Learning and Recognizing Routines and Activities in SOFiA

Carolis, Berardina De; Ferilli, Stefano; Mallardi, Giulio

doi:10.1007/978-3-319-14112-1_16

Cited by 3 publications

(2 citation statements)

References 13 publications

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“…This denotes a shift in the target audience of these methods, from data scientists to practitioners, such as business analysts and decision managers. Other technical languages employed, besides Petri nets, include Causal nets, State machines and simple Directed Acyclic Graphs, Maggi et al [26] 2012 [27], [28], [29], [30], [31], [32] Declarative Declare ProM MINERful Di Ciccio, Mecella [33] 2013 [34], [35], [36], [37] Declarative Declare ProM, Standalone Inductive Miner -Infrequent Leemans et al [38] 2013 [39], [40], [41], [42], [43], [44], [ Ferilli [56] 2014 [57], [58], [59], [60], [ while Declare is the most commonly-used language when producing declarative models.…”

Section: Model Type and Language (Rq2)mentioning

confidence: 99%

“…The WoMan framework, proposed by Ferilli in [56] and further extended in the related studies [57], [58], [59], [60], [61], includes a method to learn and refine process models from event logs, by discovering first-order logic constraints. It guarantees incrementality in learning and adapting the models, the ability to express triggers and conditions on the process tasks and efficiency.…”

Section: State Machinesmentioning

confidence: 99%

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Automated Discovery of Process Models from Event Logs: Review and Benchmark

Augusto

Conforti

Dumas

et al. 2019

IEEE Trans. Knowl. Data Eng.

312

266

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Process mining allows analysts to exploit logs of historical executions of business processes to extract insights regarding the actual performance of these processes. One of the most widely studied process mining operations is automated process discovery. An automated process discovery method takes as input an event log, and produces as output a business process model that captures the control-flow relations between tasks that are observed in or implied by the event log. Various automated process discovery methods have been proposed in the past two decades, striking different tradeoffs between scalability, accuracy and complexity of the resulting models. However, these methods have been evaluated in an ad-hoc manner, employing different datasets, experimental setups, evaluation measures and baselines, often leading to incomparable conclusions and sometimes unreproducible results due to the use of closed datasets. This article provides a systematic review and comparative evaluation of automated process discovery methods, using an open-source benchmark and covering twelve publicly-available real-life event logs, twelve proprietary real-life event logs, and nine quality metrics. The results highlight gaps and unexplored tradeoffs in the field, including the lack of scalability of some methods and a strong divergence in their performance with respect to the different quality metrics used.

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Section: Model Type and Language (Rq2)mentioning

confidence: 99%

Section: State Machinesmentioning

confidence: 99%

Automated Discovery of Process Models from Event Logs: Review and Benchmark

Augusto

Conforti

Dumas

et al. 2019

IEEE Trans. Knowl. Data Eng.

312

266

View full text Add to dashboard Cite

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A Survey on the Application of Process Mining to Smart Spaces Data

Bertrand

Abbeele

Veneruso

et al. 2023

Lecture Notes in Business Information Processing

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During the last years, a number of studies have experimented with applying process mining (PM) techniques to smart spaces data. The general goal has been to automatically model human routines as if they were business processes. However, applying process-oriented techniques to smart spaces data comes with its own set of challenges. This paper surveys existing approaches that apply PM to smart spaces and analyses how they deal with the following challenges identified in the literature: choosing a modelling formalism for human behaviour; bridging the abstraction gap between sensor and event logs; and segmenting logs in traces. The added value of this article lies in providing the research community with a common ground for some important challenges that exist in this field and their respective solutions, and to assist further research efforts by outlining opportunities for future work.

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