Human Information Interaction

Purpose: The purpose is to present an open government data evaluation method by considering comprehensive and complete dimensions and criteria - calculating the weight and importance of each criterion, examining the country in this area, clustering organizations and presenting a classification model to predict the situation.
Methodology: Library studies was used to extract the dimensions and criteria of evaluation. Population includes articles related to open government data evaluation criteria. Ten articles were reviewed by simple random sampling method. Multiple attribute decision making techniques was used to calculate the weight and importance of each criterion. Data mining techniques was incorporated to cluster and create a classification model.
Findings: By reviewing the articles 15 criteria of open government data evaluation including:  Data originality, license openness, up-to-datedness, data access rate, metadata completeness, number of data sets, format openness, non-discriminatory, comprehensible, number of data fields, free, no missing data, data request ability, visual and feedback, were extracted. Using AHP technique, the weights of the criteria were calculated, which after normalization, the total weight of the 15 extracted criteria was equal to one. "Data originality" with a weight of 0.165, " license openness " with a weight of 0.124 and " up-to-datedness" with a weight of 0.109 were ranked first to third among 15 evaluation criteria, respectively. Weight of evaluation criteria obtained and data extraction of 358 organizations in harmony with 15 evaluation criteria, the weight of organizations was calculated. The sum of the weights was equal to one. "East Azerbaijan Agricultural Jihad Organization" with a weight of 0.088, "Statistics Center of Iran" with a weight of 0.062 and "Geological Survey" with a weight of 0.058 were the first to third ranks among 358 organizations and government institutions, respectively, based on the combination of criteria and the weight of criteria.
Conclusion: Evaluation criteria obtained, calculating the weight and importance of each criterion, examining the current situation of government organizations and institutions in the country and the classification model created can help managers to understand the current situation and improve it and thus increase citizens' interaction with open government data as a kind of human information interaction.

Introduction: Errors in data collection and failure to pay attention to data that is noisy in the collection process for any reason cause problems in data-based analysis and, as a result, wrong decision-making. Therefore, solving the problem of missing or noisy data before processing and analysis is of vital importance in analytical systems. The purpose of this paper is to provide a method to identify noisy data, outliers, and missing data and provide a suitable solution for these data.
Methods: This study is applied research. Data mining techniques including binning smoothing and regression models have been used to identify and replace outlier and noisy data.
Results: The results of the tests performed in the real environment related to the data of social networks show the proper performance of the proposed method. It has also been shown that the proposed method has higher accuracy compared to the methods of binning smoothing, average and linear regression. So that for the data related to the tweet section, the mean squared error obtained for the proposed method was equal to 0.04, the binning smoothing method was equal to 0.38, the linear regression method was equal to 0.05 and the average method was equal to 0.06.
Conclusion: The method presented in this article can initially identify outlier data through one-third and two-thirds normal, and then replace the outlier data with a linear regression model, which results in improving the performance of using and processing information and improving human-information interaction