Questionnaire-based surveys are a standard method used for assessing the safety climate within an organization. However, their analysis - in particular data aggregation - poses several challenges, among which are subj...
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Questionnaire-based surveys are a standard method used for assessing the safety climate within an organization. However, their analysis - in particular data aggregation - poses several challenges, among which are subjective judgment, incompleteness and uncertainty. This paper explores the use of approaches based on belief structures for aggregating data from safety climate questionnaires. Data relevant to this study were collected through a questionnaire administered to the employees of a nuclear research centre. The results show that, while belief structures may offer a promising way to represent data collected from questionnaires, the existing aggregation methods are not always adequate. Averaging schemes applied to belief structures seem the most suited - among the methods investigated - in the specific problem context analyzed. The analysis of the survey data shows the limitations of quantitative approaches for safety culture assessment and the need to always complement these with in-depth qualitative analysis. (C) 2015 Elsevier Inc. All rights reserved.
aggregation is the process of gathering and combining information from a number of sources. In peer-to-peer systems, aggregation is a basic component of a range of applications, including monitoring and complex-query ...
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aggregation is the process of gathering and combining information from a number of sources. In peer-to-peer systems, aggregation is a basic component of a range of applications, including monitoring and complex-query resolution. Peer-to-peer aggregation services themselves are dependent on a number of other fundamental peer-to-peer services - directories, multicasting and system-size approximation. The overall performance characteristics of an aggregation service are affected by the chosen implementation method for these underlying services. To illustrate this relationship, aggregation techniques for internet-based peer-to-peer systems are surveyed and dissected into their component parts. We further consider the problem of running one-off aggregation queries in a peer-to-peer network. A new aggregation service, Bliksum, which uses a novel combination of underlying services, is introduced. Bliksum employs unstructured peer-to-peer techniques for node sampling, multicasting and system-size approximation, in combination with a method of building a temporary tree structure for aggregation itself. Unstructured peer-to-peer techniques have been shown to be highly resilient to node churn, avoiding the problem inherent in structured systems of maintaining the desired structure when the set of nodes changes rapidly. We present experiments showing that Bliksum retains these advantages while reducing communications cost and reducing information loss compared to pure gossip-based aggregation.
The aggregation procedure is an important theoretical and empirical topic of economics. It appears in the Microeconomics and Macroeconomics, in Panel and Cross-Sectional Data Analysis, in Data Mining Analysis, in Inpu...
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The aggregation procedure is an important theoretical and empirical topic of economics. It appears in the Microeconomics and Macroeconomics, in Panel and Cross-Sectional Data Analysis, in Data Mining Analysis, in Input-Output and Agent-based Computational Economics Modelling. The question of the choice of algorithm is became important since the size of the sample data has became more important, and despite of the speed of the computers. In this paper we present the "classical" algorithm (the "matrix algebraic" one) of aggregation of the two-dimensional sample data, and compare it to the alternative algorithms (the "vectorial" one) we developed. Then we present some extensions to the multidimensional aggregation.
This paper presents an analytical approach to determine the suitability of and to adapt discrete-event simulation for real-time decision making in flexible manufacturing systems (FMSs). First, a formula is developed t...
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This paper presents an analytical approach to determine the suitability of and to adapt discrete-event simulation for real-time decision making in flexible manufacturing systems (FMSs). First, a formula is developed to predict the simulation CPU run time for a given manufacturing system, a planning horizon, and a computer system. It is shown that there are only three main factors that affect simulation run time: the planning horizon, the overall system average interarrival time, and the average number of workstations per part routing. In light of this, an approach to reduce simulation run time is presented. It is based on aggregating workstations to reduce the average number of workstations per part routing. The validity of the approach is justified by comparing the performance measures of example systems with and without aggregation. A theoretical approximation of the error incurred as a result of aggregations is derived. The results show that time savings of up to 400% can be achieved at the expense of only a few percentage points loss in accuracy of the average flowtime performance measure. The developed concepts have been integrated in an algorithm to serve as a front end for discrete-event simulation to adapt simulation models to real-time decision-making requirements. The application of this algorithm is illustrated with an example.
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