Incremental Predictive Process Monitoring: How to Deal with the Variability of Real Environments

A characteristic of existing predictive process monitoring techniques is to first construct a predictive model based on past process executions, and then use it to predict the future of new ongoing cases, without the possibility of updating it with new cases when they complete their execution. This can make predictive process monitoring too rigid to deal with the variability of processes working in real environments that continuously evolve and/or exhibit new variant behaviors over time. As a solution to this problem, we propose the use of algorithms that allow the incremental construction of the predictive model. These incremental learning algorithms update the model whenever new cases become available so that the predictive model evolves over time to fit the current circumstances. The algorithms have been implemented using different case encoding strategies and evaluated on a number of real and synthetic datasets. The results provide a first evidence of the potential of incremental learning strategies for predicting process monitoring in real environments, and of the impact of different case encoding strategies in this setting

Explain, Adapt and Retrain: How to improve the accuracy of a PPM classifier through different explanation styles

Recent papers have introduced a novel approach to explain why a Predictive Process Monitoring (PPM) model for outcome-oriented predictions provides wrong predictions. Moreover, they have shown how to exploit the explanations, obtained using state-of-the art post-hoc explainers, to identify the most common features that induce a predictor to make mistakes in a semi-automated way, and, in turn, to reduce the impact of those features and increase the accuracy of the predictive model. This work starts from the assumption that frequent control flow patterns in event logs may represent important features that characterize, and therefore explain, a certain prediction. Therefore, in this paper, we (i) employ a novel encoding able to leverage DECLARE constraints in Predictive Process Monitoring and compare the effectiveness of this encoding with Predictive Process Monitoring state-of-the art encodings, in particular for the task of outcome-oriented predictions; (ii) introduce a completely automated pipeline for the identification of the most common features inducing a predictor to make mistakes; and (iii) show the effectiveness of the proposed pipeline in increasing the accuracy of the predictive model by validating it on different real-life datasets

Genetic algorithms for hyperparameter optimization in predictive business process monitoring

Predictive business process monitoring exploits event logs to predict how ongoing (uncompleted) traces will unfold up to their completion. A predictive process monitoring framework collects a range of techniques that allow users to get accurate predictions about the achievement of a goal for a given ongoing trace. These techniques can be combined and their parameters configured in different framework instances. Unfortunately, a unique framework instance that is general enough to outperform others for every dataset, goal or type of prediction is elusive. Thus, the selection and configuration of a framework instance needs to be done for a given dataset. This paper presents a predictive process monitoring framework armed with a hyperparameter optimization method to select a suitable framework instance for a given dataset

Combining NLP Approaches for Rule Extraction from Legal Documents

International audienceLegal texts express conditions in natural language describing what is permitted, forbidden or mandatory in the context they regulate. Despite the numerous approaches tackling the problem of moving from a natural language legal text to the respective set of machine-readable conditions, results are still unsatisfiable and it remains a major open challenge. In this paper, we propose a preliminary approach which combines different Natural Language Processing techniques towards the extraction of rules from legal documents. More precisely, we combine the linguistic information provided by WordNet together with a syntax-based extraction of rules from legal texts, and a logic-based extraction of dependencies between chunks of such texts. Such a combined approach leads to a powerful solution towards the extraction of machine-readable rules from legal documents. We evaluate the proposed approach over the Australian " Telecommunications consumer protections code "

Apigenin oxidovanadium(IV) cation interactions : Synthesis, spectral, bovine serum albumin binding, antioxidant and anticancer studies

Continuing and expanding our previous work on flavonoid oxidovanadium(IV) (VO) metal complexes as possible anti-cancer agents, the VOapigenin compound was synthesized and characterized. An “acetylacetone-like” coordination through the C=O and O moieties of the ligand to the metal center with one apigenin ligand per metal ion was assumed using different spectroscopies and elemental analysis as well as thermal measurements. The vibrational experimental spectrum of VOapigenin was supported by theoretical calculations. According to the structure of the flavonoid it exerted mild antioxidant properties that were enhanced by metal coordination. The compounds showed moderate anticancer activity on lung A549 and cervix HeLa cancer cell lines, displaying an incubation time dependent behavior. Cellular increase of reactive oxygen species (ROS) and glutathione depletion have been measured upon incubation with the compounds. These cell killing activities were reverted when natural antioxidants were incubated with the compounds and the addition of the antioxidant agent Nacetylcysteine generated depletion of the cellular ROS levels. Therefore, a stress oxidative mechanism of action has been assumed. Moreover, the compounds showed no toxicity against Artemia salina and were not mutagenic. Both apigenin and the complex could be transported and stored by bovine serum albumin with similar binding constants and mechanisms than other VOflavonoid complexes.Centro de Química Inorgánic

Antioxidant and anticancer effects and bioavailability studies of the flavonoid baicalin and its oxidovanadium(IV) complex

Based on the known antioxidant effect of flavonoids, baicalin (baic) found in roots of Scutellaria has been selected. Its coordination complex with the oxidovanadium(IV) cation, Na4[VO(baic)2].6H2O (VIVO(baic)), was synthesized at pH 9 in ethanol and characterized by physicochemical methods. Spectrophotometric studies at pH 9 showed a ligand: metal stoichiometry of 2:1. By vibrational spectroscopy a coordination mode through the cis 5-OH and 6-OH deprotonated groups is inferred. EPR spectroscopy shows an environment of four aryloxide (ArO−) groups in the equatorial plane of the V=O moiety, both in solution and in the solid complex. The antioxidant capacity against superoxide and peroxyl radicals of VIVO(baic) resulted greater than for baicalin and correlated with previous results obtained for other VOflavonoid complexes. The coordination mode produces delocalization of the electron density and the stabilization of the radical formed by interaction with external radicals. The complex and the ligand displayed no toxic (Artemia salina test) and no mutagenic (Ames test) effects. The complex improved the ability of the ligand to reduce cell viability of human lung cancer cell lines (A549) generating reactive oxygen species (ROS) in cells, being this effect reversed by pre-incubation of the cells with antioxidants such as vitamins C and E. The addition of NAC (N-acetyl-L-cysteine, a sequestering agent of free radicals) suppresses the anticancer effect, confirming the oxidative stress mechanism. The complex interacted with bovine serum albumin (BSA) with stronger binding than baicalin and the mechanisms involved H bonding and van der Waals interactions.Centro de Química Inorgánic

Width Helps and Hinders Splitting Flows

Peer reviewe

Width Helps and Hinders Splitting Flows

Peer reviewe

The Inner Halo of M87: A First Direct View of the Red-Giant Population

An unusually deep (V,I) imaging dataset for the Virgo supergiant M87 with the Hubble Space Telescope ACS successfully resolves its brightest red-giant stars, reaching M_I(lim) = -2.5. After assessing the photometric completeness and biasses, we use this material to estimate the metallicity distribution for the inner halo of M87, finding that the distribution is very broad and likely to peak near [m/H] ~ -0.4 and perhaps higher. The shape of the MDF strongly resembles that of the inner halo for the nearby giant E galaxy NGC 5128. As a byproduct of our study, we also obtain a preliminary measurement of the distance to M87 with the TRGB (red-giant branch tip) method; the result is (m-M)_0 = 31.12 +- 0.14 (d = 16.7 +- 0.9 Mpc). Averaging this result with three other recent techniques give a weighted mean d(M87) = (16.4 +- 0.5) Mpc.Comment: In press for Astronomy and Astrophysic

The ACS Nearby Galaxy Survey Treasury III: Cepheids in the Outer Disk of M81

The ACS Nearby Galaxy Survey Treasury (ANGST) has acquired deep ACS imaging of a field in the outer disk of the large spiral galaxy M81. These data were obtained over a total of 20 HST orbits, providing a baseline long enough to reliably identify Cepheid variable stars in the field. Fundamental mode and first overtone types have been distinguished through comparative fits with corresponding Cepheid light curve templates derived from principal component analysis of confirmed Cepheids in the LMC, SMC, and Milky Way. A distance modulus of 27.78 pm 0.05_random pm 0.14_systematic with a corresponding distance of 3.60 pm 0.23 Mpc has been calculated from a sample of 11 fundamental mode and 2 first overtone Cepheids (assuming an LMC distance modulus of mu_LMC=18.41 pm 0.10_r pm 0.13_s).Comment: 10 pages, 9 figures. Accepted for publication in AJ Fixed typo