Purpose: This article aimed to investigate the extensibility of document retrieval in the form of writing words continuously and separately in three Persian scientific databases, Magiran, SID and RICEST. Methodology: The research was conducted through the content analysis method. The statistical population consisted of existing documents in Persian scientific databases. Using Cochran's formula, 432 documents were chosen with the simple stratified sampling. The research tool was a researcher-made checklist contained examples (keywords) that could have the possibility of occurrence of writing words continuously and separately. Findings: The results revealed that there was a difference between the retrieval capability of Persian scientific databases with each form of writing words continuously and separately. In addition, findings revealed that while searching in scientific databases, only 5. 3 % of documents can be retrieved with each form of writing words and in contrast 94. 7% of documents were retrieved only with the same state of recorded examples of documents. Also, the comparison of the writing form of the examples in the PDF version of the documents with the information provided by the same documents in the Persian Scientific Databases showed that writing form of a significant portion of the examples of documents has been altered from writing words semi-spacely to separately and continuously. Conclusion: Present study revealed that the studied Persian Scientific Databases have not addressed the challenges of writing words continuously and separately in a comprehensive way in order to improve recall of search results; this undesirable situation may lead to loss documents which are saved in databases with other forms of writing.

Iran scientific information database (Ganj) which includes almost one million scientific records provides the search opportunity in dissertations, domestic scientific journals, articles, conferences, research projects, and governmental reports. A large number of researchers meet the needs of their scientific and research resources from Ganj database daily. Users’ needs and behaviors are variant and understanding it helps system administrators to use different strategies to manage the better databases and provide efficient services to users. One way to understand users’ needs is to cluster them based on their behavior and identify the features of each cluster. This study aims to cluster the users based on the analysis of their search behavior using the LRFM model. In this study, the search log data of Ganj users were collected for three months, the LRFM attributes were calculated, and then the K-means algorithm was applied to them. The optimal number of clusters was calculated based on different criteria. Based on customer value matrix the results of customer clustering users in four groups are efficient, suspicious, unreliable, and intermittent and based on customer loyalty Marcus users are categorized in loyal, potential, insecure and newcomers.

Introduction: Seeng as web has become an important source of information and search vehicle to retrieve information on the web, it is important to identify the capabilities and limitations of these tools. this study endeavored to compare the performance of search engines and specialized medical databases based on four categories of search criteria, display, navigation and user-friendliness in the field of recovery of common cancers. Methods: To conduct this descriptive study, the comparison was madebased on the data collected and their content description, the performance of search engines and specialized medical databases in the recovery of common cancers was compared. Five free and controlled keywords for common cancers were selected from the Medical Subject Headings (MeSH) and were searched in five specialized medical search engines as well as three specialized medical databases. To evaluate the performance of search engines and specialized medical databases, a researcher-made checklist was employed and data were described through descriptive statistics (frequency, percentage, & average) and Excel software. Results: In terms of search engine and database performance as well as search and display criteria, the Trip database search engine and PubMed-Embase database scored the highest. As to Trip database and WebMD search engines and navigation criteria Embase database scored the highest. In terms of the search criteria Medlineplus search engine and Embase database scored the highest. Conclusion: According to the results of the study, the performance of search engines and specialized medical databases in recovering common cancers was different based on different criteria and users can use them according to their needs.

The main challenge of the star sensor as a real-time sensor is the execution time of attitude determination. Attitude determination using the star sensor includes five main steps: star catalog and identification algorithm selection, database construction, image processing, star identification and finally, attitude determination. Star identification consists of the implementation of the selected identification algorithm on the field of view stars and database searching. in the process of attitude determination using the star sensor, database searching is the most time-consuming part. This paper deals with three methods for database searching and surveys the search time for each of the presented algorithms also the consideration of using them as the database search methods for the star sensor. The methods are the ternary search technique, Fibonacci search technique, and interpolation search technique. The presented algorithms have not been used so far in the star sensor database searching. To survey the influence of the database dimensions on the identification time, each of the presented methods was studied using seven databases with different dimensions. The results show the superiority of the interpolation search method.

The main object of this study is analyzing the components and indicators of the scholarly Publication System in the scholarly Publication databases in terms of access, communication, control, infrastructure, language, materials (information resources), support, technology, economics, evaluation, education, ethics and their characteristics. The research community has been identified by content analyzing and 73 databases were extracted, based on the frequency and approval of experts the sample limited to 12 scholarly publication databases. These include the ArXiv, DOAJ, Elsevier, Springer, Google Scholar, PubMed, Nature, Web of Science, Scopus, National Institutes of Health (NIH), SPARC (Scholarly Publishing and Academic Resource Coalition), and Amazon databases. The checklist designed to study these databases is taken from the scholarly Publication System dimensions. Research findings show that all components have been considered in publication databases and the difference between them returned in scholarly Publication indicators. however, according to the type, age and general policy of the databases, some indicators of components, Like education, economics, and information resources has not received enough attention, in all databases, some components such as technology, support and control and their indicators have an equal importance.

The present research was carried out with the aim of explicating the major writing and semantic problems of Persian language when using data environments and determining the degree of compatibility and attention to these features in Persian databases. This research is of survey analytical type being conducted through direct observation. Having reviewed the related literature, we kept a checklist of search keywords. Each of these keywords was searched in the databases under study, such as Iranian Research Institute for Information Science and Technology, Regional Centre for Information Science and Technology, NoorMagaz, and Scientific Information Database affiliated with Jahad Daneshgahi, and the number of retrieved findings was recorded. Some of the writing and semantic features of Persian language contribute to problems associated with retrieving information from the selected databases. Some of these features include connected and disconnected forms of writing of derivative, compound, and derivative-compound words, diversity of plural forms, loanwords and their equivalents in writing as well as polysemy, homonymy, etc., in semantics. For instance, retrieving different results for various writing forms of the keywords « فناورى و فن آورى » as derivative-compound words or « پتاسیوم و پتاسیم » as various forms of recording words, or retrieving different findings for keywords « دریاى خزر، دریاى مازندران و دریاى کاسپین » as well as lack of their appropriate coverage as synonymous words and giving the user information about it in order to improve the exploration process, for it has negative effects on search and retrieval process. Findings indicated that Persian databases do not pay adequate attention to writing and semantic features of Persian language, and disregard many of its features in searching and retrieving information. In connection with the impact of these features on the interaction of users with databases, Persian-speaking users’ need for native exploration tools and databases designed in accordance with the features of their own language have become more and more urgent. The present research has examined the ability of Persian databases in covering some of the features of this language, which have a noticeable impact on the process of searching and retrieval, pinpointing the weak points and strengths of these databases. The results of the present research could be utilized to improve the performance of the above-mentioned databases.

in scientific documents are rich sources of information. The first step in retrieving information from such figures is to build a valid figure database. To this end, we developed a system for generating figure database from scholarly Persian documents, in large scale. The first step is to parse files and extract figures and their corresponding descriptions. There are two general approaches for extracting figures from documents. One is based on image processing methods and another is based on processing the file primitives. The focus of this paper is on latter one. This approach is shown to be a better choice for the search engines because of its speed and scalability properties. We propose a structure based method that extracts the figures and their descriptions by analyzing the file layout. This information is saved in a database with a specific structure and is indexed for retrieval in the search engine. The proposed algorithm was implemented in Python programming language. As a benchmark we used the basic method in the literature which is based on the processing PDF file. We employed the proposed method in a case study on Iran scientific information database (Ganj). In this regard, 150 scientific documents were randomly chosen from Ganj database and analyzed using two mentioned methods. Based on our experimental results, the proposed method is more efficient than the basic method especially for Persian documents. There are many unanswered challenges for Persian documents when using the basic method. The number of noise images resulted from the basic method is high and Persian text extracted is not well organized. Our proposed method overcomes some of these drawbacks and is recommended for generating figure database from scientific Persian documents. The proposed method is able to correctly extract about 40% of the images with their corresponding descriptions which is 10% better than the basic method.