پژوهشنامه پردازش و مدیریت اطلاعات
سال سی و هفتم شماره 3 (پیاپی 109، بهار 1401)

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

With the advancement of human society and information and communication technology, the Internet of Things has penetrated into various aspects of the daily lives of people and industries. Emerging blockchain technology has become a viable solution to IoT security due to its inherent characteristics such as distribution, security, immutability, and traceability. However, the integration of IoT and blockchain has challenges such as latency, throughput, scalability, and device power limitation. Recent research has focused on the role of artificial intelligence methods in improving IoT performance in blockchain. According to the studies, there are few effects on improving the performance of IoT devices with limited power, so in this study, a conceptual model for improving blockchain performance in IoT devices with limited power by deep reinforcement learning is proposed. According to studies, there is little research on improving the performance of IoT devices with limited power, so in this study, a conceptual model to improve blockchain performance in IoT devices with limited power by deep reinforcement learning is proposed. In this model, Internet devices with limited power can delegate their extraction task to the mobile edge computing layer. The presented model has six layers of perception, data, network, consensus, mobile edge computing and application, which are explained in detail. In this model, to improve the throughput and select the mining method, a recommender located in the mobile edge computation layer is used. recommender systems are adjusted by adjusting the size and time of building blocks to improve the throughput and also tries to minimize the delay and energy consumption of the mining operation by selecting suitable method. To achieve good performance in reinforcement learning, the use of Q learning and long- short term memory is suggested. the use of deep reinforcement learning is to set the block size by considering the transmission delay in order to increase throughput as well as mining with respect to the minimum delays and energy consumption in the proposed conceptual model can improve the performance of blockchain in the IoT.

Improving the quality of supply chain performance in manufacturing sectors, factories and businesses are one of the most important objectives of managers in each country, and the economic future of each country depends on the performance of the supply chains. Also, the increasing development of the Internet of Things (IoT) has an important role in facilitating the process of businesses in different parts of the supply chain. Internet of things has many applications in this area. The aim of this study is to identify and prioritize the internet of things applications in supply chain management (SCM) with multi criteria decision making (MCDM) approach. In this research, COPRAS, a MCDM method has been used. The evaluation criteria are derived based on the opinions of experts in the Iranian society of information and communication technology and managers working in the companies that are using the IoT. These criteria are selected by thematic analysis method. The research method is applicable in terms of purpose and is descriptive in terms of means of data collection. Research shows that "Monitoring and Control of perishable food and medicine", "maintaining employee health in different industries", "Smart Manufacturing Line and Smart Devices," and “Smart maintenance and repair” are the most important application of IoT in SCM.

The present research has been done with a view to identifying and ranking the supply chain risks in digital libraries of state universities in Tehran based on the 2018 version of ISO 31000.

The present research is an applied one and to implement it, survey and analytic methods have been applied with Shannon entropy to weight risks and fuzzy TOPSIS to evaluate them. Primarily the most important risks were identified using the relevant resources and the results of previous researches; a process which led to the formulation of a questionnaire with 5 components and 109 items. The stability of the questionnaire was calculated based on the internal consistency coefficient & Cronbach alpha coefficient using SPSS software. The resulted value in each aspect of the questionnaire amounted to more than %98 and then the analysis and ranking of the risks were carried out using the above-mentioned techniques.

In this research the risks of supply chain have been evaluated in 5 sections: suppliers, production & digital resources management, distribution of digital resources, services to users and common risks among the components of supply chain. In the suppliers section the risk index of  "The fluctuation  of exchange rates" has the highest rank(=%73), in production  and digital resources management, the risk index of "The strategy of collection development of digital resources" has the highest rank(=%73), in distribution of digital resources section, the risk index of "Possibility of rendering  web-based loan services in the digital library" has the highest rank(=%84), in services to users section,  the risk index of "The possibility of searching by means of any browsers in the digital library" has the highest rank(=%88) and in the common risks among the components of supply chain section, the index risk of "The increase of hardware prices of the components of supply chain due to the fluctuation of exchange rate" has the highest rank(=%82).

The risks based on their ranking and by applying the TOPSIS technique in the supply chain of digital libraries in the universities of Tehran in order of importance that is from number 1 to number 5 include: "risks of suppliers", "common risks among the components of supply chain", "risks relating to services of users", "risks involving the production & digital resources management", and "risks involving the distribution of digital resources".

The emergence of social networks and phenomena such as tagging have led to changes in the structure, language, and content of information. The purpose of this study was to analysis of discourse of digital information organization in social tagging. Discourse analysis by Laclau and Mouffe's was used as a research method. LibraryThing were chosen with purposive sampling. End-user as a nodal point located in the center of semantic systems of digital information discourse, language, cataloging, and members were elements articulated around the end-user. Popularity, commonness, participatory, locality, relativeness, fluidity and independence from professional catalogers and standards of cataloging were identified as a features of cataloging. Easiness, unlocality, flatness and lack of specific precondition were the features of membership in this database. Language and narration of end-users were a dominate in this database but language and narration of elite in modern discourse of traditional library organization was marginalized. Tags are an important phenomenon for presentation the user language and construction of the database. Uncontrolled, relative, non-standard, unofficial and De-authorized languages of end –users provide suitable status for deconstructive interpretations from the texts in libraryThing. Classification, updatness, being free and Non-profits were the moments of information organization discourse in libraryThing. Features of modern discourse of information organization such as obsolescence, hierarchical, authoritarianism are rejecting to the field of discursivity. Discourse of digital information organization articulation in contrast with modern systems of information organization in libraries.

In this research, a new algorithm for facets extraction has been developed and introduced, which provides the experimental possibility of identifying facets based on a literary warrant. In the field of automatic facet extraction, two main ideas were considered by reviewing the researches. The first idea is that the facet appears in the context. Therefore, to identify the facet in a corpus, its context must be examined. The second idea is that the facet is the focal point in a lexical tree that is neither very general nor very specific. Based on these two ideas, first, the corpus in the medicine area and the obstetrics and gynaecology domain was prepared. The research team selected three corpora from the literary warrant and used the abstract and title of the collection of articles in the top 20 journals of the field to create a contextual corpus. This collection contained 167071 documents. 2000 articles were randomly selected to create the origin corpus. The third body is the lexical corpus. The proper words of the corpus were extracted using a web-based service. The output contained 514 words. Duplicate words were removed and finally, 480 important words were identified. Then, the words were expanded in the contextual corpus with the help of the guide set- Mesh and then-candidate dissertations were extracted based on the two conditions of frequency-based Shifting and rank-based Shifting. Finally, using the three rules of specificity, substitution, and generality, the identified facets were modified and named. Finally, 26 facets were identified in the domain of gynaecology and obstetrics. Comparing the proposed algorithm with other algorithms, it was found that the combination of statistical approach and tree pruning can have better results than purely statistical approach or tree pruning. Also, the comparison of the output facets of the algorithm with the traditional facets in this obstetrics and gynaecology domain showed that the output of the algorithm is smaller and more useful for browsing information retrieval tools. Also, in this study was specified that specialized domain facets are different from general facets and can be redefined independently, but the results cannot be generalized to all medical domains and other research needs to be done in other fields.

Due to the fact that library catalogs are often hidden in the invisible web, the purpose of the present research has been increasing visibility and findability of bibliographic records of Online Public Access Catalog’s (OPAC) of the NLAI. Also, SEO components (i.e. visibility, ranking and click through rate) were measured in Google before and after using the Google Search Console.  The research method is action research. Bibliographic records of NLAI Sitemaps were submitted to Google Search Console. Lewin's three steps (planning, execution and evaluation) were used to design the research steps.  Less than four months from submission, a fivefold increase (from 17948 to 90330) in indexing records was observed. During this period, 4% of URL sitemaps submitted to Google Search Console were indexed. This number is increasing daily. From the perspective of other SEO components (ranking and click-through rate), the ranking decreased from 7.8 to 8.6. Also the click-through rate decreased from 12.5% to 11.1%. The highest click-through rate was for desktop with 19.3%.  Click-through rate is calculated by dividing number of clicks by the number of impressions. As the number of impression of OPAC in Search Engine Results Pages increased due to the indexing of records, the number of clicks must also increase in order to achieve a high click-through rate. The click-through rate increases the OPAC’s NLAI ranking from eighth to first to third. By improving the infrastructure and addressing the problems with tags and meta tags, ranking could increase. Doubling the click-through rate on desktop compared to mobile phone is also a warning to the National Library of Iran to fundamentally changes its OPAC architecture. This structure was designed fifteen years ago and needs to be redesigned. Nowadays, to be mobile friendly is a highly important characteristic in Google rankings. The NLAI like other OPACs in the world, needs strategic and continuous planning for high visibility and ranking on the web

Business and organization environments need using numerous processing, saving and software resources for achieving their determined goals. Genesis of Cloud Computing, transform method of access to the information processing and saving resource. Having easy accessing features through network communication standard mechanism and Automatic increasing resource mechanism, cause great interest in organizations to use Cloud services. However, choosing suitable service despite increasing variety of services in terms of price, place and quality of service parameters, make the cloud services selection process so complex. One of the requirements for choosing the right cloud service is Identify the factors influencing the selection and estimation of the importance of them. This research has tried to follow a mixed approach and use systematic literature review and fuzzy Delphi method in the form of an exploratory plan, Identifies the effective factors in choosing the organizations cloud service and rank these factors with the help of the best-worst method. In the first step, by systematically reviewing the literature in this field and also comparatively reviewing the three industrial standards of cloud service level agreements, 24 elements affecting cloud service selection were identified, in order to expand the existing body of knowledge, validate and refine the factors affecting the choice of cloud service, Fuzzy Delphi method was used. Based on the consensus of experts, 20 factors affecting the choice of organizations cloud service were approved and finalized in the form of 4 main components. At the end, the factors confirmed from the previous stage were weighted and ranked by the best-worst method. The results showed that performance, security, environmental and organizational components are the most important in choosing a cloud service. Of the 20 factors influencing cloud service selection, availability factors weighing 0.137, reliability weighing 0.134, response time/delay weighing 0.122, governance weighing 0.096, and capacity weighing 0.072 have the highest weight, respectively.

In this paper, a new method for annotating images in Persian scientific documents is suggested.  Images in scientific documents contain valuable information. In many cases, by analyzing images one can understand the main idea and important results of the document. Due to the explosive growth of image data, automatic image annotation has attracted extensive attention and become one of the growing subjects in the literature. Image annotation is the first step in image retrieval methods, in which descriptive tags are assigned to each image. Here, for image annotation the associated text is used. The caption and the part of the document that includes the reference to the image are considered. None phrases in the associated text are ranked based on five different methods; term frequency, inverse document frequency, term frequency–inverse document frequency, cosine similarity between word embedding of noun phrases in the text and the caption and using both term frequency–inverse document frequency and cosine similarity methods. Image tags in every method are the noun phrases with the highest rank. Suggested methods are evaluated on the test data from Iran scientific information database (Ganj), the main database of Persian scientific documents. Term frequency–inverse document frequency method gives the best results.

Today, the present and future success of competition between organizations is based, in part, on the strategic expertise of physical and financial resources and, to a large extent, on the strategic management of knowledge and knowledge assets. The main foundation of emerging companies and organizations based on knowledge and the use of ideas, creativity, and innovation is the reality of the new business world. The overall purpose of this research identify the effective components of knowledge management in the startups of the field of information science and science and provide a model in this regard. The main purpose of this study is to identify the effective components of knowledge management in startups in the field of information science and epistemology and to provide a model in this regard. This research is applied in terms of purpose and is exploratory mixed research in terms of method. The present study was conducted in two parts: qualitative (meta-combination) and quantitative (Delphi and exploratory factor analysis). The population in the qualitative part included Persian and Latin articles related to knowledge management in startups, which were selected according to the inclusion criteria in the meta-combined method. In order to reach a consensus on the components extracted from the meta-combination, the Delphi method and a survey of 30 faculty members in this field were applied. The quantitative section includes 323 examples of startup-related companies in the field of information science and epistemology, which were selected by systematic stratified random sampling method. The data collection tool was a questionnaire whose reliability was confirmed by Cronbach's alpha for the whole questionnaire equal to 0.811. SPSS statistical software was used to do exploratory factor analysis by which 128 items of the questionnaire were classified into 9 main components and 55 sub-components. The results of confirmatory factor analysis show 9 components of knowledge application (23.875), knowledge sharing (11.261), knowledge creation (8.805), organizing knowledge (4.426), knowledge identification (2.820), knowledge presentation (2.684), knowledge development (2.342), knowledge change (2.047), and knowledge protection (1.876) are effective on knowledge management in information science and epistemology startups. Accordingly, knowledge application has the most variance and the knowledge protection has the least variance. Considering the limited studies that have been done in the field of information science and science startups in the country, the results of this research can be used as a model for implementing knowledge management in explaining and recognizing the factors affecting information science and science startups and provided further development of such startups.

The clinical staff encounter important and common information problems which solving them often requires accurate, valid and reliable information that can be accessed and used in the shortest time. The purpose of this study was to investigate the effect of education and awareness components on the collaborative information seeking behavior among the clinical staff of the Qazvin University of Medical Sciences during the implementation of research projects in 2020.

This research was a semi-experimental study based on pre-test, post-test design. The statistical population of this study consists of 40 clinical staff working in hospitals. Data collection was done through a questionnaire.

Educational intervention had a significant effect on increasing the level of collaborative awareness of individuals as well as their participation in the information seeking process (P<0.05). Evaluation of the structural equation model of CIS behavior with RMSEA= 0.058 and P value <0.05 showed that the model had a good fit.

The results of evaluating the collaborative information seeking behavior of clinical staff showed with increasing people's awareness about collaboration methods, the level of interaction and group cooperation has increased and its representation in increasing team communication and division of tasks is well visible. Information sharing as an activity that is the result of cooperation and collaboration of individuals, enables participants to create collective knowledge, maintain collaborative awareness and create a common understanding. It can include the transfer of information needs and retrieved results, knowledge processing, and transfer in the team's knowledge repositories.

The purpose of this study is to identify any possible effect of users’ capabilities, such as learning styles, information or computer literacy, on their eye-movements during interaction with website interfaces, based on eye-mind hypothesis. This research was carried out through explanatory sequential mixed method. The research population was postgraduate students of Ferdowsi University of Mashhad. Sampling was done through sequential mixed method by sampling in each layer (multilevel). Finally, 20 subjects were selected. During data gathering, various tools and approaches such as Felder and Soloman Learning Style questionnaire, Information literacy and computer literacy questionnaire, interactive sessions using eye tracker were used. Descriptive and inferential statistics were used to analyze the data and test the hypotheses. The findings of this study showed a high correlation between computer literacy and information literacy of students. Eye movements of users during interaction with website interfaces was influenced by users’ capabilities. There was a relationship between information literacy and eye movements. The fixation duration was higher for subjects with higher information literacy than those with less information literacy. Subjects with a sensory learning style experienced a greater fixation count. Yet for those who had a sequential style, the fixation duration was longer, but the fixation count due to the linear look at the user interface was less than the global style. Subjects with lower information literacy and reflective learning style experienced more fixation counts. Those with active learning style and high information literacy had longer fixation durations. Subjects with lower levels of information literacy had more fixation count than those with higher information literacy. By examining the eye movements of the users in this research, it was determined that the user interface can affect the eye movement of the users. In the process of interaction, the users’ eye movements changed from more general elements to more complex elements. In general, it was found that the level of information literacy may direct users’ eye movements, while interacting with website, and affect how information is extracted from the interface. Also, the learning style affected the movement of the eyes of the users during the interaction.