Predictive Process Monitoring Methods: Which One Suits Me Best?

Predictive process monitoring has recently gained traction in academia and is maturing also in companies. However, with the growing body of research, it might be daunting for companies to navigate in this domain in order to find, provided certain data, what can be predicted and what methods to use. The main objective of this paper is developing a value-driven framework for classifying existing work on predictive process monitoring. This objective is achieved by systematically identifying, categorizing, and analyzing existing approaches for predictive process monitoring. The review is then used to develop a value-driven framework that can support organizations to navigate in the predictive process monitoring field and help them to find value and exploit the opportunities enabled by these analysis techniques

Superovulatory Ovarian Response in Mangalica Gilts is Not Influenced by Feeding Level

The aim of the study was to compare how different feeding levels affect the ovarian potential of follicular development and oocyte maturation in response to superovulatory treatment in native Mangalica (M, n = 17) compared with Landrace (L, n = 20) pigs. Gilts of both breeds were fed high-energy (HI-2.5 kg) or low-energy (LO - 1.25 kg) feed during oestrus synchronization (15 days of Regumate (R) feeding) till the time of oocyte aspiration (Day 6 after Regumate (R)). Follicular growth was stimulated by the administration of 1000 IU equine choriou gonadotropiu (eCG) 24 h after Regumate (R) treatment, and ovulation was induced by injection of 750 IU human choriou gonadotropiu (hCG) 80 h after eCG adminstration. Ultrasound (US) investigation was done three times (4-10 h before, and 40-44 and 72-74 h after eCG administration) for the observation of follicular development. Oocyte and follicular fluid (FF) were collected endoscopically 34 h after hCG injection. Cumulus-oocyte complexes were evaluated, their morphology determined, and thereafter fixed and stained for chromatin evaluation. Oocytes were classified as meiosis-resumed (germinal vesicle breakdown, diakinesis, metaphase I to anaphase 1) or matured (telophase I and metaphase 11). FF concentrations of oestradiol and progesterone were measured by validated radioimmunoassays. In L gilts, differences were observed between HI and LO in the number of preovulatory follicles (32.3 +/- 10.5 vs 17.1 +/- 12.3, p 0.05). Initial follicular growth was not affected by feeding levels; however, preovulatory follicle size was larger in M (7.1 +/- 0.9 and 6.9 +/- 1.1 turn vs 5.7 +/- 0.7 and 5.5 +/- 0.8 mm; p <0.05). No differences were obtained with relation to mature chromatin configuration in both breeds (L gilts: HI - 70% and LO-67% vs M gilts: HI- 67% and LO - 63%). A twofold higher oestradiol concentration was detected in FF of HI-M and LO-M (29.6 +/- 6.8 and 30.9 +/- 10.3 ng/ml respectively) compared with that of L (16.9 +/- 9.7 and 17.9 +/- 3.6 ng/ml, respectively; p <0.05). The mean FF progesterone level was nearly fivefold higher in M (2020.4 +/- 1056 and 1512.2 +/- 1121.8 ng/ml) compared with L (386.2 +/- 113.7 and 298.8 +/- 125.9 ng/ml, p <0.05). The results indicate an influence of the feeding of altered energy on the number of recruitable preovulatory follicles in modern Landrace but not in native Mangalica breed. Moreover, the follicular steroid hormone milieu differs between Landrace and Mangalica gilts but not depending on feeding levels. Oocyte maturation was not affected by diet

Explainability in Predictive Process Monitoring: When Understanding Helps Improving

none3Predictive business process monitoring techniques aim at making predictions about the future state of the executions of a business process, as for instance the remaining execution time, the next activity that will be executed, or the final outcome with respect to a set of possible outcomes. However, in general, the accuracy of a predictive model is not optimal so that, in some cases, the predictions provided by the model are wrong. In addition, state-of-the-art techniques for predictive process monitoring do not give an explanation about what features induced the predictive model to provide wrong predictions, so that it is difficult to understand why the predictive model was mistaken. In this paper, we propose a novel approach to explain why a predictive model for outcome-oriented predictions provides wrong predictions, and eventually improve its accuracy. The approach leverages post-hoc explainers and different encodings for identifying the most common features that induce a predictor to make mistakes. By reducing the impact of those features, the accuracy of the predictive model is increased. The approach has been validated on both synthetic and real-life logs.noneWilliams Rizzi; Chiara Di Francescomarino; Fabrizio Maria MaggiRizzi, Williams; Di Francescomarino, Chiara; Maria Maggi, Fabrizi