2020 2nd International Conference on Process Mining (ICPM)最新文献_第2页

A Temporal Logic-Based Measurement Framework for Process Mining 基于时序逻辑的过程挖掘度量框架

2020 2nd International Conference on Process Mining (ICPM) Pub Date : 2020-10-01 DOI: 10.1109/ICPM49681.2020.00026

Alessio Cecconi, Giuseppe De Giacomo, Claudio Di Ciccio, F. Maggi, J. Mendling

{"title":"A Temporal Logic-Based Measurement Framework for Process Mining","authors":"Alessio Cecconi, Giuseppe De Giacomo, Claudio Di Ciccio, F. Maggi, J. Mendling","doi":"10.1109/ICPM49681.2020.00026","DOIUrl":"https://doi.org/10.1109/ICPM49681.2020.00026","url":null,"abstract":"The assessment of behavioral rules with respect to a given dataset is key in several research areas, including declarative process mining, association rule mining, and specification mining. The assessment is required to check how well a set of discovered rules describes the input data, as well as to determine to what extent data complies with predefined rules. In declarative process mining, in particular, some measures have been taken from association rule mining and adapted to support the assessment of temporal rules on event logs. Among them, support and confidence are used most often, yet they are reportedly unable to provide a sufficiently rich feedback to users and often cause spurious rules to be discovered from logs. In addition, these measures are designed to work on a predefined set of rules, thus lacking generality and extensibility. In this paper, we address this research gap by developing a general measurement framework for temporal rules based on Linear-time Temporal Logic with Past on Finite Traces (LTLpf). The framework is independent from the rule-specification language of choice and allows users to define new measures. We show that our framework can seamlessly adapt well-known measures of the association rule mining field to declarative process mining. Also, we test our software prototype implementing the framework on synthetic and real-world data, and investigate the properties characterizing those measures in the context of process analysis.","PeriodicalId":223427,"journal":{"name":"2020 2nd International Conference on Process Mining (ICPM)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123975680","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: 6

An Expert Lens on Data Quality in Process Mining 过程挖掘中数据质量的专家视角

2020 2nd International Conference on Process Mining (ICPM) Pub Date : 2020-10-01 DOI: 10.1109/ICPM49681.2020.00018

R. Andrews, Fahame F. Emamjome, A. T. Hofstede, H. Reijers

引用次数: 2

Using Multi-Level Information in Hierarchical Process Mining: Balancing Behavioural Quality and Model Complexity 多层次信息在分层过程挖掘中的应用:平衡行为质量和模型复杂性

2020 2nd International Conference on Process Mining (ICPM) Pub Date : 2020-10-01 DOI: 10.1109/ICPM49681.2020.00029

S. Leemans, Kanika Goel, S. V. Zelst

{"title":"Using Multi-Level Information in Hierarchical Process Mining: Balancing Behavioural Quality and Model Complexity","authors":"S. Leemans, Kanika Goel, S. V. Zelst","doi":"10.1109/ICPM49681.2020.00029","DOIUrl":"https://doi.org/10.1109/ICPM49681.2020.00029","url":null,"abstract":"Process mining techniques aim to derive knowledge of the execution of processes, by means of automated analysis of behaviour recorded in event logs. A well-known challenge in process mining is to strike an adequate balance between the behavioural quality of a discovered model compared to the event log and the model’s complexity as perceived by stakeholders. At the same time, events typically contain multiple attributes related to parts of the process at different levels of abstraction, which are often ignored by existing process mining techniques, resulting in either highly complex and/or incomprehensible process mining results. This paper addresses this problem by extending process mining to use event-level attributes readily available in event logs. We introduce (1) the concept of multi-level logs and generalise existing hierarchical process models, which support multiple modelling formalisms and notions of activities in a single model, (2) a framework, instantiation and implementation for process discovery of hierarchical models, and (3) a corresponding conformance checking technique. The resulting framework has been implemented as a plug-in of the open-source process mining framework ProM, and has been evaluated qualitatively and quantitatively using multiple real-life event logs.","PeriodicalId":223427,"journal":{"name":"2020 2nd International Conference on Process Mining (ICPM)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125255422","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: 10

Rule Mining with RuM 使用RuM进行规则挖掘

2020 2nd International Conference on Process Mining (ICPM) Pub Date : 2020-10-01 DOI: 10.1109/ICPM49681.2020.00027

Anti Alman, Claudio Di Ciccio, Dominik Haas, F. Maggi, Alexander Nolte

{"title":"Rule Mining with RuM","authors":"Anti Alman, Claudio Di Ciccio, Dominik Haas, F. Maggi, Alexander Nolte","doi":"10.1109/ICPM49681.2020.00027","DOIUrl":"https://doi.org/10.1109/ICPM49681.2020.00027","url":null,"abstract":"Declarative process modeling languages are especially suitable to model loosely-structured, unpredictable business processes. One of the most prominent of these languages is Declare. The Declare language can be used for all process mining branches and a plethora of techniques have been implemented to support process mining with Declare. However, using these techniques can become cumbersome in practical situations where different techniques need to be combined for analysis. In addition, the use of Declare constraints in practice is often hampered by the difficulty of modeling them: the formal expression of Declare is difficult to understand for users without a background in temporal logics, whereas its graphical notation has been shown to be unintuitive. In this paper, we present RuM, a novel application for rule mining that addresses the abovementioned issues by integrating multiple Declare-based process mining methods into a single unified application. The process mining techniques provided in RuM strongly rely on the use of Declare models expressed in natural language, which has the potential of mitigating the barriers of the language bias. The application has been evaluated by conducting a qualitative user evaluation with eight process analysts.","PeriodicalId":223427,"journal":{"name":"2020 2nd International Conference on Process Mining (ICPM)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127652706","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: 17

Conformance Checking Approximation Using Simulation 使用仿真的一致性检查近似

2020 2nd International Conference on Process Mining (ICPM) Pub Date : 2020-10-01 DOI: 10.1109/ICPM49681.2020.00025

M. Sani, Juan J. Garza Gonzalez, S. J. Zelst, Wil M.P. van der Aalst

引用次数: 5

Detecting System-Level Behavior Leading To Dynamic Bottlenecks 检测导致动态瓶颈的系统级行为

2020 2nd International Conference on Process Mining (ICPM) Pub Date : 2020-10-01 DOI: 10.1109/ICPM49681.2020.00014

Zahra Toosinezhad, Dirk Fahland, Özge Köroğlu, Wil M.P. van der Aalst

{"title":"Detecting System-Level Behavior Leading To Dynamic Bottlenecks","authors":"Zahra Toosinezhad, Dirk Fahland, Özge Köroğlu, Wil M.P. van der Aalst","doi":"10.1109/ICPM49681.2020.00014","DOIUrl":"https://doi.org/10.1109/ICPM49681.2020.00014","url":null,"abstract":"Dynamic bottlenecks occur when some cases in a particular part of the process are temporarily delayed. In performance-optimized systems such as production systems, warehouse automation systems, and baggage handling systems, such bottlenecks are rare, bounded in time and location, but costly when they occur and propagate through the system. Detecting and understanding the situations that cause such bottlenecks is crucial for mitigating and preventing processing delays. Classical process mining techniques that analyze performance along individual cases cannot detect these phenomena and their causes. We show that undesired system-level behavior can be detected when identifying temporal event patterns across different cases in the same process step. Conceptualizing these patterns as system-level events allows us to correlate them into cascades of system-level behavior using spatio-temporal conditions. We discover classes of frequent patterns in these cascades that describe behaviors that precede bottlenecks. Applied on event data of a major European airport, our approach could fully automatically detect cascades of undesired system-level behavior leading to dynamic bottlenecks. Each detected cascade was verified as a correct causal explanation for a dynamic bottleneck due to the physical system layout and its processing.","PeriodicalId":223427,"journal":{"name":"2020 2nd International Conference on Process Mining (ICPM)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124203965","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: 5

Collaborative and Interactive Detection and Repair of Activity Labels in Process Event Logs 过程事件日志中活动标签的协作和交互检测与修复

2020 2nd International Conference on Process Mining (ICPM) Pub Date : 2020-10-01 DOI: 10.1109/ICPM49681.2020.00017

Sareh Sadeghianasl, A. Hofstede, S. Suriadi, Selen Turkay

引用次数: 10

Time-aware Concept Drift Detection Using the Earth Mover’s Distance 利用推土机距离的时间感知概念漂移检测

2020 2nd International Conference on Process Mining (ICPM) Pub Date : 2020-10-01 DOI: 10.1109/ICPM49681.2020.00016

T. Brockhoff, M. S. Uysal, Wil M.P. van der Aalst

引用次数: 14

Design and Evaluation of a Process-aware Recommender System based on Prescriptive Analytics 基于规范分析的过程感知推荐系统设计与评价

2020 2nd International Conference on Process Mining (ICPM) Pub Date : 2020-10-01 DOI: 10.1109/ICPM49681.2020.00013

M. Leoni, M. Dees, Laurens Reulink

{"title":"Design and Evaluation of a Process-aware Recommender System based on Prescriptive Analytics","authors":"M. Leoni, M. Dees, Laurens Reulink","doi":"10.1109/ICPM49681.2020.00013","DOIUrl":"https://doi.org/10.1109/ICPM49681.2020.00013","url":null,"abstract":"Process-aware Recommender systems (PAR systems) are information systems that aim to monitor process executions, predict their outcome, and recommend effective interventions to reduce the risk of failure. While a PAR system is composed by monitoring, predictive analytics and prescriptive analytics, the lion’s share of attention in the recent years has been on the first two, overlooking the last. It seems that process participants are tacitly assumed to take the “right decision” for the most appropriate corrective actions in case of failure’s risks. Unfortunately, the assumption of selecting an effective corrective action is not always met in reality. When selecting an intervention, this is mainly based on human judgment, which naturally relies on subjective process’ perceptions, instead of objective facts. Experience has shown that, when a fact-based predictive analytics is followed by subjective prescriptive analytics, the positive effect of good predictions are nullffied by inconclusive corrective actions, yielding no final improvement. This paper discusses a PAR system that features a data-driven prescriptive analytics framework, which puts aside subjective options and focuses on factual data. The effectiveness of the proposed solution is assessed through the process of a reintegration company, showing a potential increase of customers that find a new job.","PeriodicalId":223427,"journal":{"name":"2020 2nd International Conference on Process Mining (ICPM)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114676334","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: 18

Discovery of Activities’ Actor Perspective from Emails based on Speech Acts Detection 基于语音行为检测的电子邮件活动行动者视角的发现

2020 2nd International Conference on Process Mining (ICPM) Pub Date : 2020-10-01 DOI: 10.1109/ICPM49681.2020.00021

Marwa Elleuch, Oumaima Alaoui Ismaili, N. Laga, Nour Assy, Walid Gaaloul

{"title":"Discovery of Activities’ Actor Perspective from Emails based on Speech Acts Detection","authors":"Marwa Elleuch, Oumaima Alaoui Ismaili, N. Laga, Nour Assy, Walid Gaaloul","doi":"10.1109/ICPM49681.2020.00021","DOIUrl":"https://doi.org/10.1109/ICPM49681.2020.00021","url":null,"abstract":"Process mining aims at discovering different perspectives of business processes (BP) from event logs generated by BP management systems. However, BP can be entirely or partially performed outside such systems. Emails are widely used as an alternative tool to collaboratively perform BP tasks. Recently, there have been several initiatives to extend the scope of BP mining to consider email logs. However, most of them have been mainly focused on discovering BP activity perspective neglecting other equally important knowledge. Actor perspective is one of the important knowledge that can ensure more understanding regarding individuals acting for performing BP activities. Mining such perspective from emails logs provides additional information about the precise contribution of actors in the execution of BP activities. Such information is not limited to executing activities but also refers to requesting, informing, planning or observing activities’ execution. This paper first formalizes the knowledge we may discover from emails related to actors perspectives. Then, it introduces an approach based on speech act detection from textual content of emails for discovering such knowledge. Our approach is validated using a public email dataset. Our results are publicly provided to be a first step towards ensuring reproductibility in the studied area.","PeriodicalId":223427,"journal":{"name":"2020 2nd International Conference on Process Mining (ICPM)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126405160","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: 6