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Independent modeling of various modules of an information system (IS), and consequently database subschemas, may result in formal or semantic conflicts between the modules being modeled. Such conflicts may cause collisions between the integrated database schema of a whole IS and the modeled subschemas. In our previous work, we have proposed criteria and algorithms for identifying and resolving such conflicts so as to provide a consolidation of database subschemas with the integrated database schema with respect to various database concepts, such as domains, relation schemes, primary key constraints and referential integrity constraints. In this paper, we propose a new approach and algorithms for identifying conflicts and testing consolidation of subschemas with the integrated database schema against check constraints. The proposed approach is based on satisfiability modulo theory (SMT) solvers. Hereby, we propose the integration of SMT solvers into our MDSD tool, aimed at supporting a database schema integration process. [ABSTRACT FROM AUTHOR]

In this paper, we lay the foundation for an adaptation of the teaching process to the personality traits and academic performance of the university students enrolled in applied computer science and informatics (ACSI). We discuss how such an adaptation could be supported by an analytical software solution and present the initial version of this solution. In the form of a case study, we discuss the scores from a personality questionnaire that was administered to a group of university students enrolled in an introductory programming course at the Faculty of Technical Sciences, University of Novi Sad, Serbia. During a non-mandatory workshop on programming, the participants completed the 48-item short-scale Eysenck Personality Questionnaire-Revised (EPQ-R). By using various exploratory and analytical techniques, we inspect the student EPQ-R scores and elaborate on the specificities of the participating student group. As part of our efforts to understand the broader relevance of different student personality traits in an academic environment, we also discuss how the EPQ-R scores of students could provide information valuable to the process of improving student learning and performance in university courses in ACSI. [ABSTRACT FROM AUTHOR]

Different approaches to information system (IS) development are based on different data models. The selection of a data model for conceptual design, among other things, depends on the problem domain, the knowledge, and the personal preferences of an IS designer. In some situations, a simultaneous usage of different approaches to the conceptual database design and IS development may lead to the most appropriate solutions. In our previous research we have developed a tool that provides an evolutive and incremental approach to IS development, which is based on the form type data model. The approaches based on the Extended Entity-Relationship (EER) and class data models are broadly accepted throughout the community of IS designers. In order to support the simultaneous usage of approaches based on the form type, EER and class data models, we have developed the Multi-Paradigm Information System Modeling Tool (MIST). In this paper, we present a part of our MIST tool that supports EER approach to a database design. MIST components currently provide a formal specification of an EER database schema specification and its transformation into the relational data model, or the class model. Also, MIST allows generation of Structured Query Language code for a database creation and procedural code for implementing database constraints. In addition, Java code that stores and processes data from the database, may be generated from the class model. In this paper, we present the evaluation study of the MIST EER domain-specific language. Users' perceptions of language quality characteristics are used for the evaluation. [ABSTRACT FROM AUTHOR]

Background and objective Early childhood caries (ECC) is a potentially severe disease affecting children all over the world. The available findings are mostly based on a logistic regression model, but data mining, in particular association rule mining, could be used to extract more information from the same data set. Methods ECC data was collected in a cross-sectional analytical study of the 10% sample of preschool children in the South Bačka area (Vojvodina, Serbia). Association rules were extracted from the data by association rule mining. Risk factors were extracted from the highly ranked association rules. Results Discovered dominant risk factors include male gender, frequent breastfeeding (with other risk factors), high birth order, language, and low body weight at birth. Low health awareness of parents was significantly associated to ECC only in male children. Conclusions The discovered risk factors are mostly confirmed by the literature, which corroborates the value of the methods. [ABSTRACT FROM AUTHOR]