Towards an Empirical Evaluation of Imperative and Declarative Process Mining

被引:8
作者
Back, Christoffer Olling [1 ]
Debois, Soren [2 ]
Slaats, Tijs [1 ]
机构
[1] Univ Copenhagen, Dept Comp Sci, Emil Holms Kanal 6, DK-2300 Copenhagen S, Denmark
[2] IT Univ Copenhagen, Dept Comp Sci, Rued Langgaards Vej 7, DK-2300 Copenhagen S, Denmark
来源
ADVANCES IN CONCEPTUAL MODELING, ER 2018 | 2019年 / 11158卷
关键词
Process mining; Modelling paradigms; Statistical evaluation; Declarative models; Imperative models; Hybrid models; Evaluation metrics;
D O I
10.1007/978-3-030-01391-2_24
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Process modelling notations fall in two broad categories: declarative notations, which specify the rules governing a process; and imperative notations, which specify the flows admitted by a process. We outline an empirical approach to addressing the question of whether certain process logs are better suited for mining to imperative than declarative notations. We plan to attack this question by applying a flagship imperative and declarative miner to a standard collection of process logs, then evaluate the quality of the output models w.r.t. the standard model metrics of precision and generalisation. This approach requires perfect fitness of the output model, which substantially narrows the field of available miners; possible candidates include Inductive Miner and MINERful. With the metrics in hand, we propose to statistically evaluate the hypotheses that (1) one miner consistently outperforms the other on one of the metrics, and (2) there exist subsets of logs more suitable for imperative respectively declarative mining.
引用
收藏
页码:191 / 198
页数:8
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