A knowledge-intensive approach to process similarity calculation

Montani, Stefania; Leonardi, Giorgio; Quaglini, Silvana; Cavallini, Anna; Micieli, Giuseppe

doi:10.1016/j.eswa.2015.01.027

Cited by 27 publications

(16 citation statements)

References 31 publications

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“…In particular, we plan to mine the actual process implemented locally at the admission center, in order to assess its quality and identify possible malpractices and limitations through proper comparisons with reference guidelines and stabilization best practices. We will define novel metrics to enable process comparison, by extending works in the area of graph similarity calculation [9], in order to realize a knowledge-intensive and semantic approach, along the lines we described in [10].…”

Section: Discussionmentioning

confidence: 99%

A telemedicine support for improving medical emergency management

Canonico¹,

Montani²,

Striani³

2017

ICST Transactions on Ambient Systems

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In this paper, we introduce a telemedicine architecture for supporting emergency patient stabilization and patient transportation to a fully equipped health care center. In particular, we focus on the description of a set of mobile apps, designed for supporting data recording and transmission during patient transportation by ambulance. Some of the apps are interfaced to the monitoring devices in the ambulance, and automatically send all the recorded data to a server at the destination center. One additional app enables the travelling personnel to input and transmit further significant patient data, or comments. At the destination center, the specialist physician is allowed to inspect the data as soon as they are received, possibly providing immediate advice. The exploitation of the apps also allows to maintain the transportation data over time, for medico-legal purposes, or to perform a-posteriori analyses. Some first evaluation results are discussed in the paper.

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Section: Discussionmentioning

confidence: 99%

A telemedicine support for improving medical emergency management

Canonico¹,

Montani²,

Striani³

2017

ICST Transactions on Ambient Systems

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“…It is worth noting that all of these works, including our own previous contribution (Montani et al, 2015), are however only loosely related to the present paper, since they do not focus on traces, but on process models (i.e., graphs), and do not aim at enabling operational support.…”

Section: Related Workmentioning

confidence: 99%

“…The works in Bergmann & Gil (2014); Montani et al (2015), on the other hand, exploit semantic information in activity comparison. In Bergmann & Gil (2014), a system working on workflows represented as semantically labeled graphs is presented.…”

Section: Related Workmentioning

confidence: 99%

“…The work is particularly focused on the data flow. The work in Montani et al (2015) exploits domain knowledge and temporal information to parametrize the cost of node substitutions and edge substitution. As in LaRosa et al (2013), gateway nodes are also considered.…”

Section: Related Workmentioning

confidence: 99%

“…These works have required the introduction of different metrics for graph comparison. Most of them are based on extensions of the graph edit distance (Minor et al, 2008;Bergmann & Gil, 2014;Kunze & Weske, 2011;Li et al, 2008;Montani et al, 2015;Dijkman et al, 2009;LaRosa et al, 2013).…”

Section: Related Workmentioning

confidence: 99%

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Trace retrieval for business process operational support

Bottrighi

Canensi

Leonardi

et al. 2016

Expert Systems with Applications

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Operational support assists users while process instances are being executed, by making predictions about the instance completion, or recommending suitable actions, resources or routing decisions, on the basis of the already completed instances, stored as execution traces in the event log.In this paper, we propose a case-based retrieval approach to business process management operational support, where log traces are exploited as cases. Once past traces have been retrieved, classical statistical techniques can be applied to them, to support prediction and recommendation. The framework enables the user to submit queries able to express complex patterns exhibited by the current process instance. Such queries can be composed by several simple patterns (i.e., single actions, or direct sequences of actions), separated by delays (i.e., actions we do not care about). Delays can also be imprecise (i.e., the number of actions can be given as a range). The tool also relies on a tree structure, adopted as an index for a quick retrieval from the available event log.Our approach is highly innovative with respect to the existing literature panorama, since it is the first work that exploits case-based retrieval techniques in the operational support context; moreover, the possibility of * Corresponding author. Tel. +39 0131 360340Email addresses: alessio.bottrighi@uniupo.it (Alessio Bottrighi), canensi@di.unito.it (Luca Canensi), giorgio.leonardi@mfn.unipmn.it (Giorgio Leonardi), stefania.montani@uniupo.it (Stefania Montani), terenz@di.unito.it (Paolo Terenziani) Preprint submitted to Expert Systems with ApplicationsOctober 1, 2015 retrieving traces by querying complex patterns and the indexing strategy are major departures also with respect to other existing trace retrieval tools proposed in the Case Based Reasoning area. Thanks to its characteristics and methodological solutions, the tool implements operational support tasks in a flexible and efficient way, as demonstrated by our experimental results.

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A process mining methodology for modeling unstructured processes

Stefanini

Aloini

Benevento

et al. 2020

Knowl Process Manag

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The effective representation of business processes is widely recognized as a critical task in Business Process Management (BPM). Unfortunately, the complexity of unstructured processes makes process modeling extremely challenging and limits the suitability of traditional model‐driven approaches, which appear considerably less effective and efficient. Nevertheless, most of the recent and promising data‐driven approaches dealing with unstructured processes are not yet completely developed and typically fail to provide an adequate procedural process model. This study proposes a novel process mining‐based methodology to achieve a significant process model when unstructured processes occur. Specifically, the method assesses and combines the outcomes of different process mining algorithms by evaluating the process model through appropriate quality parameters (i.e., accuracy and comprehensibility). The final output of the method corresponds to a unitary procedural process model that is mathematically computable, evaluable through objective quality metrics, comparable with other process models, convertible to other model languages, and usable for supporting BPM activities. Finally, a real case study of an Italian hospital is presented to verify the applicability of the proposed methodology.

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A knowledge-intensive approach to process similarity calculation

Cited by 27 publications

References 31 publications

A telemedicine support for improving medical emergency management

A telemedicine support for improving medical emergency management

Trace retrieval for business process operational support

A process mining methodology for modeling unstructured processes

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