91,758 research outputs found
Using phase-type models to monitor and predict process target compliance
Processes are ubiquitous, spanning diverse areas such as business, production, telecommunications and healthcare.
They have been studied and modelled for many years in an attempt to increase understanding, improve
efficiency and predict future pathways, events and outcomes. More recently, process mining has emerged with
the intention of discovering, monitoring, and improving processes, typically using data extracted from event
logs. This may include discovering the tasks within the overall processes, predicting future trajectories, or
identifying anomalous tasks. We focus on using phase-type process modelling to measure compliance with
known targets and, inversely, determine suitable targets given a threshold percentage required for satisfactory
performance. We illustrate the ideas with an application to a stroke patient care process, where there are multiple
outcomes for patients, namely discharge to normal residence, nursing home, or death. Various scenarios
are explored, with a focus on determining compliance with given targets; such KPIs are commonly used in
Healthcare as well as for Business and Industrial processes. We believe that this approach has considerable
potential to be extended to include more detailed and explicit models that allow us to assess complex scenarios.
Phase-type models have an important role in this work.peer-reviewe
Clustering-Based Predictive Process Monitoring
Business process enactment is generally supported by information systems that
record data about process executions, which can be extracted as event logs.
Predictive process monitoring is concerned with exploiting such event logs to
predict 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 predicate can be, for example, a temporal logic constraint or
a time constraint, or any predicate that can be evaluated over a completed
trace. 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
traces are clustered according to control flow information. Secondly, a
classifier is built for each cluster using event data to discriminate between
fulfillments and violations. 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
Specification-Driven Predictive Business Process Monitoring
Predictive analysis in business process monitoring aims at forecasting the
future information of a running business process. The prediction is typically
made based on the model extracted from historical process execution logs (event
logs). In practice, different business domains might require different kinds of
predictions. Hence, it is important to have a means for properly specifying the
desired prediction tasks, and a mechanism to deal with these various prediction
tasks. Although there have been many studies in this area, they mostly focus on
a specific prediction task. This work introduces a language for specifying the
desired prediction tasks, and this language allows us to express various kinds
of prediction tasks. This work also presents a mechanism for automatically
creating the corresponding prediction model based on the given specification.
Differently from previous studies, instead of focusing on a particular
prediction task, we present an approach to deal with various prediction tasks
based on the given specification of the desired prediction tasks. We also
provide an implementation of the approach which is used to conduct experiments
using real-life event logs.Comment: This article significantly extends the previous work in
https://doi.org/10.1007/978-3-319-91704-7_7 which has a technical report in
arXiv:1804.00617. This article and the previous work have a coauthor in
commo
Towards Identifying and closing Gaps in Assurance of autonomous Road vehicleS - a collection of Technical Notes Part 1
This report provides an introduction and overview of the Technical Topic Notes (TTNs) produced in the Towards Identifying and closing Gaps in Assurance of autonomous Road vehicleS (Tigars) project. These notes aim to support the development and evaluation of autonomous vehicles. Part 1 addresses: Assurance-overview and issues, Resilience and Safety Requirements, Open Systems Perspective and Formal Verification and Static Analysis of ML Systems. Part 2: Simulation and Dynamic Testing, Defence in Depth and Diversity, Security-Informed Safety Analysis, Standards and Guidelines
Addressing Uncertainty in TMDLS: Short Course at Arkansas Water Resources Center 2001 Annual Conference
Management of a critical natural resource like water requires information on the status of that resource. The US Environmental Protection Agency (EPA) reported in the 1998 National Water Quality Inventory that more than 291,000 miles of assessed rivers and streams and 5 million acres of lakes do not meet State water quality standards. This inventory represents a compilation of State assessments of 840,000 miles of rivers and 17.4 million acres of lakes; a 22 percent increase in river miles and 4 percent increase in lake acres over their 1996 reports. Siltation, bacteria, nutrients and metals were the leading pollutants of impaired waters, according to EPA. The sources of these pollutants were presumed to be runoff from agricultural lands and urban areas. EPA suggests that the majority of Americans-over 218 million-live within ten miles of a polluted waterbody. This seems to contradict the recent proclamations of the success of the Clean Water Act, the Nation\u27s water pollution control law. EPA also claims that, while water quality is still threatened in the US, the amount of water safe for fishing and swimming has doubled since 1972, and that the number of people served by sewage treatment plants has more than doubled
Nutrient Trading in Lake Rotorua: Determining Net Nutrient Inputs
Lake Rotorua is experiencing increasing nutrient-related water quality problems. This paper is one in a series that explores the idea of creating a nutrient trading system as part of the ongoing policy response to this problem.1 Most of the current nutrient flows to the Lake come from non-point rural sources - measuring these emissions is challenging. We find that it is possible to monitor/model nutrient loss from a wide range of activities in the Rotorua catchment. The model OVERSEER combined with ROTAN and some other models for forestry, urban and geothermal activities and horticulture already exist. They are currently in a process of enhancement - a particular area of current weakness is knowledge of the groundwater lags from specific locations in the catchment. The land-based models need to be used in a specific form that relies on initialisation with verifiable data and uses easily collated and verified data on an annual basis. The form of the model should be fixed for each regulatory year to minimise uncertainty for landowners and regulators. The models need to be updated to reflect new science. The process for doing this needs to be strategic and credible (this will be discussed in a later paper on governance processes). Once changes are recommended they need to be implemented in a way that is perceived to be fair.Water quality; monitor, verify, report, model, emissions trading
Towards the Safety of Human-in-the-Loop Robotics: Challenges and Opportunities for Safety Assurance of Robotic Co-Workers
The success of the human-robot co-worker team in a flexible manufacturing
environment where robots learn from demonstration heavily relies on the correct
and safe operation of the robot. How this can be achieved is a challenge that
requires addressing both technical as well as human-centric research questions.
In this paper we discuss the state of the art in safety assurance, existing as
well as emerging standards in this area, and the need for new approaches to
safety assurance in the context of learning machines. We then focus on robotic
learning from demonstration, the challenges these techniques pose to safety
assurance and indicate opportunities to integrate safety considerations into
algorithms "by design". Finally, from a human-centric perspective, we stipulate
that, to achieve high levels of safety and ultimately trust, the robotic
co-worker must meet the innate expectations of the humans it works with. It is
our aim to stimulate a discussion focused on the safety aspects of
human-in-the-loop robotics, and to foster multidisciplinary collaboration to
address the research challenges identified
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