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Robust Drift Characterization from Event Streams of Business Processes

Alireza Ostovar, Sander J. J. Leemans, Marcello La Rosa
2020 ACM Transactions on Knowledge Discovery from Data  
To address the above limitations, this paper proposes a robust, automated method for charactering process drifts from event streams.  ...  As such, a number of techniques have been developed to detect process drifts, i.e. statistically significant changes in process behavior, from process event logs (offline) or event streams (online).  ...  ACKNOWLEDGMENTS We thank Abderrahmane Maaradji for his feedback on early versions of this paper. This research is partly funded by the Australian Research Council (grant DP150103356).  ... 
doi:10.1145/3375398 fatcat:34qw57v4pveuziq4g5sjbqhx3e

A Survey on Concept Drift in Process Mining

Denise Maria Vecino Sato, Sheila Cristiana De Freitas, Jean Paul Barddal, Edson Emilio Scalabrin
2022 ACM Computing Surveys  
Concept drift in process mining (PM) is a challenge as classical methods assume processes are in a steady-state, i.e., events share the same process version.  ...  Existing works depict that (i) PM still primarily focuses on offline analysis, and (ii) the assessment of concept drift techniques in processes is cumbersome due to the lack of common evaluation protocol  ...  ACKNOWLEDGMENTS The authors would like to thank the support and funding of this research by CAPES Coordenação de Aperfeiçoamento de Pessoal de Nível Superior -Brasil (CAPES) -Finance Code 001 under Grant  ... 
doi:10.1145/3472752 fatcat:zay6purz35bsfejb3zdpn3yb4m

A Robust and Accurate Approach to Detect Process Drifts from Event Streams [article]

Yang Lu, Qifan Chen, Simon Poon
2021 arXiv   pre-print
Business processes are bound to evolve as a form of adaption to changes, and such changes are referred as process drifts.  ...  The proposed method can accurately detect drift points from event logs and is robust to noises. Both artificial and real-life event logs are used to evaluate our method.  ...  We focus on offline process drift detection from the control-flow perspective. We propose an event-stream based process drift detection method which is accurate, robust to noise and reasonably fast.  ... 
arXiv:2103.10749v3 fatcat:iwchmlazxbcd3o5av6fy7dxtra

Drift Detection Analytics for IoT Sensors

Sathyan Munirathinam
2021 Procedia Computer Science  
Manufacturing operation team could establish a new business process to respond to the early drift alarms by using quality shift left approach.  ...  Manufacturing operation team could establish a new business process to respond to the early drift alarms by using quality shift left approach.  ...  This study aims to address the concept drift, which is a major challenge in the processing of voluminous data streams.  ... 
doi:10.1016/j.procs.2021.01.341 fatcat:4to5zz7eaffslnd2xezpzo63vy

CONDA-PM—A Systematic Review and Framework for Concept Drift Analysis in Process Mining

Ghada Elkhawaga, Mervat Abuelkheir, Sherif I. Barakat, Alaa M. Riad, Manfred Reichert
2020 Algorithms  
This requires continuous monitoring of business processes to gain insights into whether they conform to the intended design or deviate from it.  ...  Despite its importance, there exists no comprehensive framework for analysing concept drift types, affected process perspectives, and granularity levels of a business process.  ...  Concept Drift and Process Drift In [9] , the authors define business process drift detection as "a family of techniques to analyse event logs or event streams generated during the execution of a business  ... 
doi:10.3390/a13070161 fatcat:jne6xqmz3fbpzfsx56laixop4i

Incremental Predictive Process Monitoring: How to Deal with the Variability of Real Environments [article]

Chiara Di Francescomarino, Chiara Ghidini, Fabrizio Maria Maggi, Williams Rizzi, Cosimo Damiano Persia
2018 arXiv   pre-print
A characteristic of existing predictive process monitoring techniques is to first construct a predictive model based on past process executions, and then use it to predict the future of new ongoing cases  ...  The results provide a first evidence of the potential of incremental learning strategies for predicting process monitoring in real environments, and of the impact of different case encoding strategies  ...  Acknowledgements We would like to thank Matthias Weidlich and Marco Maisenbacher for providing us with the concept drift benchmark logs.  ... 
arXiv:1804.03967v1 fatcat:2436ejsinfhqlb6mmq3fob2u3a

Special issue on the best papers of SDM'09

Zoran Obradovic, Huan Liu
2009 Statistical analysis and data mining  
An event in time series is characterized by an interval of measurements that differs significantly from a baseline.  ...  a data stream and propose the first streaming algorithm for estimating the DCS of a stream using limited memory.  ... 
doi:10.1002/sam.10056 fatcat:6h3ihjqgyfhrnb3v2ssnnud62q

Optimization and Prediction Techniques for Self-Healing and Self-Learning Applications in a Trustworthy Cloud Continuum

Juncal Alonso, Leire Orue-Echevarria, Eneko Osaba, Jesús López Lobo, Iñigo Martinez, Josu Diaz de Arcaya, Iñaki Etxaniz
2021 Information  
In this paper, we analyze how artificial intelligence (AI)-based techniques and tools can enhance the operation of complex applications to support the broad and multi-stage heterogeneity of the infrastructural  ...  In the "traditional" cloud, computing resources are typically homogeneous in order to facilitate economies of scale.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/info12080308 fatcat:744byhtgsbhlnd4pvtnc5dzrym

Semantic Scan: Detecting Subtle, Spatially Localized Events in Text Streams [article]

Abhinav Maurya, Kenton Murray, Yandong Liu, Chris Dyer, William W. Cohen, Daniel B. Neill
2016 arXiv   pre-print
This enables more timely and accurate detection and characterization of anomalous, spatially localized emerging events.  ...  However, these methods have numerous shortcomings that make them unsuitable for rapid detection of locally emerging events on massive text streams.  ...  INTRODUCTION Text streams are ubiquitous in data processing and knowledge discovery workflows.  ... 
arXiv:1602.04393v1 fatcat:oz6jkowgjfcpdklpgzjunjsuoa

Strategic Drift and Strategic Crisis Management of Organization

2014 China-USA Business Review  
of strategic crisis management and its implementation in business.  ...  The research questions whose solution is sought are connected with the relation of strategic drift-strategic crisis-strategic crisis management, in terms of whether the errors in the process of strategic  ...  again is connected with notions from the methodology of the strategic management, and later also with the process of management of crises.  ... 
doi:10.17265/1537-1514/2014.07.006 fatcat:qvauylethrhktmxv7m3vlya7ne

The Lambda and the Kappa

Jimmy Lin
2017 IEEE Internet Computing  
they enable the decoupling of the pendulum at different parts of the stack, thus allowing their motions to drift out of phase.  ...  In the kappa architecture, everything's a stream. And if everything's a stream, all you need is a stream processing engine.  ...  Protect Your Organization from Hackers by Thinking Like Them Take Our E-Learning Courses in the Art of Hacking You and your staff can take these courses where you are and at your own pace, getting hands-on  ... 
doi:10.1109/mic.2017.3481351 fatcat:muhgt2udcba7tob5viooipyvbe

Pattern Discovery of User Interface Sequencing by Rehabilitation Clients with Cognitive Impairments

William N. Robinson, Ali Raza Syed, Arash Akhlaghi, Tianjie Deng
2012 2012 45th Hawaii International Conference on System Sciences  
This paper introduces theory and design of stream sequence-mining for UI event streams. 45th Hawaii International Conference on System Sciences 978-0-7695-4525-7/12 $26.00  ...  Thus, monitoring software usage, particularly email event sequences, is important.  ...  Process Mining Process mining extracts information from event logs to derive business-process descriptions [31] .  ... 
doi:10.1109/hicss.2012.467 dblp:conf/hicss/RobinsonSAD12 fatcat:uofjctlt65c7rcl57z6575owji

Labelling Drifts in a Fault Detection System for Wind Turbine Maintenance [chapter]

Iñigo Martinez, Elisabeth Viles, Iñaki Cabrejas
2018 Studies in Computational Intelligence  
characterization of the existent drifts.  ...  Such concept drift events are desired to be detected by means of statistical detectors and window-based approaches.  ...  Acknowledgements This research has been supported by NEM Solutions, a technology-based company focused that provides intelligent maintenance of complex systems to O&M businesses.  ... 
doi:10.1007/978-3-319-99626-4_13 fatcat:77ovqk34hvda5a3qnrrziqpxmi

A survey on concept drift adaptation

João Gama, Indrė Žliobaitė, Albert Bifet, Mykola Pechenizkiy, Abdelhamid Bouchachia
2014 ACM Computing Surveys  
Assuming a general knowledge of supervised learning in this paper we characterize adaptive learning process, categorize existing strategies for handling concept drift, discuss the most representative,  ...  The survey aims at covering the different facets of concept drift in an integrated way to reflect on the existing scattered state-of-the-art.  ...  Thus, in Process Mining 5 [van der Aalst 2012; 2011], the area of research dealing with the different kinds of analyses of (business) processes by extracting information from event logs recorded by an  ... 
doi:10.1145/2523813 fatcat:hmocrqsq5rgnhe6udanangvija

Data science and AI in FinTech: An overview [article]

Longbing Cao, Qiang Yang, Philip S. Yu
2021 arXiv   pre-print
Smart FinTech synthesizes broad DSAI and transforms finance and economies to drive intelligent, automated, whole-of-business and personalized economic and financial businesses, services and systems.  ...  Here, we present a highly dense research overview of smart financial businesses and their challenges, the smart FinTech ecosystem, the DSAI techniques to enable smart FinTech, and some research directions  ...  in stochastic processes; distributional stream drifts and concept changes in business transactions; and imbalanced learning.  ... 
arXiv:2007.12681v2 fatcat:jntzuwaktjg2hmmjypi5lvyht4
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