1,721,043 research outputs found
Detecting flight trajectory anomalies and predicting diversions in freight transportation (extended abstract)
No full textTimely identifying flight diversions is a crucial aspect of efficient multi-modal transportation. When an airplane diverts, logistics providers must promptly adapt their transportation plans in order to ensure proper delivery despite such an unexpected event. In practice, the different parties in a logistics chain do not exchange real-Time information related to flights. This calls for a means to detect diversions that just requires publicly available data, thus being independent of the communication between different parties. The dependence on public data results in a challenge to detect anomalous behavior without knowing the planned flight trajectory. Our work addresses this challenge by introducing a prediction model that just requires information on an airplane's position, velocity, and intended destination. This information is used to distinguish between regular and anomalous behavior. When an airplane displays anomalous behavior for an extended period of time, the model predicts a diversion. A quantitative evaluation shows that this approach is able to detect diverting airplanes with excellent precision and recall even without knowing planned trajectories as required by related research. By utilizing the proposed prediction model, logistics companies gain a significant amount of response time for these cases. The work summarized in this extended abstract has been published in [Di16]
RuM: Declarative Process Mining, Distilled
Full text linkFlexibility is a key characteristic of numerous business process management domains. In these domains, the paths to fulfil process goals may not be fully predetermined, but can strongly depend on dynamic decisions made based on the current circumstances of a case. A common example is the adaptation of a standard treatment process to the needs of a specific patient. However, high flexibility does not mean chaos: certain key process rules still delimit the execution space, such as rules that prohibit the joint administration of certain drugs in a treatment, due to dangerous interactions. A renowned means to handle flexibility by design is the declarative approach, which aims to define processes through their core behavioural rules, thus leaving room for dynamic adaptation. This declarative approach to both process modelling and mining involves a paradigm shift in process thinking and, therefore, the support of novel concepts and tools. Complementing our tutorial with the same title, this paper provides a high-level introduction to declarative process mining, including its operationalisation through the RuM toolkit, key conceptual considerations, and an outlook for the future
Resource Optimization in Business Processes
Full text linkIn administrative processes, such as financial or governmental processes, humans typically do most of the work and must be allocated to tasks in an efficient manner. This allocation is made complicated by the different authorizations and the varying effectiveness of people for tasks. Moreover, administrative processes operate under substantial uncertainty, as the customer’s journey through the process typically is uncertain upon their arrival. To help solve this problem, we present a framework for resource optimization in administrative processes and delineate its differences from existing resource allocation models. We proceed to show several resource allocation solutions that have been developed with the framework. We specifically address the challenges that are encountered when implementing these solutions, some of which remain unresolved. By doing so we aim to shed light on promising avenues for future research in this domain.</p
Which Tables are Mine(able)?
No full textIdentifying relevant tables in databases to build event logs is typically a manual, error-prone task in process mining. This paper introduces TabMine, a semi-automated algorithm that identifies these tables by leveraging both the network structure of tables and their natural language descriptions. By integrating process-related business documents with table metadata, TabMine employs machine learning techniques, specifically community detection and natural language processing, to align table communities with the corresponding documents. This enables analysts to build event logs for process mining from a targeted list of tables without prior knowledge of the specific database or ERP system
Towards a Maturity Assessment Framework for MBSE Adoption: Results from a Meta-synthesis
No full textAs engineering systems become increasingly complex, organizations must adopt strategic approaches to manage the interdependencies of their processes, tools, and teams. Model-Based Systems Engineering (MBSE) offers a promising solution, but transitioning from a traditional SE approach to MBSE is a complex endeavor that requires significant organizational change. This paper addresses the need for structured guidance in this process by proposing a maturity assessment framework that supports organizations in navigating this transition. The proposed framework is developed using a design science based approach and identifies key challenges, pitfalls, and best practices that are organized into several maturity levels of MBSE adoption. This structured, high-level approach provides organizations with the tools to understand their current maturity level, prioritize efforts, and avoid common missteps. The framework allows organizations to tailor the insights to their unique context, ensuring practical applicability. It emphasizes the importance of leadership, cultural readiness, technical tools, workforce development, and modeling practices for successful MBSE implementation
Understanding Capability Progression:A Model for Defining Maturity Levels for Organizational Capabilities
Full text linkThe pressure for organizations to gain and keep their competitive advantage necessitates continuous assessment and improvement of their capabilities. Maturity modeling has emerged as a management approach to guide organizations in developing and improving their capabilities, following a structured path for improvement within a specific domain. Existing research lacks a theoretically grounded model for defining maturity levels, particularly concerning organizational capabilities. This paper addresses this gap by introducing a model for defining maturity levels for organizational capabilities. Drawing on the Dreyfus model of skill acquisition, the model defines the characteristics of organizational capabilities across six maturity levels. The model is developed following design science research and demonstrated and evaluated in the development of a data analytics maturity model. The findings of the expert survey provided positive evidence regarding the validity, relevance, completeness, clarity, and utility. We emphasize the distinction between capability-based and process-based maturity levels and propose our model as a tool to support the development of capability-based maturity models in various domains
Learning Analytics Dashboard with Peer Comparison for Student Feedback in Conceptual Modeling Education
No full textConceptual modeling education benefits from technological support due to the complex nature of the learning processes required to master modeling skills. Along with existing modeling and prototyping tools, providing feedback to students using Learning Analytics Dashboards (LADs) can enhance their learning experience. To interpret LADs, students are provided with a frame of reference, often peer comparison, although its effectiveness is debated. This study presents two LADs used to provide feedback to students from diverse backgrounds enrolled in a conceptual modeling course: a default-LAD with mastery and progress reference frames, and an extended peer-LAD that also includes a performance reference frame. We examine students’ preferences for LAD visuals, the relationship between their study activity and performance, and the relationship between the use patterns of different LAD versions and student activity and performance. The results show that most of the relationships are significant only for the peer-enhanced LAD and are stronger for students with less modeling experience, underscoring the value of peer LADs for novice modelers
Towards Taming Large Language Models with Prompt Templates for Legal GRL Modeling
Full text linkThe Legal Goal-oriented Requirements Language (Legal GRL) is a promising conceptual modeling approach for supporting regulatory compliance analysis. Yet, despite early attempts at automation, such Legal GRL models are still manually created, being time consuming and error prone. Recent work has demonstrated how Large Language Models can support the creation of conceptual models. Although showing promise, the application scenarios for conceptual modeling are often limited to well structured, and scoped, scenarios. Dealing with practical, less controlled, regulatory analyses, whereby often a particular actor or topic needs to be pulled into focus, is an open issue. In this paper, we propose using prompt templates to structure the process of using LLMs to create a Legal GRL model from text. The core idea is that prompt templates are created from state of the art prompt patterns, which can restrict LLM output, can manage a LLM conversation context, and can structure a LLM conversation. We report on an initial assessment of prompt templates on multiple law articles from the healthcare and energy community domains. Our initial results are promising for Legal GRL modeling, but at the same time show that caution is warranted.</p
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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