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检索条件"丛书名=Lecture Notes in Business Information Processing 252"

共 8647 条记录，以下是551-560 订阅

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CaseID Detection for Process Mining: A Heuristic-Based Methodology 5th

CaseID Detection for Process Mining: A Heuristic-Based Metho...

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5th International Conference on Process Mining (ICPM)

作者： De Fazio, Roberta Balzanella, Antonio Marrone, Stefano Marulli, Fiammetta Verde, Laura Reccia, Vincenzo Valletta, Paolo Univ Campania Luigi Vanvitelli Dipartimento Matemat & Fis Viale Lincoln 7 Caserta Italy Gemat Srl Via Diocleziano 107 Naples Italy

ISBN: (纸本)9783031561061;9783031561078

Process Mining is getting a growing interest in many contexts where performance bottlenecks are critical for the business. Unfortunately, real cyber-physical systems are usually not implemented to easily address these techniques. One of the most frequent problems to face is transforming acquired data, often heterogeneous and unlabeled to allow the application of Process Mining technique. In this study, we propose an automatised and unsupervised methodology for extracting CaseIDs from an unlabeled event log. The proposed detection of CaseIDs is based on the definition of appropriate heuristic metrics, able to highlight the correlation between events that are part of the same process instance, according to temporal and topological features (e.g., kinds of functionally-related devices, topological distance, etc.). These features constitute the inputs for a clustering technique, which has been used to extract different cases. The performance of the proposed methodology was evaluated on a real diagnostic management system to support the decisions in maintenance operations in railway infrastructures. The system has been reproduced and tested in Gematica's laboratory for simulating the data used in this work.

关键词： Unlabeled Event Log CaseID detection Event Data K-Means Clustering Predictive Maintenance Railway Infrastructure

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Identifying Relevant Data in RDF Sources 18th

Identifying Relevant Data in RDF Sources

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18th International Conference on Research Challenges in information Sciences (RCIS)

作者： Chevallier, Zoe Kedad, Zoubida Finance, Beatrice Chaillan, Frederic Univ Versailles Paris Saclay DAVID Lab Versailles France Grand Paris Sud Evry France

ISBN: (纸本)9783031594670;9783031594687

The increasing number of RDF data sources published on the web represents an unprecedented amount of information. However, querying these sources to extract the relevant information for a specific need represented by a target schema is a complex task as the alignment between the target and the source schemas might not be provided or incomplete. This paper presents an approach which aims at automatically populating the classes of a target schema. Our approach relies on a semi-supervised learning algorithm that iteratively identifies instance patterns in the data source that represent candidate instances for the target schema. We present some preliminary experiments showing the effectiveness of our approach.

关键词： RDF data sources Target Schema Instantiation Web Data extraction Semi-supervised learning

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Forecasting Electricity Price During Extreme Events Using a Hybrid Model of LSTM and ARIMA Architecture 25th

Forecasting Electricity Price During Extreme Events Using a ...

引用

25th International Conference on Enterprise information Systems (ICEIS)

作者： Borges, Joao Maia, Rui Guerreiro, Sergio Univ Lisbon Inst Super Tecn Lisbon Portugal INESC ID Rua Alves Redol 9 P-1000029 Lisbon Portugal

ISBN: (纸本)9783031647475;9783031647482

Forecasting electricity prices during extreme events is crucial for grid stability, cost management, and risk mitigation in the energy sector. This study presents a novel hybrid forecasting framework that integrates Long Short-Term Memory (LSTM) and Autoregressive Integrated Moving Average (ARIMA) models, enhanced with the Holt-Winters method. The issues caused by extreme events in energy markets, where abrupt and unpredictably changing price variations are typical, are addressed by this hybrid architecture. The ARIMA model adds statistical rigour to account for subtle underlying patterns, while the LSTM component captures intricate temporal connections and quick price fluctuations linked to intense occurrences. The Holt-Winters approach further enhances the model's ability to handle seasonality and trend variations, which become especially pronounced during extreme events. This paper intends to present the current project development stage and guide research on precisely investigating optimal predictive modelling methods in extreme event scenarios.

关键词： Extreme events Electricity price forecasting Machine learning

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Fast & Sound: Accelerating Synthesis-Rules-Based Process Discovery 1

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25th International Working Conference on business Process Modeling, Development, and Support (BPMDS) / 29th International Working Conference on Exploring Modeling Methods for Systems Analysis and Development (EMMSAD)

作者： Huang, Tsung-Hao Schneider, Enzo Pegoraro, Marco van der Aalst, Wil M. P. Rhein Westfal TH Aachen Proc & Data Sci PADS Aachen Germany Rhein Westfal TH Aachen Aachen Germany

ISBN: (数字)9783031610073

ISBN: (纸本)9783031610066;9783031610073

Process discovery aims to construct process models describing the observed behaviors of information systems. It is an essential step in process mining projects as most process mining techniques assume a process model as input. While various process discovery algorithms exist, few provide desirable properties: soundness and free-choiceness. By exploiting the free-choice net theory, the recently developed Synthesis Miner not only guarantees the two desirable properties but also enables a more flexible representation (non-block structures) of the discovered process models. The flexibility allows the Synthesis Miner to discover process models with potentially higher quality. Nevertheless, applying the Synthesis Miner remains a challenge due to its lack of scalability. In this paper, we identify the bottleneck and address it by introducing various extensions that utilize the log heuristics and extract the minimal sub-net of the process model. The evaluation using real-life event logs shows that the proposed extensions improve the scalability of the Synthesis Miner by reducing the computation time by 82.85% on average.

关键词： Process Modeling Process Mining Process Discovery Free-choice Workflow Net Synthesis Rules

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Analyzing Customer Sentiments: A Comparative Evaluation of Large Language Models for Enhanced business Intelligence 1

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36th International Conference on Advanced information Systems Engineering (CAiSE)

作者： Beranek, Pavel Merunka, Vojtech Czech Univ Life Sci Fac Econ & Management Dept Informat Engn Prague Czech Republic Jan Evangelista Purkyne Univ Usti Nad Labem Sci Fac Dept Informat Usti Nad Labem Czech Republic Czech Univ Life Sci Prague Fac Econ & Management Dept Informat Engn Prague Czech Republic Czech Tech Univ Fac Nucl Sci & Phys Engn Dept Software Engn Prague Czech Republic

ISBN: (数字)9783031610035

ISBN: (纸本)9783031610028;9783031610035

This paper presents a comparative evaluation of Large Language Models (LLMs) against established sentiment analysis tools, focusing on their efficiency in parsing complex customer reviews for sentiment and emotion. Our research reveals that LLMs are capable of detecting a wide range of emotions, offering granular insights that transcend traditional analysis capabilities. However, the study also highlights a notable discrepancy in reproducibility and reliability among smaller LLMs (SLMs) when compared to the VADER model, a stalwart in sentiment analysis. These findings elucidate the potential and limitations of employing SLMs for business intelligence purposes, particularly in automating sentiment analysis of customer feedback. We discuss the implications of these outcomes for businesses looking to leverage AI for customer insights, emphasizing the importance of model selection based on the specific requirements of reproducibility and depth of analysis.

关键词： Large Language Models Small Language Models Sentiment Analysis Granular Sentiment Analysis Emotion Detection

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Evidence-Based Student Career and Performance Analysis with Process Mining: A Case Study 5th

Evidence-Based Student Career and Performance Analysis with ...

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5th International Conference on Process Mining (ICPM)

作者： Potena, Domenico Genga, Laura Basta, Annalisa Mercati, Chiara Diamantini, Claudia Univ Politecn Marche Ancona Italy Eindhoven Univ Technol Eindhoven Netherlands

ISBN: (纸本)9783031561061;9783031561078

Educational process mining aims at leveraging data generated during students' learning processes to extract evidence-based insights supporting the continuous improvements of educational programs. In this work, we showcase the application of educational process mining techniques to analyze students' careers at an Italian university, focusing on their progression and outcomes. The study uncovers trends in compliance with curriculum requirements, exam-taking patterns and graduation times. Predictive models are then employed to elucidate the impact of different factors, e.g., the number of exams passed during the first year, on graduation times. These findings provide insights for educational institutions seeking support mechanisms to improve students' success rates.

关键词： Educational process mining Curriculum Mining Student performance analysis

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Exploring Gamification in Process Mining Education: Towards a Playful and Engaging Approach 5th

Exploring Gamification in Process Mining Education: Towards ...

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5th International Conference on Process Mining (ICPM)

作者： Delcoucq, Landelin Univ Mons UMONS Mons Belgium

ISBN: (纸本)9783031561061;9783031561078

This article explores how gamification, i.e. the integration of game elements, of the teaching of Process Mining enhances its effectiveness. By combining traditional academic teaching with game elements, gamification stimulates student engagement, facilitates understanding of complex concepts and encourages active learning. This article defines what gamification is (what), what it can bring to the teaching of Process Mining (why), how it can be implemented (how) as well as its limitations and how its development can be envisaged based on strong theoretical concepts and data gathered from the audience concerned.

关键词： Teaching Gamification Process Mining

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Comparative Evaluation of Non-immersive and Immersive Approaches for Upper Limb Rehabilitation: A Performance and Usability Study 25th

Comparative Evaluation of Non-immersive and Immersive Approa...

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25th International Conference on Enterprise information Systems (ICEIS)

作者： Herrera, V Reyes-Guzman, A. Vallejo, D. Castro-Schez, J. J. Monekosso, D. Gonzalez-Morcillo, C. Albusac, J. Univ Castilla La Mancha Ciudad Real Spain Hosp Nacl Paraplej Toledo Toledo Spain Univ Durham Durham England

ISBN: (纸本)9783031647543;9783031647550

Emerging technologies have recently played a critical role in enhancing the upper extremity rehabilitation process, serving as an adjunct to the traditional therapeutic treatments that patients receive. Ideally, these technologies should facilitate accurate tracking of hand movements, collect data on patient performance during functional tasks, and provide an objective view of potential patient progress. While wearable and mechanical devices offer superior accuracy in capturing measurements, the physical limitations of patients require the introduction of hands-free solutions. This research explores hands-free hand tracking technologies in the Box and Block Test (BBT). It compares them to the actual test performed at the Hospital Nacional de Paraplejicos (Toledo, Spain). The study analyses the kinematics in order to evaluate the performance of the therapeutic exercises in three different environments: a technology-free physical environment, a non-immersive virtual space and a fully immersive virtual scenario. This evaluation includes a functional analysis based on the precision of the movements and the goals achieved to complete the therapy. In addition, the clinical study employs a comprehensive questionnaire to evaluate usability, user interaction, presence, and potential VR-induced effects. The results show a remarkable similarity between therapies delivered in traditional, technology-free environments and those delivered in virtual reality-based scenarios.

关键词： Virtual reality Upper limb rehabilitation Free-hand interaction Hand tracking Usability

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Evolutionary Graph-Clustering vs Evolutionary Cluster-Detection Approaches for Community Identification in PPI Networks 6th

Evolutionary Graph-Clustering vs Evolutionary Cluster-Detect...

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6th International Conference on information and Knowledge Systems (ICIKS)

作者： Ben M'Barek, Marwa Ben Hmida, Sana Borgi, Amel Rukoz, Marta Univ Tunis El Manar Fac Sci Tunis LIPAH Tunis 2092 Tunisia Paris Dauphine Univ PSL Res Univ CNRS UMR 7243LAMSADE Paris France

ISBN: (纸本)9783031516634;9783031516641

Community detection in protein-protein interaction networks (PPIs) is an active area of research, and many studies have applied Genetic Algorithms (GAs) to this problem. This paper summarizes the different GA based approaches for community detection in PPIs and provides a taxonomy of these methods. Detailed comparative studies are then provided comparing an evolutionary graph-clustering approach (EGCPI) based on the partitioning paradigm and an evolutionary cluster-detection approach based on an evolutive and incremental search for potential communities in the graphs (GA-PPI-Net). The communities obtained by the two algorithms on Collins PPI network are compared according to the average similarity and interaction between genes, and also according to the recovery rate of known communities in some biological pathways. Experiments tests verify the effectiveness of the GA-PPI-Net approach compared with EGCPI approach.

关键词： Community detection Biological networks PPI networks Genetic Algorithm GA-PPI-Net EGCPI

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Empirical Investigation of Quantum Computing on Solving Complex Problems

Empirical Investigation of Quantum Computing on Solving Comp...

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24th International Conference on Agile Software Development (XP)

作者： Hussain, Shahid Neupane, Yuba Wang, Wen-Li Ibrahim, Naseem Khan, Saif Ur Rehman Kareem, Asif Penn State Behrend Dept Comp Sci & Software Engn Sch Engn Erie PA 16563 USA Shifa Tameer E Millat Univ STMU Dept Comp Islamabad 45550 Pakistan Advocate Hlth Downers Grove IL USA

ISBN: (纸本)9783031485497;9783031485503

Context: The rules of Quantum Mechanics have been exploited through Quantum Computing (QC) to solve specific problems and process information in expeditious ways as compared to Conventional Computing (CC) such as factoring integers. Problem: With the alluring computation capability of QC, it is still important to assess the implications and limitations of QC in solving a variety of computationally demanding problems. Method: In this regard, an empirical study was conducted to assess the efficacy of QC in terms of solving certain complex problems by keeping a tradeoff between the execution time and problem size. An analysis was performed based on the widely used Shor's algorithms and the efficacy of QC as compared to CC was reported. Results: The outcomes show that QC has the potential to exponentially speed up the identification of a solution to certain polynomial problems that are intractable for CC. However, further research is needed to fully understand the potential and limitations of QC for Non-Polynomial (NP) complete problems.

关键词： Quantum Computing Conventional Computing Shor's Algorithm Complexity

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