The idea of automatic generation of data entry forms based on relational data models is a common and known idea that has been discussed day by day more than before according to the popularity of agile methods as well as improvement of programming tools in software development. One of the requirements of automation methods, whether in commercial products or relevant research projects, could be the concept of metadata as a mediator between database and data entry forms. The metadata usually includes some schemas and constraints of target database, which could be used as a model for automatic generation of data entry forms. However, the most metadata models proposed in relevant researches have simple and undetailed structure. In other words, only the initial requirements of data entry are covered in the proposed metadata. In this study, the main objective is to emphasize the structure of metadata to discuss its enrichment methods to cover more data entry requirements. In this regard, some parts of a metadata model are also presented for objectification of the ideas.

The role of designers is very important in relational database schema design. Relational database design tools help designers to design semantically correct and without anomaly databases; however, they are not good enough yet. There is a shortcoming in quality evaluation techniques for relational databases; therefore, database designers may not be able to systematically design better database schemes. In this paper, first, we define quality indicators for evaluating quality of relational database schemes based on software measurement techniques. Then we introduce a technique for evolutionary design of relational database schemes based on the model with which the quality of database schemes can be estimated. In this technique we try to minimize the number of states to be considered for finding the suitable answer.

Nowadays, standardized metadata for geospatial data is a key in sharing and finding information on the web and crucial in building Geospatial Data Infrastructure (GDI). The main objective of this paper was to develop a web-based metadata dissemination system for in-situ sensors based on, most importantly, interoperable, standard and open technologies introduced by Open GIS Consortium (OGC), namely Geography Markup Language (GML). In this research, at first a use case diagram was developed to demonstrate the user’s requirement. Then, an application XML schema based on user’s requirement was created. To build this schema, some GML schema documents (developed by Open GIS Consortium (OGC)) were imported into the application schema. System architecture was designed based on client/server model and a UML class diagram was also developed to present all classes and their attributes, operations and associations within the system. Implementation was conducted using GML, XML, XMLHTTP, DOM, ASP, and VBScript that brought out a web-network-based in-situ sensors metadata application. This application provided a user friendly interface to search and find sensor related information. Results showed that although GML and XML are powerful tools to build geo-metadata, it is important to note that GML document size may be a problem when dealing with huge amount of data.

In this work, we introduce MRE2C method for classifying multi relational data. Multi-relational data are stored on relational databases where they consist of multiple relations that are linked together by entity-relationship links. MRE2C creates multiple different feature subsets of relational database and then applies traditional classifiers as base classifiers. Final by using a proposed two-step combining classifier method, the results of base classifiers are combined. In first step, the proposed method uses local voting to create meta-features and then it learns meta learner to combine predication of base classifiers. Testing has been performed on two databases and six benchmark tasks. We compare our proposed method with other state-of-the-art multi relational classification methods which use different approaches to deal with multi relational setting. We showed that the proposed method achieves promising results in experiments.

Purpose: Designing metadata ontology model for semantic representation of Theses by using the SPAR (Semantic Publishing and Referencing) Ontologies.Method: This study was an applied form and two methods were used, Content Analysis and mapping. The metadata of 69 theses and dissertations on the National Library and Archive of Iran in three Databases: 1) Digital Library of National Library and Archive of Iran. 2) Rasa Software and 3) Ganj in the Iranian Research Institute for Information Science and Technology were selected modified and completed by mapping. On the other hand, by analyzing the entities of each SPAR ontology and suggesting another entity to the researcher, the checklist was formed. This checklist included classes, properties, and individuals. At last, by entering them into Protégé software version 5.5, the model of metadata ontology, MdOntTDs, was drawn.Findings: Findings identified deficiencies in the existence of four important metadata elements (subject, supervisor, advisor, and abstract) in RASA and NLAI Digital Library. Among the 18 SPAR Ontologies, the most entities were selected from FaBiO, FRAPO, and CiTO respectively. All entities of BiDO, BiRO, C4O, Fivestar, FR, FRBR, PO, PRO, PSO, and PWO were suitable for theses. 195 individuals from 6 SPAR Ontologies, 292 individuals labeled with MdTDs from theses, and 100 individuals labeled with SUNMdTDs were selected by the researcher and entered into the software. 1558 entities categorized by class, Properties (object, data, and Annotation), and individuals along with the description and definition of each entity were placed in the software, in the form of hierarchical and determining axioms for classes. And specifying domain and range for relationships. Finally, the RDF graph was drawn using the OntoGraf plugin, and the final Model, MdOntTDs was developed. this research has proposed three new types of metadata: 1) Except for the existing keywords, topics have been categorized and modeled up to three levels including 4 main categories, 16 subcategories, and many units. Each of these final topics has been related to “hasSubject” and “isSubjectOf” properties. 2) The research methods of Theses that were connected with “hasMethod” and “used in” properties. 3) The papers taken from Theses were also searched, as far as possible, and were connected with “hasJournalArticle” and “journalArticleOf” properties.Conclusion: This model, if implemented, can overcome keyword search limitations, the problem of linking and Data sharing on the web, and the inconsistency of data. In the software, classes and their related individuals are visible in the form of a hierarchical network in RDF triples, and the connection between entities with increasing access points promises deeper semantic searches. However, due to the absence or lack of tagged and linked data, usage of the some of selected entities is not possible.