Abstract
The problem of understanding whether a process trace satisfies a prescriptive model is a fundamental conceptual modeling problem in the context of process-based information systems. In business process management, and in process mining in particular, this amounts to check whether an event log conforms to a prescriptive process model, i.e., whether the actual traces present in the log are allowed by all behaviors implicitly expressed by the model. The research community has developed a plethora of very sophisticated conformance checking techniques that are particularly effective in the detection of non-conforming traces, and in elaborating on where and how they deviate from the prescribed behaviors. However, they do not provide any insight to distinguish between conforming traces, and understand their differences. In this paper, we delve into this rather unexplored area, and present a new process mining quality measure, called informativeness, which can be used to compare conforming traces to understand which are more relevant (or informative) than others. We introduce a technique to compute such measure in a very general way, as it can be applied on process models expressed in any language (e.g., Petri nets, Declare, process trees, BPMN) as long as a conformance checking tool is available. We then show the versatility of our approach, showing how it can be meaningfully applied when the activities contained in the process are associated to costs/rewards, or linked to strategic goals.
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Notes
- 1.
We are fully aware of the different senses in which the term soft goal is employed in the literature [6], namely, alternatively as a synonym to non-functional requirement, fuzzy propositional content, or propositional contents without a generally accepted satisfaction criteria. Here, as in [7], we use the term in the specific sense just defined.
- 2.
As discussed in [12], even though people intuitively assume a positive connotation of the term value, value emerges from events that impact goals either positively or negatively. More specifically, the value ascribed to an event consists of benefits (positive value contributions) or sacrifices (negative value contributions).
- 3.
For simplicity, we model this as a deferred choice, but in reality we should also ensure that the customer cannot proceed to the order finalization if the cart is empty.
- 4.
In this context, value denotes a symbolic/numeric constant.
- 5.
Sub-sequences should not be confused with sub-strings.
- 6.
Consider the set I of indexes of events in trace t: \(I = \{1, 2, \dots , |t| \}\). By taking only events from a subset of I, we can generate a possible sub-sequence of t. Therefore, the set of all possible sub-sets of I, also called power-set \(\mathcal {P}(I)\), contains the indexes of all possible sub-sequences and \(|\mathcal {P}(I)| = 2^{|I|} = 2^{|t|}\). From this value, we need to remove two special sub-sequences: the empty and the original ones. Therefore, we end up with \(2^{|t|} - 2\) possible sub-sequences.
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Acknowledgements
The work is supported by Innovation Fund Denmark project EcoKnow.org (7050-00034A). The authors would like to thank Marlon Dumas for providing the inspiration for Fig. 1.
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Burattin, A., Guizzardi, G., Maggi, F.M., Montali, M. (2019). Fifty Shades of Green: How Informative is a Compliant Process Trace?. In: Giorgini, P., Weber, B. (eds) Advanced Information Systems Engineering. CAiSE 2019. Lecture Notes in Computer Science(), vol 11483. Springer, Cham. https://doi.org/10.1007/978-3-030-21290-2_38
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