Résultats de recherche pour « Process monitoring »

Predictive Business Process Monitoring is becoming an essential aid for organizations, providing online operational support of their processes. This paper tackles the fundamental problem of equipping predictive business process monitoring with explanation capabilities, so that not only the what but also the why is reported when predicting generic KPIs like remaining time, or activity execution. We use the game theory of Shapley Values to obtain robust explanations of the predictions. The approach has been implemented and tested on real-life benchmarks, showing for the first time how explanations can be given in the field of predictive business process monitoring.

Abstract Predictive Process Monitoring [29] is a branch of process mining that aims at predicting the future of an ongoing (uncompleted) process execution. Typical examples of predictions of the future of an execution trace relate to the outcome of a process execution, to its completion time, or to the sequence of its future activities

Results of the first comprehensive light pollution survey in Hong Kong are presented. The night-sky brightness was measured and monitored around the city using a portable light sensing device called the Sky Quality Meter over a 15-month period beginning in March 2008. A total of 1,957 data sets were taken at 199 distinct locations, including urban and rural sites covering all 18 Administrative Districts of Hong Kong. The survey shows that the environmental light pollution problem in Hong Kong is severe - the urban night-skies (sky brightness at 15.0 mag per arcsec square) are on average ~100 times brighter than at the darkest rural sites (20.1 mag per arcsec square), indicating that the high lighting densities in the densely populated residential and commercial areas lead to light pollution. In the worst polluted urban location studied, the night-sky at 13.2 mag per arcsec square can be over 500 times brighter than the darkest sites in Hong Kong. The observed night-sky brightness is found to be affected by human factors such as land utilization and population density of the observation sites, together with meteorological and/or environmental factors. Moreover, earlier night-skies (at 9:30pm local time) are generally brighter than later time (at 11:30pm), which can be attributed to some public and commercial lightings being turned off later at night. On the other hand, no concrete relationship between the observed sky brightness and air pollutant concentrations could be established with the limited survey sampling. Results from this survey will serve as an important database for the public to assess whether new rules and regulations are necessary to control the use of outdoor lightings in Hong Kong.

Market demand places great emphasis in industry on product quality. Consequently, process monitoring and control have become important aspects of systems engineering. In this article we detail the results of a 2-year study focusing on the development of a condition monitoring system for a fed-batch fermentation system operated by Biochemie Gmbh in Austria. We also demonstrate the suitability and limitations of current state of the art technologies in this field and suggest novel modifications and configurations to improve their suitability for application to a fed-batch fermentation system.

Business processes that involve AI-powered automation have been gaining importance and market share in recent years. These business processes combine the characteristics of classical business process management, goal-driven chatbots, conversational recommendation systems, and robotic process automation. In the new context, prescriptive process monitoring demands innovative approaches. Unfortunately, data logs from these new processes are still not available in the public domain. We describe the main challenges in this new domain and introduce a synthesized dataset that is based on an actual use case of intelligent process automation with chatbot orchestration. Using this dataset, we demonstrate crowd-wisdom and goal-driven approaches to prescriptive process monitoring.

This paper introduces a fast algorithm for moving window principal component analysis (MWPCA) which will adapt a principal component model. This incorporates the concept of recursive adaptation within a moving window to (i) adapt the mean and variance of the process variables, (ii) adapt the correlation matrix, and (iii) adjust the PCA model by recomputing the decomposition. This paper shows that the new algorithm is computationally faster than conventional moving window techniques, if the window size exceeds 3 times the number of variables, and is not affected by the window size. A further contribution is the introduction of an N -step-ahead horizon into the process monitoring. This implies that the PCA model, identified N -steps earlier, is used to analyze the current observation. For monitoring complex chemical systems, this work shows that the use of the horizon improves the ability to detect slowly developing drifts.

With our growing reliability on distributed networks, the security aspect of such networks becomes of prime importance. In large scale distributed networks it becomes cardinal to have an efficient and effective monitoring scheme. The monitoring schemes supervise the node behaviour in the network and look out for any discrepancy. Monitoring schemes comprise of monitoring components that work together to help schemes in meeting various security requirement parameters for the networks. These security parameters are breached via various attacks by manipulation of monitoring components of particular monitoring schemes to produce faulty results and thereby reducing efficiency of networks, reliability and security. In this paper we have discussed these components of monitoring, multiple monitoring schemes, their security parameters and various types of attacks possible on these monitoring components by manipulating assumptions of monitoring schemes.

A short review about the biosensor research activities for bioprocess monitoring in the F.R.G. after its reunification is given. The principles of biosensor applications are presented. In situ sensors and sensors based on the principles of flow injection analysis are studied. Some applications of a four-channel enzyme thermistor, bio-field effect transistors, and immunoanalysis systems for real process monitoring are presented.

M87 is a nearby radio galaxy that is detected at energies ranging from radio to VHE gamma-rays. Its proximity and its jet, misaligned from our line-of-sight, enable detailed morphological studies and extensive modeling at radio, optical, and X-ray energies. Flaring activity was observed at all energies, and multi-wavelength correlations would help clarify the origin of the VHE emission. In this paper, we describe a detailed temporal and spectral analysis of the VERITAS VHE gamma-ray observations of M87 in 2008 and 2009. In the 2008 observing season, VERITAS detected an excess with a statistical significance of 7.2 sigma from M87 during a joint multi-wavelength monitoring campaign conducted by three major VHE experiments along with the Chandra X-ray Observatory. In February 2008, VERITAS observed a VHE flare from M87 occurring over a 4-day timespan. The peak nightly flux above 250GeV was 7.7% of the Crab Nebula flux. M87 was marginally detected before this 4-day flare period, and was not detected afterwards. Spectral analysis of the VERITAS observations showed no significant change in the photon index between the flare and pre-flare states. Shortly after the VHE flare seen by VERITAS, the Chandra X-ray Observatory detected the flux from the core of M87 at a historical maximum, while the flux from the nearby knot HST-1 remained quiescent. Acciari et al. (2009) presented the 2008 contemporaneous VHE gamma-ray, Chandra X-ray, and VLBA radio observations which suggest the core as the most likely source of VHE emission, in contrast to the 2005 VHE flare that was simultaneous with an X-ray flare in the HST-1 knot. In 2009, VERITAS continued its monitoring of M87 and marginally detected a 4.2 sigma excess corresponding to a flux of ~1% of the Crab Nebula. No VHE flaring activity was observed in 2009.

The enactment of business processes is generally supported by information systems that record data about each process execution (a.k.a. case). This data can be analyzed via a family of methods broadly known as process mining. Predictive process monitoring is a process mining technique concerned with predicting how running (uncompleted) cases will unfold up to their completion. In this paper, we propose a predictive process monitoring framework for estimating the probability that a given predicate will be fulfilled upon completion of a running case. The framework takes into account both the sequence of events observed in the current trace, as well as data attributes associated to these events. The prediction problem is approached in two phases. First, prefixes of previous (completed) cases are clustered according to control flow information. Second, a classifier is built for each cluster using event data attributes to discriminate between cases that lead to a fulfillment of the predicate under examination and cases that lead to a violation within the cluster. At runtime, a prediction is made on a running case by mapping it to a cluster and applying the corresponding classifier. The framework has been implemented in the ProM toolset and validated on a log pertaining to the treatment of cancer patients in a large hospital.