International Conference on Advanced Information Systems Engineering最新文献

PMDG: Privacy for Multi-Perspective Process Mining through Data Generalization PMDG:基于数据泛化的多角度过程挖掘的隐私

International Conference on Advanced Information Systems Engineering Pub Date : 2023-05-01 DOI: 10.48550/arXiv.2305.00960

Ryan Hildebrant, Stephan A. Fahrenkrog-Petersen, M. Weidlich, Shangping Ren

{"title":"PMDG: Privacy for Multi-Perspective Process Mining through Data Generalization","authors":"Ryan Hildebrant, Stephan A. Fahrenkrog-Petersen, M. Weidlich, Shangping Ren","doi":"10.48550/arXiv.2305.00960","DOIUrl":"https://doi.org/10.48550/arXiv.2305.00960","url":null,"abstract":"Anonymization of event logs facilitates process mining while protecting sensitive information of process stakeholders. Existing techniques, however, focus on the privatization of the control-flow. Other process perspectives, such as roles, resources, and objects are neglected or subject to randomization, which breaks the dependencies between the perspectives. Hence, existing techniques are not suited for advanced process mining tasks, e.g., social network mining or predictive monitoring. To address this gap, we propose PMDG, a framework to ensure privacy for multi-perspective process mining through data generalization. It provides group-based privacy guarantees for an event log, while preserving the characteristic dependencies between the control-flow and further process perspectives. Unlike existin privatization techniques that rely on data suppression or noise insertion, PMDG adopts data generalization: a technique where the activities and attribute values referenced in events are generalized into more abstract ones, to obtain equivalence classes that are sufficiently large from a privacy point of view. We demonstrate empirically that PMDG outperforms state-of-the-art anonymization techniques, when mining handovers and predicting outcomes.","PeriodicalId":321309,"journal":{"name":"International Conference on Advanced Information Systems Engineering","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122395011","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CREATED: Generating Viable Counterfactual Sequences for Predictive Process Analytics 创建:为预测过程分析生成可行的反事实序列

International Conference on Advanced Information Systems Engineering Pub Date : 2023-03-28 DOI: 10.48550/arXiv.2303.15844

Olusanmi Hundogan, Xixi Lu, Yupei Du, H. Reijers

{"title":"CREATED: Generating Viable Counterfactual Sequences for Predictive Process Analytics","authors":"Olusanmi Hundogan, Xixi Lu, Yupei Du, H. Reijers","doi":"10.48550/arXiv.2303.15844","DOIUrl":"https://doi.org/10.48550/arXiv.2303.15844","url":null,"abstract":"Predictive process analytics focuses on predicting future states, such as the outcome of running process instances. These techniques often use machine learning models or deep learning models (such as LSTM) to make such predictions. However, these deep models are complex and difficult for users to understand. Counterfactuals answer ``what-if'' questions, which are used to understand the reasoning behind the predictions. For example, what if instead of emailing customers, customers are being called? Would this alternative lead to a different outcome? Current methods to generate counterfactual sequences either do not take the process behavior into account, leading to generating invalid or infeasible counterfactual process instances, or heavily rely on domain knowledge. In this work, we propose a general framework that uses evolutionary methods to generate counterfactual sequences. Our framework does not require domain knowledge. Instead, we propose to train a Markov model to compute the feasibility of generated counterfactual sequences and adapt three other measures (delta in outcome prediction, similarity, and sparsity) to ensure their overall viability. The evaluation shows that we generate viable counterfactual sequences, outperform baseline methods in viability, and yield similar results when compared to the state-of-the-art method that requires domain knowledge.","PeriodicalId":321309,"journal":{"name":"International Conference on Advanced Information Systems Engineering","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115848864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Learning When to Treat Business Processes: Prescriptive Process Monitoring with Causal Inference and Reinforcement Learning 学习何时处理业务流程:基于因果推理和强化学习的规定性流程监控

International Conference on Advanced Information Systems Engineering Pub Date : 2023-03-07 DOI: 10.48550/arXiv.2303.03572

Z. Bozorgi, M. Dumas, M. Rosa, Artem Polyvyanyy, M. Shoush, Irene Teinemaa

{"title":"Learning When to Treat Business Processes: Prescriptive Process Monitoring with Causal Inference and Reinforcement Learning","authors":"Z. Bozorgi, M. Dumas, M. Rosa, Artem Polyvyanyy, M. Shoush, Irene Teinemaa","doi":"10.48550/arXiv.2303.03572","DOIUrl":"https://doi.org/10.48550/arXiv.2303.03572","url":null,"abstract":"Increasing the success rate of a process, i.e. the percentage of cases that end in a positive outcome, is a recurrent process improvement goal. At runtime, there are often certain actions (a.k.a. treatments) that workers may execute to lift the probability that a case ends in a positive outcome. For example, in a loan origination process, a possible treatment is to issue multiple loan offers to increase the probability that the customer takes a loan. Each treatment has a cost. Thus, when defining policies for prescribing treatments to cases, managers need to consider the net gain of the treatments. Also, the effect of a treatment varies over time: treating a case earlier may be more effective than later in a case. This paper presents a prescriptive monitoring method that automates this decision-making task. The method combines causal inference and reinforcement learning to learn treatment policies that maximize the net gain. The method leverages a conformal prediction technique to speed up the convergence of the reinforcement learning mechanism by separating cases that are likely to end up in a positive or negative outcome, from uncertain cases. An evaluation on two real-life datasets shows that the proposed method outperforms a state-of-the-art baseline.","PeriodicalId":321309,"journal":{"name":"International Conference on Advanced Information Systems Engineering","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129696822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Why am I Waiting? Data-Driven Analysis of Waiting Times in Business Processes 为什么我还在等待?业务流程中等待时间的数据驱动分析

International Conference on Advanced Information Systems Engineering Pub Date : 2022-12-02 DOI: 10.48550/arXiv.2212.01392

Katsiaryna Lashkevich, Fredrik P. Milani, David Chapela-Campa, Ihar Suvorau, M. Dumas

引用次数: 1

Probabilistic and Non-deterministic Event Data in Process Mining: Embedding Uncertainty in Process Analysis Techniques 过程挖掘中的概率和非确定性事件数据:在过程分析技术中嵌入不确定性

International Conference on Advanced Information Systems Engineering Pub Date : 2022-05-10 DOI: 10.48550/arXiv.2205.04827

Marco Pegoraro

引用次数: 2

Situation Awareness for Autonomous Vehicles Using Blockchain-based Service Cooperation 基于区块链服务合作的自动驾驶车辆态势感知

International Conference on Advanced Information Systems Engineering Pub Date : 2022-04-07 DOI: 10.48550/arXiv.2204.03313

H. Nguyen, Tri Nguyen, T. Leppänen, Juha Partala, S. Pirttikangas

{"title":"Situation Awareness for Autonomous Vehicles Using Blockchain-based Service Cooperation","authors":"H. Nguyen, Tri Nguyen, T. Leppänen, Juha Partala, S. Pirttikangas","doi":"10.48550/arXiv.2204.03313","DOIUrl":"https://doi.org/10.48550/arXiv.2204.03313","url":null,"abstract":"Efficient Vehicle-to-Everything enabling cooperation and enhanced decision-making for autonomous vehicles is essential for optimized and safe traffic. Real-time decision-making based on vehicle sensor data, other traffic data, and environmental and contextual data becomes imperative. As a part of such Intelligent Traffic Systems, cooperation between different stakeholders needs to be facilitated rapidly, reliably, and securely. The Internet of Things provides the fabric to connect these stakeholders who share their data, refined information, and provided services with each other. However, these cloud-based systems struggle to meet the real-time requirements for smart traffic due to long distances across networks. Here, edge computing systems bring the data and services into the close proximity of fast-moving vehicles, reducing information delivery latencies and improving privacy as sensitive data is processed locally. To solve the issues of trust and latency in data sharing between these stakeholders, we propose a decentralized framework that enables smart contracts between traffic data producers and consumers based on blockchain. Autonomous vehicles connect to a local edge server, share their data, or use services based on agreements, for which the cooperating edge servers across the system provide a platform. We set up proof-of-concept experiments with Hyperledger Fabric and virtual cars to analyze the system throughput with secure unicast and multicast data transmissions. Our results show that multicast transmissions in such a scenario boost the throughput up to 2.5 times where the data packets of different sizes can be transmitted in less than one second.","PeriodicalId":321309,"journal":{"name":"International Conference on Advanced Information Systems Engineering","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114604828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Soundness of Data-Aware Processes with Arithmetic Conditions 具有算术条件的数据感知过程的可靠性

International Conference on Advanced Information Systems Engineering Pub Date : 2022-03-28 DOI: 10.48550/arXiv.2203.14809

Paolo Felli, M. Montali, S. Winkler

{"title":"Soundness of Data-Aware Processes with Arithmetic Conditions","authors":"Paolo Felli, M. Montali, S. Winkler","doi":"10.48550/arXiv.2203.14809","DOIUrl":"https://doi.org/10.48550/arXiv.2203.14809","url":null,"abstract":". Data-aware processes represent and integrate structural and behavioural constraints in a single model, and are thus increasingly inves-tigated in business process management and information systems engineering. In this spectrum, Data Petri nets (DPNs) have gained increasing popularity thanks to their ability to balance simplicity with expressive-ness. The interplay of data and control-ﬂow makes checking the correctness of such models, speciﬁcally the well-known property of soundness, crucial and challenging. A major shortcoming of previous approaches for checking soundness of DPNs is that they consider data conditions without arithmetic, an essential feature when dealing with real-world, concrete applications. In this paper, we attack this open problem by providing a foundational and operational framework for assessing soundness of DPNs enriched with arithmetic data conditions. The framework comes with a proof-of-concept implementation that, instead of relying on ad-hoc techniques, employs oﬀ-the-shelf established SMT technologies. The implementation is validated on a collection of examples from the literature, and on synthetic variants constructed from such examples.","PeriodicalId":321309,"journal":{"name":"International Conference on Advanced Information Systems Engineering","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126496637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Bootstrapping Generalization of Process Models Discovered From Event Data 从事件数据中发现过程模型的自举泛化

International Conference on Advanced Information Systems Engineering Pub Date : 2021-07-08 DOI: 10.1007/978-3-031-07472-1_3

Artem Polyvyanyy, Alistair Moffat, Luciano Garc'ia-Banuelos

引用次数: 5

P-SGD: A Stochastic Gradient Descent Solution for Privacy-Preserving During Protection Transitions 保护过渡期间隐私保护的随机梯度下降解P-SGD

International Conference on Advanced Information Systems Engineering Pub Date : 2021-06-28 DOI: 10.1007/978-3-030-79382-1_3

Karam Bou Chaaya, R. Chbeir, M. Barhamgi, Philippe Arnould, D. Benslimane

引用次数: 0

Learning Accurate Business Process Simulation Models from Event Logs via Automated Process Discovery and Deep Learning 通过自动化流程发现和深度学习从事件日志中学习准确的业务流程模拟模型

International Conference on Advanced Information Systems Engineering Pub Date : 2021-03-22 DOI: 10.1007/978-3-031-07472-1_4

Manuel Camargo, M. Dumas, Oscar González Rojas

引用次数: 10