درخت حوزه‌های تخصصی

The purpose of this article is to measurement and testing a high performance organizational culture model in Iran's petrochemical industries.This research is of quantitative type and in terms of purpose, exploratory and data collection method is survey and from the point of view of cross-sectional time. Data collection tool is a researcher-made questionnaire whose validity has been confirmed by industrial and academic elites. The reliability of the questionnaire was confirmed by Cronbach's alpha test (coefficient of 0.815). The statistical population of the study included 8050 employees of Petropars, Jam, Lavan and Fanavaran petrochemical companies working in Iranian Petrochemical Industries. The sample size was calculated by the Cochran's formula of 385 people. 420 questionnaires were distributed and 400 correct questionnaires were collected. The sampling method is stratified random. Data analysis was performed using structural equation modeling using Amos Software. The results of statistical analysis were confirmed by confirmatory factor analysis and in terms of significance by the method of structural equations up to the level of the main categories and after confirmation of formal and content from 400 experts and managers of petrochemical industries. In the next step with a quantitative approach, the current state of high performance organizational culture according to the pattern the extract was assayed. For this purpose, mean comparison, regression and repetition tests used. The results obtained from field survey indicated that the high-performance organizational culture model was approved and dimensions of Behavioral factors, Internal bedding factors, External bedding factors and Process factors were placed in the first to fourth priorities, respectively.

This paper describes a series of experiments in using logistic regression machine learning as a method for entity resolution. From these experiments the authors concluded that when a supervised ML algorithm is trained to classify a pair of entity references as linked or not linked pair, the evaluation of the model’s performance should take into account the transitive closure of its pairwise linking decisions, not just the pairwise classifications alone. Part of the problem is that the measures of precision and recall as calculated in data mining classification algorithms such as logistic regression is different from applying these measures to entity resolution (ER) results.. As a classifier, logistic regression precision and recall measure the algorithm’s pairwise decision performance. When applied to ER, precision and recall measure how accurately the set of input references were partitioned into subsets (clusters) referencing the same entity. When applied to datasets containing more than two references, ER is a two-step process. Step One is to classify pairs of records as linked or not linked. Step Two applies transitive closure to these linked pairs to find the maximally connected subsets (clusters) of equivalent references. The precision and recall of the final ER result will generally be different from the precision and recall measures of the pairwise classifier used to power the ER process. The experiments described in the paper were performed using a well-tested set of synthetic customer data for which the correct linking is known. The best F-measure of precision and recall for the final ER result was obtained by substantially increasing the threshold of the logistic regression pairwise classifier.