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The journal Information Systems publishes articles concerning the design and implementation of languages, data models, process models, algorithms, software and hardware for information systems. Subject areas include data management issues as presented in the principal international database conferences (e.g., ACM SIGMOD/PODS, VLDB, ICDE and ICDT/EDBT) as well as data-related issues from the fields of data mining/machine learning, information retrieval coordinated with structured data, internet and cloud data management, business process management, web semantics, visual and audio information systems, scientific computing, and data science. We welcome systems papers that focus on implementation considerations in massively parallel data management, fault tolerance, and special purpose hardware for data-intensive systems; theoretical papers that either break significant new ground or unify and extend existing algorithms for data-intensive applications; and manuscripts from application domains, such as urban informatics, social and natural science, and Internet of Things, which present innovative, high-performance, and scalable solutions to data management problems for those domains. All papers should motivate the problems they address with compelling examples from real or potential applications. Systems papers must be serious about experimentation either on real systems or simulations based on traces from real systems. Papers from industrial organizations are welcome. Theoretical papers should have a clear motivation from applications and clearly state which ideas have potentially wide applicability. Authors of selected articles that have been accepted for publication in Information Systems are invited by the EiCs to submit the experiment described in their papers for reproducibility validation. The resulting additional reproducibility paper is co-authored by the reproducibility reviewers and the authors of the original publication. As part of its commitment to reproducible science, Information Systems also welcomes experimental reproducible survey papers. Such submissions must: (i) apply a substantial portion of the different surveyed techniques to at least one existing benchmark and perhaps one or more new benchmarks, and (ii) be reproducible (the validation of reproducibility will result in a separate paper following the guidelines of our Reproducibility Editor). In addition to publishing submitted articles, the Editors-in-Chief will invite retrospective articles that describe significant projects by the principal architects of those projects. Authors of such articles should write in the first person, tracing the social as well as technical history of their projects, describing the evolution of ideas, mistakes made, and reality tests. We will make every effort to allow authors the right to republish papers appearing in Information Systems in their own books and monographs.</pre><div class="alert alert-warning"> Last updated by <a href="/researcher/1">Dou Sun</a> in 2024-07-14</div> </div> </div> <div class="portlet" id="yw2"> <div class="portlet-decoration"> <div class="portlet-title"><i class="icon icon-list-alt"></i> Special Issues</div> </div> <div class="portlet-content"> <a name="5429"></a><pre>Special Issue on AI-Enhanced Business Process Management</br>Submission Date: 2025-07-01</br></br>Artificial Intelligence (AI) is rapidly evolving, offering advanced techniques and applications across a wide range of domains. In recent years, there has been a significant increase in interest from both industry and academia in applying AI to Business Process Management (BPM), which combines insights from operations management, computer science, and data science. AI is set to revolutionize BPM by simplifying human interactions, enhancing task execution, and enabling the full automation of processes traditionally performed manually. The development of AI techniques is driving the emergence of AI-augmented BPM systems (ABPMS) that are autonomous, adaptive, intelligent, and self-optimizing. The impact of AI on BPM is multifaceted. On one hand, AI can dramatically simplify human interactions with business processes by providing intelligent recommendations, automating routine tasks, and facilitating decision-making through advanced analytics. On the other hand, AI can support task execution by augmenting human capabilities, offering insights from vast amounts of data, and learning from historical performance to optimize future operations. Furthermore, AI enables the full automation of processes that have traditionally required manual intervention, thereby increasing efficiency, reducing errors, and lowering operational costs. ABPMS represent a new generation of information systems designed to be more autonomous, adaptive, and intelligent. These systems continuously monitor and analyze business processes, adapting in real-time to changing conditions and optimizing performance based on predefined goals and experiential learning. The integration of AI into BPM allows for the creation of systems that are not only self-optimizing but also capable of evolving over time, making them more resilient and effective in achieving business objectives. Guest editors: Assoc. Prof. Valeria Fionda (Executive Guest Editor) University of Calabria, Arcavacata di Rende, Italy Email: valeria.fionda@unical.it Dr. Antonio Ielo University of Calabria, Arcavacata di Rende, Italy Email: antonio.ielo@unical.it Assist. Prof. Arik Senderovich York University, Toronto, Ontario, Canada Email: sariks@yorku.ca Assist. Prof. Emilio Sulis University of Turin, Turin, Italy Email: emilio.sulis@unito.it Special issue information: This special issue aims to explore the foundational, conceptual, and technical challenges of integrating AI with BPM. We invite contributions from researchers, practitioners, and students that advance the synergy between AI and BPM. Topics of interest include, but arenot limited to: Machine Learning and Deep Learning to support workflow management and process automation Neuro-symbolic Approaches, integrating symbolic reasoning with neural networks for BPM Business Process Monitoring, predictions and recommendations Natural language processing and process modeling AI-based techniques for new business models Personalized recommendations to improve business processes AI-based techniques for process mining AI-assisted process design AI-based techniques to manage process exceptions Automated-planning for business processes Business Process rule mining Knowledge representation, management and reasoning on process specifications Decision support systems for business processes Robotic Process Automation (RPA) Trustworthy AI, explainability, transparency in the field of BPM New AI-enhanced BPM models Social, Economic, and Business impacts of AI-enhanced BPM Generative AI for BPM Value alignment in AI-driven process management Manuscript submission information: Tentative Schedule: Submission Open Date: November 1, 2024 Submission Deadline: July 1, 2025 Editorial Acceptance Deadline: December 31, 2025 Submission Guidelines: All manuscripts should be submitted electronically through Editorial Manager ® at https://www.editorialmanager.com/infosys/default.aspx. When submitting papers, please select the Article Type as "VSI: AI-Enhanced BPM". Authors should prepare their manuscripts according to the "Guide for Authors" of the Information Systems outlined at the journal website: https://www.sciencedirect.com/journal/information-systems/publish/guide-for-authors. All papers will be peer-reviewed following a regular reviewing procedure. For any further information, the authors may contact the Guest Editors. Keywords: Business Process Management; Process Mining; Artificial Intelligence</pre><div class="alert alert-warning">Last updated by <a href="/researcher/1">Dou Sun</a> in 2024-12-27</div><a name="5428"></a><pre>Special Issue on Autonomous Process Execution Systems</br>Submission Date: 2025-09-01</br></br>This special issue focuses on advancements in autonomous process execution and adaptation, showcasing that AI-based approaches in process management can be applied beyond process analysis, yielding direct changes to how organizations run. The special issue follows a Dagstuhl Seminar on the same topic that aims to advance the realization of Autonomous Business Process systems (ABPs), and aims to combine academic and industry perspectives, highlighting aspects such as application potential, as well as evaluations and critical discussions of real-world limitations. This special issue aims to solicit papers relevant for (and potentially produced by) both academic and industrial researchers from the AI and BPM communities to foster joint efforts and collaboration to advance the vision of ABPs and address the challenges outlined in the manifesto. The special issue covers topics at the intersection of AI and business process execution and adaption, potentially contributing to the advancement of ABPMSs, including declarative process specification and reasoning with a focus on framed autonomy; planning and program synthesis; explainable and trustworthy AI; conversational systems and natural language processing; causal discovery and neuro-symbolic reasoning; self-healing and auto- corrective techniques; large foundation models; and legal, safety, and ethical aspects of autonomous enterprises. Topics of interest include, but are not limited to: ● Declarative process specification and reasoning with particular focus on framed autonomy ● Process analysis for the (run-time) adaptation and optimization of autonomous business processes ● Planning, program synthesis, and reasoning for autonomous process execution ● Explainable and trustworthy AI for autonomous process execution ● Conversational systems and natural language processing for autonomous process adaptation and optimization ● Formal methods for automating tasks and processes ● AI for process autonomy over uncertain data and models ● Causal discovery and reasoning for autonomous process executions ● Self-healing and auto-corrective techniques for business process executions ● Neuro-symbolic reasoning for adaptive business process execution ● Large foundation models for autonomous process improvement and monitoring ● Legal, social, safety, and ethical aspects of autonomous enterprises ● Feasibility and viability challenges of ABPMSs While contributions from seminar participants are strongly encouraged, the special issue explicitly invites submissions from the broader AI and information systems communities as well. Guest editors: Dr. Timotheus Kampik Umeå University, Umeå, Sweden Email: tkampik@cs.umu.se Dr. Fabiana Fournier IBM Haifa Research Labs, Haifa, Israel Email: fabiana@il.ibm.com Dr. Lior Limonad IBM Haifa Research Labs, Haifa, Israel Email: liorli@il.ibm.com Prof. Marlon Dumas University of Tartu, Tartu, Estonia Email: marlon.dumas@ut.ee Prof. Giuseppe De Giacomo University of Oxford, Oxford, UK Email: giuseppe.degiacomo@cs.ox.ac.uk Manuscript submission information: Tentative Schedule: Submission Open Date: November 1, 2024 Submission Deadline: September 1, 2025 Editorial Acceptance Deadline: March 1, 2026 Submission Guidelines: All manuscripts should be submitted electronically through Editorial Manager® at https://www.editorialmanager.com/infosys/default.aspx. When submitting papers, please select the Article Type as "VSI: Autonomous Process Execution Systems". Authors should prepare their manuscripts according to the "Guide for Authors" of the Information Systems outlined at the journal website: https://www.sciencedirect.com/journal/information-systems/publish/guide-for-authors. All papers will be peer-reviewed following a regular reviewing procedure. For any further information, the authors may contact the Guest Editors. Keywords: (Business Process Management) OR (Business Process Execution) AND (Artificial Intelligence) OR (Autonomous Agents)</pre><div class="alert alert-warning">Last updated by <a href="/researcher/1">Dou Sun</a> in 2024-12-27</div></div> </div>  <div class="portlet" id="yw3"> <div class="portlet-decoration"> <div class="portlet-title"><i class="icon icon-file"></i> Related Journals</div> </div> <div class="portlet-content"> <div class="hidden-phone"> <div id="yw4" class="grid-view"> <table class="table table-hover table-striped table-bordered table-condensed"> <thead> <tr> <th id="yw4_c0">CCF</th><th id="yw4_c1">Full Name</th><th id="yw4_c2">Impact Factor</th><th id="yw4_c3">Publisher</th><th id="yw4_c4">ISSN</th></tr> </thead> <tbody> <tr class="odd"> <td></td><td><a href="/journal/558">International Journal of Advanced Research in Electrical and Electronics Engineering</a></td><td> </td><td>AR Publication</td><td>0000-0000</td></tr> <tr class="even"> <td></td><td><a 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