computer Science, Information Systems
Iris Reinhartz-Berger, Alan Hartman, Doron Kliger
Summary: Many IT departments provide solutions that partially meet the needs of business units. This research aims to develop a data-driven analysis method to support the selection of solutions with higher prospects of adoption and identify design gaps and barriers.
INFORMATION SYSTEMS
(2024)
Article
Computer Science, Information Systems
Orlenys Lopez-Pintado, Marlon Dumas, Jonas Berx
Summary: Business process simulation is a versatile technique that predicts the impact of changes on process performance. However, previous approaches have limitations due to their treatment of resources as undifferentiated entities. This article addresses this issue by proposing a new simulation approach that treats each resource as an individual entity with its own performance and availability. The article also presents methods for discovering simulation models with differentiated resources and optimizing resource availability calendars. Empirical evaluation demonstrates that differentiated resource models better replicate cycle time distributions and work rhythm, and iterative optimization of resource allocations and calendars leads to improved cost-time tradeoffs.
INFORMATION SYSTEMS
(2024)
