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Pergamon Press
Information systems

Pergamon Press

0306-4379

Information systems/Journal Information systemsSCIEI
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    Business process simulation: Probabilistic modeling of intermittent resource availability and multitasking behavior

    Lopez-Pintado O.Dumas M.
    1.1-1.18页
    查看更多>>摘要：© 2024 Elsevier LtdIn business process simulation, resource availability is typically modeled by assigning a calendar to each resource, e.g., Monday–Friday, 9:00–18:00. Resources are assumed to be always available during each time slot in their availability calendar. This assumption often becomes invalid due to interruptions, breaks, or time-sharing across processes. In other words, existing approaches fail to capture intermittent availability. Another limitation of existing approaches is that they either do not consider multitasking behavior, or if they do, they assume that resources always multitask (up to a maximum capacity) whenever available. However, studies have shown that the multitasking patterns vary across days. This paper introduces a probabilistic approach to model resource availability and multitasking behavior for business process simulation. In this approach, each time slot in a resource calendar has an associated availability probability and a multitasking probability per multitasking level. For example, a resource may be available on Fridays between 14:00–15:00 with 90% probability, and given that they are performing one task during this slot, they may take on a second concurrent task with 60% probability. We propose algorithms to discover probabilistic calendars and probabilistic multitasking capacities from event logs. An evaluation shows that, with these enhancements, simulation models discovered from event logs better replicate the distribution of activities and cycle times, relative to approaches with crisp calendars and monotasking assumptions.
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    • NETL
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    • Elsevier

    Finding meaningful paths in heterogeneous graphs with PathWays

    Barret N.Manolescu I.Gauquier A.Law J.-J....
    1.1-1.15页
    查看更多>>摘要：© 2024 Elsevier LtdGraphs, and notably RDF graphs, are a prominent way of sharing data. As data usage democratizes, users need help figuring out the useful content of a graph dataset. In particular, journalists with whom we collaborate are interested in identifying, in a graph, the connections between entities, e.g., people, organizations, emails, etc. We present a novel method for exploring data graphs through their data paths connecting Named Entities (NEs, in short); each data path leads to a tabular-looking set of results. NEs are extracted from the data through dedicated Information Extraction modules. Our method builds upon the pre-existing ConnectionLens platform and follow-up work in the Abstra project, which builds simple, visual ER-style summaries of semi-structured data. The contribution of the present work, and its novelty, is twofold. First, we propose a novel analysis of entity-to-entity paths contained in datasets of any nature, and propose a new method for ranking paths, leveraging a novel Information Extraction (IE) module we built on top of ChatGPT. Second, we present an efficient approach to enumerate and compute NE paths, based on an algorithm which automatically recommends sub-paths to materialize, and rewrites the path queries using these subpaths. Our experiments demonstrate the interest of NE paths and the efficiency of our method for computing and ranking them.
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    • NETL
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    • Elsevier

    Electricity behaviors anomaly detection based on multi-feature fusion and contrastive learning

    Guan Y.Shi Y.Wang X.Chen Z....
    1.1-1.10页
    查看更多>>摘要：© 2024 Elsevier LtdAbnormal electricity usage detection is the process of discovering and diagnosing abnormal electricity usage behavior by monitoring and analyzing the electricity usage in the power system. How to improve the accuracy of anomaly detection is a popular research topic. Most studies use neural networks for anomaly detection, but ignore the effect of missing electricity data on anomaly detection performance. Missing value completion is an important method to improve the quality of electricity data and to optimize the anomaly detection performance. Moreover, most studies have ignored the potential correlation relationship between spatial features by modeling the temporal features of electricity data. Therefore, this paper proposes an electricity anomaly detection model based on multi-feature fusion and contrastive learning. The model integrates the temporal and spatial features to jointly accomplish electricity anomaly detection. In terms of temporal feature representation learning, an improved bi-directional LSTM is designed to achieve the missing value completion of electricity data, and combined with CNN to capture the electricity consumption behavior patterns in the temporal data. In terms of spatial feature representation learning, GCN and Transformer are used to fully explore the complex correlation relationships among data. In addition, in order to improve the performance of anomaly detection, this paper also designs a gated fusion module and combines the idea of contrastive learning to strengthen the representation ability of electricity data. Finally, we demonstrate through experiments that the method proposed in this paper can effectively improve the performance of electricity behavior anomaly detection.
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    • Elsevier

    A framework for measuring the quality of business process simulation models

    Chapela-Campa D.Dumas M.Benchekroun I.Baron O....
    1.1-1.18页
    查看更多>>摘要：© 2024 The AuthorsBusiness Process Simulation (BPS) is an approach to analyze the performance of business processes under different scenarios. For example, BPS allows us to estimate the impact of adding one or more resources on the cycle time of a process. The starting point of BPS is a process model annotated with simulation parameters (a BPS model). BPS models may be manually designed, based on information collected from stakeholders and from empirical observations, or automatically discovered from historical execution data. Regardless of its provenance, a key question when using a BPS model is how to assess its quality. In particular, in a setting where we are able to produce multiple alternative BPS models of the same process, this question becomes: How to determine which model is better, to what extent, and in what respect? In this context, this article studies the question of how to measure the quality of a BPS model with respect to its ability to accurately replicate the observed behavior of a process. Rather than pursuing a one-size-fits-all approach, the article recognizes that a process covers multiple perspectives. Accordingly, the article outlines a framework that can be instantiated in different ways to yield quality measures that tackle different process perspectives. The article defines a number of concrete quality measures and evaluates these measures with respect to their ability to discern the impact of controlled perturbations on a BPS model, and their ability to uncover the relative strengths and weaknesses of two approaches for automated discovery of BPS models. The evaluation shows that the proposed measures not only capture how close a BPS model is to the observed behavior, but they also help us to identify the sources of discrepancies.
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    • Elsevier

    Tri-AL: An open source platform for visualization and analysis of clinical trials

    Nahed P.Kambar M.E.Z.N.Taghva K.Golab L....
    1.1-1.13页
    查看更多>>摘要：© 2024 Elsevier LtdClinicalTrials.gov hosts an online database with over 440,000 medical studies (as of 2023) evaluating drugs, supplements, medical devices, and behavioral treatments. Target users include scientists, medical researchers, pharmaceutical companies, and other public and private institutions. Although ClinicalTrials has some filtering ability, it does not provide visualization tools, reporting tools or historical data; only the most recent state of each trial is visible to users. To fill these functionality gaps, we present Tri-AL: an open-source data platform for clinical trial visualization, information extraction, historical analysis, and reporting. This paper describes the design and functionality of Tri-AL, including a programmable module to incorporate machine learning models and extract disease-specific data from unstructured trial reports, which we demonstrate using Alzheimer's disease reporting as a case study. We also highlight the use of Tri-AL for trial participation analysis in terms of sex, gender, race and ethnicity. The source code is publicly available at https://github.com/pouyan9675/Tri-AL.
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    • NETL
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    • Elsevier

    Proactive conformance checking: An approach for predicting deviations in business processes

    Grohs M.Rehse J.-R.Pfeiffer P.
    1.1-1.18页
    查看更多>>摘要：© 2024 The AuthorsModern business processes are subject to an increasing number of external and internal regulations. Compliance with these regulations is crucial for the success of organizations. To ensure this compliance, process managers can identify and mitigate deviations between the predefined process behavior and the executed process instances by means of conformance checking techniques. However, these techniques are inherently reactive, meaning that they can only detect deviations after they have occurred. It would be desirable to detect and mitigate deviations before they occur, enabling managers to proactively ensure compliance of running process instances. In this paper, we propose Business Process Deviation Prediction (BPDP), a novel predictive approach that relies on a supervised machine learning model to predict which deviations can be expected in the future of running process instances. BPDP is able to predict individual deviations as well as deviation patterns. Further, it provides the user with a list of potential reasons for predicted deviations. Our evaluation shows that BPDP outperforms existing methods for deviation prediction. Following the idea of action-oriented process mining, BPDP thus enables process managers to prevent deviations in early stages of running process instances.
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    • NETL
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    • Elsevier

    Using AI explainable models and handwriting/drawing tasks for psychological well-being

    Prinzi F.Vitabile S.Barbiero P.Greco C....
    1.1-1.11页
    查看更多>>摘要：© 2024 The AuthorsThis study addresses the increasing threat to Psychological Well-Being (PWB) posed by Depression, Anxiety, and Stress conditions. Machine learning methods have shown promising results for several psychological conditions. However, the lack of transparency in existing models impedes practical application. The study aims to develop explainable machine learning models for depression, anxiety and stress prediction, focusing on features extracted from tasks involving handwriting and drawing. Two hundred patients completed the Depression, Anxiety, and Stress Scale (DASS-21) and performed seven tasks related to handwriting and drawing. Extracted features, encompassing pressure, stroke pattern, time, space, and pen inclination, were used to train the explainable-by-design Entropy-based Logic Explained Network (e-LEN) model, employing first-order logic rules for explanation. Performance comparison was performed with XGBoost, enhanced by the SHAP explanation method. The trained models achieved notable accuracy in predicting depression (0.749 ±0.089), anxiety (0.721 ±0.088), and stress (0.761 ±0.086) through 10-fold cross-validation (repeated 20 times). The e-LEN model's logic rules facilitated clinical validation, uncovering correlations with existing clinical literature. While performance remained consistent for depression and anxiety on an independent test dataset, a slight degradation was observed for stress prediction in the test task.
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    • Elsevier

    A universal approach for simplified redundancy-aware cross-model querying

    Koupil P.Crha D.Holubova I.
    1.1-1.32页
    查看更多>>摘要：© 2024 Elsevier LtdNumerous challenges and open problems have appeared with the dawn of multi-model data. In most cases, single-model solutions cannot be straightforwardly extended, and new, efficient approaches must be found. In addition, since there are no standards related to combining and managing multiple models, the situation is even more complicated and confusing for users. This paper deals with the most important aspect of data management — querying. To enable the user to grasp all the popular models, we base our solution on the abstract categorical representation of multi-model data, which can be viewed as a graph. To unify the querying of multi-model data, we enable the user to query the categorical graph using a SPARQL-based model-agnostic query language called MMQL. The query is then decomposed and translated into languages of the underlying systems. The intermediate results are then combined into the final categorical result that can be expressed in any selected format. The support for cross-model redundancy enables one to create distinct query plans and choose the optimal one. We also introduce a proof-of-concept implementation of our solution called MM-quecat.
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    • NETL
    • NSTL
    • Elsevier

    Making cyber-human systems smarter

    Alter S.
    1.1-1.12页
    查看更多>>摘要：© 2024The term smart is often used carelessly in relation to systems, devices, and other entities such as cities that capture or otherwise process or use information. This conceptual paper treats the idea of smartness in a way that suggests directions for making cyber-human systems smarter. Cyber-human systems can be viewed as work systems. This paper defines work system, cyber-human system, algorithmic agent, and smartness of systems and devices. It links those ideas to challenges that can be addressed by applying ideas that managers and IS designers discuss rarely, if at all, such as dimensions of smartness for devices and systems, facets of work, roles and responsibilities of algorithmic agents, different types of engagement and patterns of interaction between people and algorithmic agents, explicit use of various types of knowledge objects, and performance criteria that are often deemphasized. In combination, those ideas reveal many opportunities for IS analysis and design practice to make cyber-human systems smarter.
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    • NETL
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    • Elsevier

    Bridging reading and mapping: The role of reading annotations in facilitating feedback while concept mapping

    Diaz O.Garmendia X.
    1.1-1.17页
    查看更多>>摘要：© 2024 The Author(s)Concept maps are visual tools for organizing knowledge, commonly used in education and design. The process often involves reading and developing conceptual models, where feedback is crucial. Learners (e.g., students, designers) often refer to reading materials, and receive feedback from instructors (e.g., teachers, stakeholders) based on the maps they create. However, annotations made by learners, like highlights, are usually not visible to instructors, limiting tailored feedback. We propose incorporating annotation practices into concept mapping. Learners could highlight text and link these highlights to existing or newly created concepts in their concept map. This way, instructors can access both the concept map and the relevant readings for better feedback. This vision is realized through Concept&Go, a plug-in for the editor CmapCloud. This extension aims at the interplay between mapping, reading, and feedback during concept mapping. The effectiveness of this approach is demonstrated through a focus group (n=5) and a UTAUT evaluation (n=12). Concept&Go is publicly available.
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    • NETL
    • NSTL
    • Elsevier