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

Discovering Hierarchical Processes Using Flexible Activity Trees for Event Abstraction 使用灵活的活动树发现分层过程进行事件抽象

2020 2nd International Conference on Process Mining (ICPM) Pub Date : 2020-10-01 DOI: 10.1109/ICPM49681.2020.00030

Xixi Lu, A. Gal, H. Reijers

引用次数: 7

Events Put into Context (EPiC) 将事件放入上下文(EPiC)

2020 2nd International Conference on Process Mining (ICPM) Pub Date : 2020-10-01 DOI: 10.1109/ICPM49681.2020.00020

M. Dees, Bart F. A. Hompes, Wil M.P. van der Aalst

引用次数: 4

Process Mining over Unordered Event Streams 无序事件流的过程挖掘

2020 2nd International Conference on Process Mining (ICPM) Pub Date : 2020-10-01 DOI: 10.1109/ICPM49681.2020.00022

Ahmed Awad, M. Weidlich, S. Sakr

{"title":"Process Mining over Unordered Event Streams","authors":"Ahmed Awad, M. Weidlich, S. Sakr","doi":"10.1109/ICPM49681.2020.00022","DOIUrl":"https://doi.org/10.1109/ICPM49681.2020.00022","url":null,"abstract":"Process mining is no longer limited to the one-off analysis of static event logs extracted from a single enterprise system. Rather, process mining may strive for immediate insights based on streams of events that are continuously generated by diverse information systems. This requires online algorithms that, instead of keeping the whole history of event data, work incrementally and update analysis results upon the arrival of new events. While such online algorithms have been proposed for several process mining tasks, from discovery through conformance checking to time prediction, they all assume that an event stream is ordered, meaning that the order of event generation coincides with their arrival at the analysis engine. Yet, once events are emitted by independent, distributed systems, this assumption may not hold true, which compromises analysis accuracy. In this paper, we provide the first contribution towards handling unordered event streams in process mining. Specifically, we formalize the notion of out-of-order arrival of events, where an online analysis algorithm needs to process events in an order different from their generation. Using directly-follows graphs as a basic model for many process mining tasks, we provide two approaches to handle such unorderedness, either through buffering or speculative processing. Our experiments with synthetic and real-life event data show that these techniques help mitigate the accuracy loss induced by unordered streams.","PeriodicalId":223427,"journal":{"name":"2020 2nd International Conference on Process Mining (ICPM)","volume":"82 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":"134401926","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

Identifying and Reducing Errors in Remaining Time Prediction due to Inter-Case Dynamics 识别和减少由于案例间动态导致的剩余时间预测误差

2020 2nd International Conference on Process Mining (ICPM) Pub Date : 2020-10-01 DOI: 10.1109/ICPM49681.2020.00015

E. Klijn, Dirk Fahland

引用次数: 8

TOAD: Trace Ordering for Anomaly Detection 异常检测的跟踪排序

2020 2nd International Conference on Process Mining (ICPM) Pub Date : 2020-10-01 DOI: 10.1109/ICPM49681.2020.00033

Florian Richter, Yifeng Lu, Ludwig Zellner, Janina Sontheim, T. Seidl

{"title":"TOAD: Trace Ordering for Anomaly Detection","authors":"Florian Richter, Yifeng Lu, Ludwig Zellner, Janina Sontheim, T. Seidl","doi":"10.1109/ICPM49681.2020.00033","DOIUrl":"https://doi.org/10.1109/ICPM49681.2020.00033","url":null,"abstract":"Outlier detection is one of the most important tasks to keep your processes in control. Unawareness of critical anomalies can lead to exhausting expenses, hence, it is highly beneficial to treat process failures as soon as possible. However, anomalies are difficult to detect due to their rarity though they occur too often to neglect the necessity of its detection. Even if the detection problem is solved, the treatment of singular anomalies and the adjustment of the process based on each abnormal trace is tedious and costly regarding time and money. To increase the efficiency of later anomaly treatment, we propose a novel strategy to detect collective anomalies. However, this is not equivalent to anomaly clustering as a post-processing step. TOAD orders process instances by similarity and detects abnormal accumulations of deviating cases. These collections are abnormal due to their aggregated behavior. Assuming that similar deviations are caused by the same reason, the treatment of such an anomaly is more cost-efficient than the handling of deviating singletons. Applying TOAD to an event log yields a ranking of significant, temporally abnormal trace collections, that provide a baseline for further analysis.","PeriodicalId":223427,"journal":{"name":"2020 2nd International Conference on Process Mining (ICPM)","volume":"19 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":"131424986","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

Anomaly Detection on Event Logs with a Scarcity of Labels 标签稀缺事件日志的异常检测

2020 2nd International Conference on Process Mining (ICPM) Pub Date : 2020-10-01 DOI: 10.1109/ICPM49681.2020.00032

Sylvio Barbon Junior, P. Ceravolo, E. Damiani, Nicolas Jashchenko Omori, G. Tavares

引用次数: 7

Queueing Inference for Process Performance Analysis with Missing Life-Cycle Data 缺少生命周期数据的过程性能分析的排队推理

2020 2nd International Conference on Process Mining (ICPM) Pub Date : 2020-10-01 DOI: 10.1109/ICPM49681.2020.00019

Guy Berkenstadt, A. Gal, Arik Senderovich, Roee Shraga, M. Weidlich

引用次数: 5

Classifying process deviations with weak supervision 在监督不力的情况下对工艺偏差进行分类

2020 2nd International Conference on Process Mining (ICPM) Pub Date : 2020-10-01 DOI: 10.1109/ICPM49681.2020.00023

Manal Laghmouch, Mieke Jans, B. Depaire

{"title":"Classifying process deviations with weak supervision","authors":"Manal Laghmouch, Mieke Jans, B. Depaire","doi":"10.1109/ICPM49681.2020.00023","DOIUrl":"https://doi.org/10.1109/ICPM49681.2020.00023","url":null,"abstract":"Although conformance checking is great at detecting process deviations, it still poses challenges that hinders adoption in auditing practice. A major challenge is that in real life a large number of deviating cases is often detected of which only a small amount are true anomalies and thus of real interest to auditors. The number of deviations are often too large to inspect one by one, which explains why auditing requires a sample-based approach. This paper contributes to the research on the practical feasibility of continuous auditing and studies the potential of weak supervision to classify deviations into anomalies and exceptions, allowing auditors to do a full-population analysis of the identified deviations. The Snorkel framework is applied which uses a set of imperfect domain expert rules to classify the set of deviations into anomalies and exceptions. A controlled and artificial experiment has been set up to explore the relation between the performance of this approach and the number and quality of domain expert rules. The results demonstrate the potential of this approach as a limited number of medium to high quality domain expert rules succeeds to classify deviations with acceptable accuracy.","PeriodicalId":223427,"journal":{"name":"2020 2nd International Conference on Process Mining (ICPM)","volume":"2 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":"130283614","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

A Generic Framework for Trace Clustering in Process Mining 过程挖掘中跟踪聚类的通用框架

2020 2nd International Conference on Process Mining (ICPM) Pub Date : 2020-10-01 DOI: 10.1109/ICPM49681.2020.00034

F. Zandkarimi, Jana-Rebecca Rehse, Pouya Soudmand, Hartmut Hoehle

引用次数: 15

Conference Organization 会议组织

2020 2nd International Conference on Process Mining (ICPM) Pub Date : 2020-10-01 DOI: 10.1109/ICAC.2005.66

Zuwairie Ibrahim, Shukri Zainal Abidin, M. Mustafa, J. Habibi, Sadiq Hussain, Taha Osman, A. Selamat, R. K. Subramanian, R. Ayani, Y. M. Teo, Kamalrulnizam Abu-Bakar, Irfan Awan Asfand-E-Yar, Norasyikin Khamis, Z. Yusof, Suiping Zhou, H. Haron, A. Abraham, Patrick Wong, M. Othman, S. Shamsuddin, Russell Cheng, M. Ngadiman, N. Safri, J. Yunus

{"title":"Conference Organization","authors":"Zuwairie Ibrahim, Shukri Zainal Abidin, M. Mustafa, J. Habibi, Sadiq Hussain, Taha Osman, A. Selamat, R. K. Subramanian, R. Ayani, Y. M. Teo, Kamalrulnizam Abu-Bakar, Irfan Awan Asfand-E-Yar, Norasyikin Khamis, Z. Yusof, Suiping Zhou, H. Haron, A. Abraham, Patrick Wong, M. Othman, S. Shamsuddin, Russell Cheng, M. Ngadiman, N. Safri, J. Yunus","doi":"10.1109/ICAC.2005.66","DOIUrl":"https://doi.org/10.1109/ICAC.2005.66","url":null,"abstract":"Technical Program Committee Samaila Umaru, Ahmadu Bello University, Nigeria Yi Deng, Hunan Institute of Engineering, China Yulin Wang, Wuhan University, China Junzheng Li, Hunan Agricultural University, China Xia Liu, Hunan Normal University, China Zengfu Deng, Hunan Normal University, China Zhang Yang, Fujian University of Technology, China Chengcheng Yao, The Ohio state University, USA Yifen Li, Central South University, China Jiajia Li, Changsha University of Science and Technology, China Feng Hou, University of Illinois at Urbana-Champaign, USA Shengxiang Deng, Central South University, China Xun Wu, Tongji University, China Fangxiong Xiao, Jinling Institute of Technology, China Xiangqian Fan, Nanjing University, China Weizhen Chen, Tongji University, China Yaling zheng, Hangzhou Dianzi University, China Minglei Song, Henan University of Urban Construction, China Mingqi Sun, Harbin University of Science and Technology, China Srikanta Patnaik, SOA University, Bhubaneswar, India Ranjan Kumar Mallick, SOA University, Bhubaneswar, India Xiaozhou Hu, Central South University, China Huaping Liu, Tsinghua University, China Yucai Zhou, Changsha University of Science and Technology, China Hamilton Ostum, University of Arizona Tucson, USA Donovan Young, Marshall University, Canada","PeriodicalId":223427,"journal":{"name":"2020 2nd International Conference on Process Mining (ICPM)","volume":"43 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":"116602414","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