Object-centric process mining operates on event logs where each event references multiple objects of different types. A fundamental challenge is defining a case notion - the grouping of events into coherent process execution instances -without which process discovery and conformance checking cannot proceed. Existing approaches either flatten the log to a single object type (losing inter-object coordination) or use the connected component of the object graph (creating overly complex cases due to resource-like objects). We propose an Entity-Relationship-schema-guided framework that identifies the primary entity (PE) type anchoring each process execution, classifies other entity types as secondary coordination entities or resources, and defines cases as connected components of the primary and secondary entity object relationship graph. The resulting case notion is shown to produce strict partitions with automatic transitive closure. The framework is illustrated with an order management process and validated on a 1,000-event OCEL log producing 40 structurally coherent cases.

This research explores opportunities for generative artificial intelligence (GenAI) in higher education constituent (customer) relationship management (CRM) to address the industry’s need for digital transformation driven by demographic shifts, economic challenges, and technological advancements. Using a qualitative research approach grounded in the principles of grounded theory, we conducted semi-structured interviews and an open-ended qualitative data collection instrument with technology vendors, implementation consultants, and HEI professionals that are actively exploring GenAI applications. Our findings highlight six primary types of GenAI—textual analysis and synthesis, data summarization, next-best action recommendations, speech synthesis and translation, code development, and image and video creation—each with applications across student recruitment, advising, alumni engagement, and administrative processes. We propose an evaluative framework with eight readiness criteria to assess institutional preparedness for GenAI adoption. While GenAI offers potential benefits, such as increased efficiency, reduced costs, and improved student engagement, its success depends on data readiness, ethical safeguards, and institutional leadership. By integrating GenAI as a co-intelligence alongside human expertise, HEIs can enhance CRM ecosystems and better support their constituents.

In this paper, we report our experience with several LLMs for their ability to understand a process model in an interactive, conversational style, find syntactical and logical errors in it, and reason with it in depth through a natural language (NL) interface. Our findings show that a vanilla, untrained LLM like ChatGPT (model o3) in a zero-shot setting is effective in understanding BPMN process models from images and answering queries about them intelligently at syntactic, logic, and semantic levels of depth. Further, different LLMs vary in performance in terms of their accuracy and effectiveness. Nevertheless, our empirical analysis shows that LLMs can play a valuable role as assistants for business process designers and users. We also study the LLM's "thought process" and ability to perform deeper reasoning in the context of process analysis and optimization. We find that the LLMs seem to exhibit anthropomorphic properties.

Online retail platforms are increasingly challenged by the proliferation of low-quality products, which may damage their reputation and sales. To address this problem, we propose a system architecture to proactively identify products that are likely to go "out of favor." Our approach uses historical data to extract useful information from customer ratings and textual reviews. Available data are fed into a state-of-the-art deep learning sequence model to forecast future ratings. We then analyze rating trends, extracting hyperparameters that a binary classifier uses to label products as "out-of-favor" or not. We tested this system on an Amazon dataset comprising nearly 800,000 observations across 2826 electronics products. Our results show that the Long Short-Term Memory (LSTM) model excels in forecasting future product ratings compared to other benchmarks. Ablation analysis shows sentiment-related features significantly improve rating forecasts by up to 40%, with review topics adding 10% and other review characteristics, 4%. Counterintuitively, topic extraction from reviews does not provide substantial benefits, despite the heavy computational resources it requires. Finally, the two-stage classification process, which leverages time-series data and rating trends, offers a more stable and robust performance than conventional single-stage methods. We provide considerations for system architecture development through robustness checks ensuring its resilience to stressors. Our experiments indicate that rating trends can change in subtle ways over time, leading a promising "star" product to turn into a liability ("dog"). E-commerce platforms can use the proposed system architecture proactively to identify and remove potentially dubious products instead of waiting to take reactive action.

Since the Economist magazine heralded blockchain as "the trust machine" in 2015, the blockchain paradigm has experienced crests and falls, including a recent phase of disillusionment due to its failure to meet the high expectations, e.g., to revolutionize record keeping, data management, and workflow, envisioned during its early history. However, despite the waning interest in this technology in some quarters, its deployment has become ever more essential in areas such as decentralized finance (DeFi), Non-fungible Tokens (NFTs), and other application domains beyond cryptocurrencies. In particular, recent advancements in Artificial Intelligence (AI) surrounding Large Language Models (LLM) offer new opportunities for blockchain adoption where trust and reliability become critical. As the blockchain technology transitions from a stage of disillusionment to one of enlightenment, anticipation is building for its mainstream adoption, with focused endeavors towards removing adoption barriers across diverse business contexts, exemplified by studies included in this special issue on Blockchain Technology and Applications. In this paper, we first survey the current state of the blockchain technology and then highlight its potential for enhancing trust and accountability in emerging phenomena such as AI generated content (AIGC). We conclude by introducing the papers included in the special issue.

Construction waste has always been the primary source of solid waste. In the United States, more than 600 million tons of construction and demolition wastes (CD&W) are generated annually, and plenty of construction waste, including concrete, bricks, and wood, will be landfilled directly. In recent years, people gradually realized the importance of reducing construction waste and developed a variety of methods to address the problems. However, there are still millions of tons of recyclable and reusable materials thrown away during the building demolition process because there is a lack of systematic understanding of options with respect to material types, conditions, and the complex value chains of a circular materials system, which leads both the demand and supply sides to underestimate building materials' salvaged value. The paper sets out to address the problem by proposing a general framework that focuses on constructing a knowledge graph to evaluate building materials' potential salvage methods based on material type, usage duration, weather conditions, and local policy. Once the fundamental framework is established, the objective is to integrate the knowledge graph with BIM in order to automatically determine the condition of materials for salvage. This includes quantifying reusable and recyclable building materials and deducing their appropriate salvage methods.

Much work has been done in process mining in the last two decades, where the focus of most efforts has been on unearthing the process models from log traces where each trace could be related to a unique case identifier that pertains to a single instance, such as an online customer order, a production order, a patient visit, etc. The case identifiers in these cases are customer order number, production order number, patient id, respectively, and there is a one-to-one relationship between the case identifier and the log data. On the other hand, in so-called object-centric (OC) logs, multiple objects are associated in one log record giving rise to many-to-many relationships among these objects and leading to ambiguities and redundancies in the log data. Hence, these logs become very difficult to analyze in their raw form as single linear files and it is important to convert them into database models. In this paper, we show how OC logs can be structured into a STAR and a fully normalized database schemas. The two schemas are compared and the benefits of our approach for log processing and ensuring log integrity are discussed.

The transition of a business to a circular business model (CBM) calls for significant and ongoing shifts in different business management models and strategies. However, there is a lack of research focused on the technological, financial, societal, and institutional influences on the CBM transition in small and/or medium-sized enterprises (SMEs). To address this gap, our study develops a theoretical framework for the transition towards CBM. We conducted a systematic literature review with the objective of determining the relationships among technological, financial, societal, and institutional influences for CBMs. Following this, we then established a conceptual framework that comprises these four key influences for a transition plan in the context of an innovative business model with a focus on the value proposition, value creation, and value delivery. An illustrative case example of the manufacturing industry for the transition plan to CBM was presented as well. The proposed framework is designed to lead the shift towards circular economy-oriented business models that aim to promote sustainability in business. In addition, we uncovered several potential avenues for further investigation. We expect the framework towards both contribute to the expansion of the existing body of research in the field and provide business practitioners with guidelines on the CBMs' transition for SMEs.

Many activities in business process management, such as process retrieval, process mining, and process integration, need to determine the similarity between business processes. Along with many other relational behavior semantics, Transition Adjacency Relation (abbr. TAR) has been proposed as a kind of behavioral gene of process models and a useful perspective for process similarity measurement. In this article we explain why it is still relevant and necessary to improve TAR or pTAR (i.e., projected TAR) computation efficiency and put forward a novel approach for TAR computation based on Petri net unfolding. This approach not only improves the efficiency of TAR computation, but also enables the long-expected combined usage of TAR and Behavior Profiles (abbr. BP) in process model similarity estimation.

In the past seventy years since the sale of the first mainframe computer by IBM in 1953, the world has become an information society that relies on networked computing systems in all aspects of work and life. Along with the evolution of computing, Information Systems has become an indispensable discipline in business and management that serves the needs of other managerial disciplines and performs its own business functions. Further information technology (IT) has become a sine qua non for the survival of a business and a source of much competitive advantage. Without advanced IT systems, an organization cannot survive for long today. In this paper, we recount and evaluate the evolution of Information Systems research since its infancy in the 1980s by empirically analyzing the research that has been published in leading academic journals. We found that research in Information Systems has evolved through several stages. During this evolution the basic disciplines from which researchers have drawn have also shifted considerably. Over this period the underlying technology support for the research has also developed enormously from simple data processing to systems development, client-server networks, electronic commerce, several generations of the Web (1.0, 2.0 and 3.0) and blockchain. Each new technology generation has spawned its own unique set of research issues and in turn influenced the research agenda of the scholars in the IS field in terms of research topics, methods, and theories. History offers a mirror to the past and a lesson for the present, and thus we believe that the results of this study should provide instructional value for new researchers such as doctoral students in Information Systems and other related disciplines.

Business process redesign has garnered much interest because such processes must be continuously improved to realize greater efficiency in the midst of evolving changes in work practices, environment, and technology. This helps a business to stay competitive. A process model may be redesigned by a rearrangement of various activities as per best practices to satisfy predefined business rules and constraints for a specific objective. This objective is stated in terms of functional goals like desired or acceptable process behavior, and nonfunctional goals such as cost, time, quality of service, and flexibility. Thus, many potential improvement possibilities that emerge through restructuring can be explored. When many such alternatives exist, there is a need for a formal methodology for evaluating these designs on various metrics to gain a deeper understanding of the tradeoffs involved. In this article, we describe a novel, formal methodology for optimizing the cost-benefit considerations within a space of possible redesigns to develop a cost vs. time efficient frontier. We also illustrate our approach with two case studies, one based on a large, real dataset, and describe a proposal for implementing it. Several managerial insights emerge from our work.

Binary "YES-NO" notions of process compliance are not very helpful to managers for assessing the operational performance of their company because a large number of cases fall in the grey area of partial compliance. Hence, it is necessary to have ways to quantify partial compliance in terms of metrics and be able to classify actual cases by assigning a numeric value of compliance to them. In this paper, we formulate an evaluation framework to quantify the level of compliance of business processes across different levels of abstraction (such as task,trace and process level) and across multiple dimensions of each task (such as temporal, monetary, role-, data-, and quality-related) to provide managers more useful information about their operations and to help them improve their decision making processes. Our approach can also add social value by making social services provided by local, state and federal governments more flexible and improving the lives of citizens.

Business Process Management (BPM) systems usually neglect the human and social aspects (or team effects) involved in business process execution. Our work fills a large gap in literature by addressing multi-level teams that arise in business processes where teams are formed at both the task and process levels. In this paper, we develop a methodology called BPMTeams based on social network analysis for building an execution model for a social BPM. This model is used to make resource assignments to form dynamic teams that perform various team-based activities in a process. We further develop various resource assignment strategies and evaluate them using parameters estimated from a real data set in the IT incident management domain to understand how team effects play out in social business processes. The overall team effect in a process is analyzed at two levels: as a task team effect where the synergistic role of a team in a specific task is realized; and a process team effect that arises from inter-team synergies across the individual task teams in a process. The results offer some balanced insights for the interplay of these effects by highlighting the benefits and disadvantages of teams selected by a purely data-driven approach.

Process model extraction (PME) is a recently emerged interdiscipline between natural language processing (NLP) and business process management (BPM), which aims to extract process models from textual descriptions. Previous process extractors heavily depend on manual features and ignore the potential relations between clues of different text granularities. In this paper, we formalize the PME task into the multi-grained text classification problem, and propose a hierarchical neural network to effectively model and extract multi-grained information without manually-defined procedural features. Under this structure, we accordingly propose the coarse-to-fine (grained) learning mechanism, training multi-grained tasks in coarse-to-fine grained order to share the high-level knowledge for the low-level tasks. To evaluate our approach, we construct two multi-grained datasets from two different domains and conduct extensive experiments from different dimensions. The experimental results demonstrate that our approach outperforms the state-of-the-art methods with statistical significance and further investigations demonstrate its effectiveness.

Blockchain technology enables various business transactions to be performed in an immutable and transparent manner. Within the business process management community, blockchain technology has been positioned as a way to better support the execution of inter-organisational business processes, where the entities involved may not completely trust each other. However, the architectures proposed thus far in the literature for blockchain-enabled business process management can be described as “heavy-weight”, since they promote the blockchain platform as the monolithic focal point of all business logic and process operations. We propose an alternative: a federated and flexible architecture that leverages the capabilities of blockchain, but without overloading the functionalities of the blockchain platform with those already extant in Business Process Management Systems (BPMSs). We illustrate its benefits, and demonstrate its feasibility, through the implementation of a prototype.

As we are living in the era of social media apps, from Facebook to WhatsApp which are using everywhere. All these apps are being used by everyone. Although this may seem to be a very good sign that we are moving to the new era of “THE DIGITAL WORLD” but it may have some consequences like spreading of artificial news, crack of personal information like credit card, debit card, passwords or digital wallets etc. The users believe that every message shared on social media might be true. So, to protect our internet users we have come up with an idea that provides the ability to discover homonym attack and malicious links which warns the user before they can access the site. The Social engineering attacks have stirred terribly removed from this like fraudulent attack within which we tend to completely rely upon our browser to present north American nation a warning. This situation may worry some computer users, but we generally do not think much about when we perform any action on our mobile phones. But all these so called to do steps are not the right way to deal with these situation .The primary commitments of this paper are understanding a working meaning of IDN satirizing assaults and how those IDN spaces are being introduced in the URL bar in some Internet programs, proposing a working arrangement that reports IDN ridiculing assaults which convert URL into Unicode and punycode.

In this paper, we study the characteristics of a global help desk operation using Volvo IT support help desk. We analyze the incidents log produced by the Volvo IT department that gives the full path of an incident and the participating country and worker at each step. Our main goal is to gain a better understanding of the nature of international traffic flows that occur during the resolution of incidents. We find that increasing the number of participating countries negatively affects IT service efficiency metrics. Further, we breakdown international traffic flows (by highand low-efficiency countries) and examine the country effects and their implications in terms of efficiency and cost in considerable detail. The results and insights gained are discussed at length and can help in optimizing incident resolution workflows from a cost, efficiency and resource allocation perspective.