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

共 8057 条记录，以下是61-70 订阅

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ProcessLLM: A Large Language Model Specialized in the Interpretation, Analysis, and Optimization of business Processes 22nd

ProcessLLM: A Large Language Model Specialized in the Interp...

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22nd International Conference on business Process Management (BPM)

作者： Buss, Alina Kratsch, Wolfgang Schmid, Sebastian Johannes Wang, Hongyang Tech Univ Munich TUM Sch Management Munich Germany Tech Univ Appl Sci Augsburg Augsburg Germany Univ Bayreuth Bayreuth Germany Fraunhofer FIT Branch Business & Informat Syst Engn Augsburg Germany Fraunhofer FIT Branch Business & Informat Syst Engn Bayreuth Germany FIM Res Ctr Informat Management Augsburg Germany FIM Res Ctr Informat Management Bayreuth Germany

ISBN: (纸本)9783031786655;9783031786662

Generative AI, notably embodied by Large Language Models (LLMs), has the potential to revolutionize key aspects of business Process Management (BPM). Addressing the inadequacies of general-purpose LLMs in complex BPM tasks, we follow the Design Science Research paradigm and propose ProcessLLM, a specialized LLM fine-tuned to support BPM tasks. Evaluated in both artificial and real-life business settings, ProcessLLM demonstrates promising performance and a sophisticated understanding of BPM concepts and the BPMN language. Our research serves as a foundational exploration at the intersection of BPM and LLMs, paving the way for future research in LLM-enhanced BPM. Validated by business experts, our research also demonstrates the real-world utility and applicability of LLMs in BPM, thus setting the stage for their broader adoption in operational BPM settings.

关键词： business Process Management Large Language Models Generative Artificial Intelligence Natural Language processing

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TrustOps: Continuously Building Trustworthy Software 2nd

TrustOps: Continuously Building Trustworthy Software

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28th International Conference on Enterprise Design Operations and Computing

作者： Brito, Eduardo Castillo, Fernando Pullonen-Raudvere, Pille Werner, Sebastian Cybernetica AS Estonia Maealuse 2-1 EE-12618 Tallinn Estonia TU Berlin Informat Syst Engn Einsteinufer 17 Berlin Germany

ISBN: (纸本)9783031790584;9783031790591

Software services play a crucial role in daily life, with automated actions determining access to resources and information. Trusting service providers to perform these actions fairly and accurately is essential, yet challenging for users to verify. Even with publicly available codebases, the rapid pace of development and the complexity of modern deployments hinder the understanding and evaluation of service actions, including for experts. Hence, current trust models rely heavily on the assumption that service providers follow best practices and adhere to laws and regulations, which is increasingly impractical and risky, leading to undetected flaws and data leaks. In this paper, we argue that gathering verifiable evidence during software development and operations is needed for creating a new trust model. Therefore, we present TrustOps, an approach for continuously collecting verifiable evidence in all phases of the software life cycle, relying on and combining already existing tools and trust-enhancing technologies to do so. For this, we introduce the adaptable core principles of TrustOps and provide a roadmap for future research and development.

关键词： trustworthy software continuous software engineering evidence-based life cycle

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Analysis of the Strengths of Process Mining Techniques in the Optimization of Logistics Processes 14th

Analysis of the Strengths of Process Mining Techniques in th...

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14th International Symposium on business Modeling and Software Design (BMSD)

作者： Dolak, Radim Halaska, Michal Chmelicek, Jakub Silesian Univ Opava Sch Business Adm Karvina Karvina 73340 Czech Republic Co TP Ostrava 70900 Czech Republic

ISBN: (纸本)9783031640728;9783031640735

The paper analyzes the adequacy and strengths of Process Mining techniques for logistics process optimization. The theoretical part of the paper will mention the main benefits of utilizing Process Mining techniques for logistics processes. In this, we refer to related work and analyze theoretical aspects concerning ProcessMining techniques in general and their utilization for logistics processes. The practical part will be in the form of a case study. It will describe all necessary steps of Process Mining, such as the acquisition of data, preparing dataset, importing and analyzing data from a logistics software tool. The primary goal is to use Process Mining software based on the Disco tool to identify flaws and potentially improper operations in the chosen logistics process.

关键词： Process Mining Logistics BPM

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Study on the Path of Enhancing Agricultural Insurance Consumption in the Context of Digital Innovation Through fsQCA Method 1

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23rd Wuhan International Conference on E-business (WHICEB)

作者： Guo, Shuhang Yang, Jiapeng Cent Univ Finance & Econ Sch Informat Beijing 102200 Peoples R China

ISBN: (数字)9783031602603

ISBN: (纸本)9783031602597;9783031602603

The current demand for agricultural insurance is insufficient and of low quality. And in the current context of digital innovation, a new market ecology has been created, alleviating the original problems of information asymmetry, low financial availability and high transaction costs. It provides new opportunities to increase consumption of agricultural insurance. To address the question of what combination of conditions can increase agricultural insurance consumption, we focus on six influencing factors of agricultural insurance consumption. The fuzzy set qualitative comparative analysis method is used to identify the group state paths that promote the increase of agricultural insurance consumption in the context of digital innovation. Through configuration analysis, wederive three paths to increase agricultural insurance consumption: autonomy-driven, internally and externally driven, and externally technology-assisted. On this basis, we then put forward relevant policy recommendations with a view to protecting the healthy development of the agricultural economy by increasing agricultural insurance consumption.

关键词： Digital Innovation Agricultural Insurance Consumption Configuration Analysis

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A Survey on Perceptions of Data Sharing in the Norwegian Public Sector 14th

A Survey on Perceptions of Data Sharing in the Norwegian Pub...

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14th International Conference on Software business (ICSOB)

作者： Knutsen, Leif Z. Ngereja, Bertha Bjaaland, Ingebjorg Flaata Hannay, Jo E. Tanilkan, Sinan S. Simula Metropolitan Ctr Digital Engn Pb 4 St Olavs Plass N-0130 Oslo Norway Norwegian Comp Ctr Pb 114 Blindern N-0314 Oslo Norway

ISBN: (纸本)9783031532269;9783031532276

Sharing data among public institutions is essential for reaping the benefits of data-driven capabilities. Literature to date has identified several types of benefits that are likely to accrue to a wide range of sectors, as well as challenges and obstacles to implementing data-sharing solutions. We sought to identify perceptions of possible benefits, likely challenges, and the likelihood of overcoming them in the Norwegian public sector. Our survey of IT practitioners interested in the subject suggests that optimism about data sharing is high, concerns about a wide range of challenges are also high, and confidence in public institutions is tenuous. Responses also suggest that divisional management may be critical in implementing data sharing solutions. The pattern of responses suggests uncertainty consistent with low maturity in the field. We posit that data sharing among public institutions is part of a broader set of capabilities needed for public service innovation across institutions.

关键词： Data Sharing Public Sector Survey Digitalization

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ProtoNER: Few Shot Incremental Learning for Named Entity Recognition Using Prototypical Networks

ProtoNER: Few Shot Incremental Learning for Named Entity Rec...

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21st International Conference on business Process Management (BPM)

作者： Kumar, Ritesh Goyal, Saurabh Verma, Ashish Isahagian, Vatche IBM Res Cambridge MA 02142 USA

ISBN: (纸本)9783031509735;9783031509742

Key value pair (KVP) extraction or Named Entity Recognition (NER) from visually rich documents has been an active area of research in document understanding and data extraction domain. Several transformer based models such as LayoutLMv2 [1], LayoutLMv3 [2], and LiLT [3] have emerged achieving state of the art results. However, addition of even a single new class to the existing model requires (a) re-annotation of entire training dataset to include this new class and (b) retraining the model again. Both of these issues really slow down the deployment of updated model. We present ProtoNER: Prototypical Network based end-to-end KVP extraction model that allows addition of new classes to an existing model while requiring minimal number of newly annotated training samples. The key contributions of our model are: (1) No dependency on dataset used for initial training of the model, which alleviates the need to retain original training dataset for longer duration as well as data re-annotation which is very time consuming task, (2) No intermediate synthetic data generation which tends to add noise and results in model's performance degradation, and (3) Hybrid loss function which allows model to retain knowledge about older classes as well as learn about newly added classes. Experimental results show that ProtoNER finetuned with just 30 samples is able to achieve similar results for the newly added classes as that of regular model finetuned with 2600 samples.

关键词： business Document information Extraction Few Shot Class Incremental Learning (FSCIL) Named Entity Recognition (NER) Key Value Pair Extraction (KVP) Token Classification Prototypical Networks

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Unlocking Viewer Insights in Linear Television: A Machine Learning Approach 23rd

Unlocking Viewer Insights in Linear Television: A Machine Le...

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23rd International Conference on Perspectives in business Informatics Research (BIR)

作者： Carreno, Javier Khuong An Nguyen Luo, Zhiyuan Fish, Andrew Royal Holloway Univ London Egham TW20 0EX Surrey England Univ Liverpool Liverpool L69 3BX Merseyside England

ISBN: (纸本)9783031713323;9783031713330

Amidst the digital transformation, traditional linear TV faces major challenges, including fragmented viewership, fixed schedule, and inaccurate targeting. Therefore, this paper proposes a novel Machine Learning framework to understand the audience's demographics from their viewing behaviour. By employing state-of-the-art classification models on an extensive TV first-party dataset, we achieved an average 88.6% accuracy in correctly identifying each household demographics. Our result offers promising outcomes for refining strategies within linear TV to improve viewer engagement, content programming, and market insights.

关键词： Audience Insights Machine Learning Household Classification

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From Digital Tracks to Digital Twins: On the Path to Cross-Platform Profile Linking

From Digital Tracks to Digital Twins: On the Path to Cross-P...

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Enterprise Design, Operations, and Computing (EDOC) Forum / IDAMS Workshop / SoEA4EE Workshop / IRESEARCH Workshop / MIDas4CS Workshop

作者： Schultenkaemper, Sergej Baeumer, Frederik S. Bellgrau, Benjamin Lee, Yeong Su Geierhos, Michaela Bielefeld Univ Appl Sci & Arts Bielefeld Germany Univ Bundeswehr Munich Res Inst CODE Neubiberg Germany

ISBN: (纸本)9783031547119;9783031547126

In recent years, many studies have focused on correlating the profiles of real users across different social media. On the one hand, this provides a better overview of the user's social behavior;on the other hand, it can be used to warn of possible abuse through identity theft or cyberbullying. We try to make the threat on the Web predictable for the individual user by creating digital twins. To do this, it is important to use different data sources and to merge overlapping data across platforms. In this paper, we show that YouTube is a suitable entry point into the online social network for making connections between platforms, tracking user activity across platforms, and finally merging the collected profile information into an overall picture, the digital twin.

关键词： Profile Matching Identity Linkage Social Media

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Paving theWay for the Low-/No-Code Development of Digital Therapeutics: The DTxTAPP Framework

Paving theWay for the Low-/No-Code Development of Digital Th...

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Enterprise Design, Operations, and Computing (EDOC) Forum / IDAMS Workshop / SoEA4EE Workshop / IRESEARCH Workshop / MIDas4CS Workshop

作者： Weimann, Thure Georg Tech Univ Dresden Res Grp Digital Hlth Dresden Germany

ISBN: (纸本)9783031547119;9783031547126

Digital Therapeutics (DTx) are considered primarily patient-facing software applications that promise personalized behavioral treatments whenever and where needed. In recent years, the DTx market has seen strong growth due to reforms promoting the digital transformation of national healthcare systems, leading to competitive pressure among vendors and shorter times to market. To accelerate the development of DTx and deal with their complexity, so-called low/no-code platforms that build on the ideas of model-driven engineering havemoved into the focus of research and practice. However, research regarding the generic development ofDTxis still in its infancy, and prior contributions mainly focused on specific modeling and development aspects. Following a design science research approach, a synergized framework is derived based on previous conceptualizations to provide a holistic view on DTx intervention modeling. The proposed framework describes how the intervention workflow modeling can be systematically structured, may help to organize existing intervention knowledge, and provides the foundation for a technical instantiation. After demonstrating the framework, implications for future research and practice are discussed.

关键词： Digital health Intervention modeling Low-code development

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Generating Event Logs from Hybrid Process Models

Generating Event Logs from Hybrid Process Models

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21st International Conference on business Process Management (BPM)

作者： Alman, Anti Maggi, Fabrizio Maria Montali, Marco Rivkin, Andrey Univ Tartu Inst Comp Sci Tartu Estonia Free Univ Bozen Bolzano Bolzano Italy Tech Univ Denmark Inst Math & Comp Sci Lyngby Denmark

ISBN: (纸本)9783031509735;9783031509742

To carry on controlled experiments in process mining, it is necessary to generate event logs with specific characteristics. This has led to the development of log generation techniques in which a process model is simulated to generate an event log that is both compliant with the process model and also has certain user-defined properties (e.g., a certain number of traces, traces with certain lengths, etc.). Such techniques are available for a variety of modeling languages, both procedural and declarative. However, they are limited to simulating a single (procedural or declarative) process model at a time and do not allow simulating concurrent executions of multiple separate, but interacting, processes. In this paper, we introduce a log generation approach that takes multiple (procedural and declarative) process models (i.e., a Hybrid business Process Representation) as input and produces an event log matching the concurrent execution of these models on the same case instances. We discuss the details of our approach and evaluate its implementation.

关键词： Log Generation Process Simulation Hybrid Process Model Data Petri net Declare Finite State Automaton

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