جستجوی مقالات مرتبط با کلیدواژه « کنترل کیفیت » در نشریات گروه « کتابداری و مدیریت اطلاعات »

Research information databases and search engines are one of the main resources used by researchers every day. To accurately retrieve information from these databases, data need to be stored correctly. Manual controlling of data quality is costly and time-consuming. Here we suggest data mining methods for controlling the quality of a research database. To this end, common errors that are seen in a database should be collected. Metadata of every record in addition to its error codes is saved in a dataset. Statistics and data mining methods are applied to this dataset and patterns of errors and their relationships are discovered. Here we considered Iran's scientific information database (Ganj) as a case study. Experts defined 59 errors. Intimate features of every record, such as its subject, authors' names and name of the university, with its error codes were saved in a dataset. The dataset containing 41021 records was formed.  Statistics methods and association rules were applied to the dataset and the relationship between errors and their pattern of repetition was discovered. Based on our results, in average by considering 25 % of errors in every subject, up to 80%  of errors of all the records in a subject are covered. All the records were also clustered using K-means clustering. Although there was some similarity between records of different subjects, there was not seen any evident relationship between the pattern of repetition of the errors and the subject of records.

In the scientific literature, indexing and quality control are key processes that, if done correctly, can be properly retrieved by search engines by researchers. On the other hand, the use of mechanisms such as infallibility and empowerment of users has made research organizations 100% free from quality control. Also, the restriction on the use of specialized organizational human resources has doubled the importance of paying attention to sampling methods. Although in scientific sources, sampling methods in physical and tangible products have been well and adequately addressed, but in the field of data, especially research data, little work has been done. In this research, a framework for sampling in data quality control processes is provided. Also, an algorithm has been developed for statistical design to minimize type I and II errors. As a case study of research data, the information dissemination database of dissertations / dissertations (pious) of graduates of the whole country (Ganj) has been selected and the research method has been implemented in this database. The results of this study showed that, considering the acceptable quality of many pious information items after registration, sampling is a vital task in improving the efficiency of the information organization and analysis unit. The classification of information items into three categories is critical, main and partial, and determining the number and method of sampling for each category is another result of this research. The framework presented in this research can be localized for various data-driven organizations, especially businesses based on research data. Since any revision of AQL and LTPD values affects type I and II errors, it is necessary to apply the algorithms developed in this research to new AQL and LTPD values as well. Obviously, the results of the algorithm implementation such as number of samples, acceptance number and rejection number will be updated in this process.

Today, measuring data quality is one of the most important strategies in improving data-driven business processes. Any correct decision to improve systems in this category of organizations depends on an appropriate analysis of data quality. Research data and especially theses / dissertations  of graduates of the whole country based on this principle from various aspects of data quality need to be reviewed and evaluated. In the process of registering, the quality control of metadata is one of the most important parts that examines the information items of documents (such as the name of the researcher, supervisors and advisors, abstract, index, etc.).In the current situation, incompatible documents are identified during the quality control process and after entering in the system (as text), the relevant document is systematically returned to the researcher. Non-standard incompatible identified and the lack of proper classification for incompatible documents leading to informing tastes to researchers, making statistical analysis of metadata quality problems difficult, and also make it impossible to analyze the root of observed errors. Therefore, in this study, the incompatible structure observed after standardization has been used experimentally in two-month periods and the results have been presented. Incompatible in thesis defense date, the title page (Persian and English) and the existence of white pages in the file have been among the most important reasons for returning documents to users.  Also, the analysis of all data showed that 59% of the incompatibles were related to the attached files and 41% to the information recorded in the system. Finally, based on the performed analyzes, guidelines have been provided for the users of the system in order to improve the quality of data, according to specialized areas

Recent researches suggest that data is known as one of the most valuable resources of any organization. On the other hand, today science and technology data have become a key to the growth and development of each country. Treasure system plays a key role in advancing science and technology in the country and providing researchers with the services of effective and efficient research. Accordingly, the performance of this system is one of the factors influencing this mission. On the other hand, the major part of the observed quality problems in the area of information dissemination by the system is rooted radically in the output of another system called the registration system, which is at the very beginning of the process of recording, organizing and disseminating information. Determining the desirable status (or level of quality) in each field of the metadata from a variety of quality aspects such as accuracy, inconsistency, completeness, etc. is one of the most vital steps in the field of quality of the registration system. In this study, after reviewing the standard
data quality management models, a comprehensive framework was developed to determine the quality dimensions of the registration system The TDQM model was selected as one of the most widely used data quality model to evaluate the components of the system from its main four-dimensional core, accessibility, terrain and display. Then, in the form of a Quality Management System, key factors will be determined in the quality control system of the registration system. The data quality plan presented after the validation by the team of experts as the reference point for determining the non-conformities of the data. At the end of the present study, practical steps were taken to improve the quality of this system.