Development of a co-operative distributed process mining system for quality assurance

In this paper, a co-operative distributed process mining system (CDPMS) is developed to streamline the workflow along the supply chain in order to offer shorter delivery times, more flexibility and higher customer satisfaction with learning ability. The proposed system is equipped with the ‘distributed process mining’ feature which is used to discover the hidden relationships among each working decision in distributed manner. This method incorporates the concept of data mining and knowledge refinement into decision making process for ensuring ‘doing the right things’ within the workflow. An example of implementation is given, based on the case of slider manufacturer.

Automatic processing, quality assurance and serving of real-time weather data over lightweight protocols

Recent advances in technology have produced a significant increase in the availability of free sensor data over the Internet. With affordable weather monitoring stations now available to individual meteorology enthusiasts a reservoir of real time data such as temperature, rainfall and wind speed can now be obtained for most of the United States and Europe. Despite the abundance of available data, obtaining useable information about the weather in your local neighbourhood requires complex processing that poses several challenges. This paper discusses a collection of technologies and applications that harvest, refine and process this data, culminating in information that has been tailored toward the user. In this case we are particularly interested in allowing a user to make direct queries about the weather at any location, even when this is not directly instrumented, using interpolation methods. We also consider how the uncertainty that the interpolation introduces can then be communicated to the user of the system, using UncertML, a developing standard for uncertainty representation.

Automatic processing, quality assurance and serving of real-time weather data

Recent advances in technology have produced a significant increase in the availability of free sensor data over the Internet. With affordable weather monitoring stations now available to individual meteorology enthusiasts a reservoir of real time data such as temperature, rainfall and wind speed can now be obtained for most of the United States and Europe. Despite the abundance of available data, obtaining useable information about the weather in your local neighbourhood requires complex processing that poses several challenges. This paper discusses a collection of technologies and applications that harvest, refine and process this data, culminating in information that has been tailored toward the user. In this case we are particularly interested in allowing a user to make direct queries about the weather at any location, even when this is not directly instrumented, using interpolation methods. We also consider how the uncertainty that the interpolation introduces can then be communicated to the user of the system, using UncertML, a developing standard for uncertainty representation.

Quality Assurance in Extreme Programming

Our previous research about possible quality improvements in Extreme Programming (XP) led us to a conclusion that XP supports many good engineering practices but there is still place for refinements. Our proposal was to add dedicated Quality Assurance (QA) measures, which should be sufficiently effective and at the same time simpler enough in the context of XP. This paper intends to analyze the possibilities for an effective way for applying approved quality assurance practices to XP. The last should not affect negatively to the process and in the meantime must lead to better quality assurance. We aim to make changes to XP that even if would slow down a bit the development process, will make it more suitable for widest range of projects including large and very large projects as well as life critical and highly reliable systems.

Developing a Conceptual Framework for Cloud Security Assurance

Postprint

Compliance with standards, assurance and audit: does this equal security?

Postprint

The Importance of Proper Measurement for a Cloud Security Assurance Model

Winner of a best paper award.

Assurance maladies critiques : conception d'un avenant de sévérité

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal.

Coût de la transplantation de cornée pour la Régie de l'assurance maladie du Québec

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal.

Assurance maladies critiques : conception d'un avenant de sévérité

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal.

Coût de la transplantation de cornée pour la Régie de l'assurance maladie du Québec

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal.

Analytical strategies for the early quality and safety assurance in the global feed chain. Approaches for nitrogen adulterants in soybean meal and mineral and transformer oils in vegetable oils.

In the past decade, several major food safety crises originated from problems with feed. Consequently, there is an urgent need for early detection of fraudulent adulteration and contamination in the feed chain. Strategies are presented for two specific cases, viz. adulterations of (i) soybean meal with melamine and other types of adulterants/contaminants and (ii) vegetable oils with mineral oil, transformer oil or other oils. These strategies comprise screening at the feed mill or port of entry with non-destructive spectroscopic methods (NIRS and Raman), followed by post-screening and confirmation in the laboratory with MS-based methods. The spectroscopic techniques are suitable for on-site and on-line applications. Currently they are suited to detect fraudulent adulteration at relatively high levels but not to detect low level contamination. The potential use of the strategies for non-targeted analysis is demonstrated.