Nowadays, modern business includes acquisition and storing enormous data volumes, larger than ever before.Such data represent a significant value that an organization or a society can utilize to reach created goals and provide sustainable development.Unfortunately, a daily practice still intensively points out to the problem of a serious gap between the identified needs for knowledge, on one hand, and inability of the disciplines of Computer Science, Software Engineering, Informatics, and Data Science (CSSEIDS, for short), combined with the modern software technologies, to address such needs in an effective way, on the other hand.One of the important causes of such phenomenon is in a lack of strongly educated and interdisciplinary oriented experts showing an appropriate level of knowledge both in CSSEIDS, as well as in the disciplines of Business, Management and Economics, for a specific problem domain.In this paper, we address issues on how to come to more flexible and interdisciplinary oriented study models capable of producing various forms of digital managers and digital engineers, as a new profile of experts, ready to cope with digital economy and digital transformation in a modern society.Massive deployment of such experts is a way to significantly raise the level of organization maturity regarding capabilities for: information management, quality management, business processes, and big data analytics.

Hedge funds (HF) are actively managed investment vehicles employing diverse and often complex strategies.Accurate returns forecasting is essential for optimizing their performance and managing risk.This paper investigates the application of nonlinear dimensionality reduction (DR) methods in forecasting HF strategy performance, building upon prior work in financial time series analysis.We evaluate the effectiveness of Kernel Principal Component Analysis (KPCA), t-Distributed Stochastic Neighbor Embedding (t-SNE), Uniform Manifold Approximation and Projection (UMAP), and autoencoders on predictive performance of machine learning models.The extracted features are fed into several forecasting models, Support Vector Machine (SVM) with linear and nonlinear kernels, Neural Network (NN), and Extreme Gradient Boosting (XGB), to predict returns of five diverse HF investment strategies: Commodity Trading Advisors, Equity Long Short, Equity Market Neutral, Fixed Income Arbitrage, and Global Macro.The results demonstrate that nonlinear DR methods, particularly autoencoders, and KPCA combined with NN, significantly outperform other techniques.Our findings highlight the value of nonlinear transformations in enhancing predictive accuracy for HF returns time series.

Teaching programming poses countless challenges.One of them is determining the most effective notation to introduce coding concepts to beginners.This paper examines the merits and drawbacks of introducing block-based, text-based, or both notations at the same time when it comes to learning basic programming concepts.By comparing these approaches, the objective of this research is to clarify and assess the learning outcomes related to teaching beginners through different notations.In this empirical study, we report on a controlled experiment during short-term visits that promoted programming in primary schools.Our multinational study divided participants into three groups, one using block-based, one using text-based, and one using both notations.After training, the participants were solving practical programming assignments.The study results revealed that the participants' performance was not influenced by notation usage, as there was no statistical significance between the three groups.However, the performance outcomes were correlated with the duration of the sessions.Our findings from the controlled experiment suggest that educators can utilize different notations confidently while teaching beginners the first steps in programming.

The fourth industrial revolution introduces changes in traditional manufacturing systems and creates basis for a lot-size-one production.The complexity of production processes is significantly increased, alongside the need to enable efficient process simulation, execution, monitoring, real-time decision making and control.The main goal of our research is to define a methodological approach and a software solution in which the Model-Driven Software Development (MDSD) principles and Domain-Specific Modeling Languages (DSMLs) are used to create a framework for the formal description and automatic execution of production processes.In that way production process models are used as central artefacts to manage the production.In this paper, we propose a DSML which can be used to create production process models that are suitable for automatic generation of executable code.The generated code is used for automatic execution of production processes within a simulation or a shop floor.

We investigate the potential of using open data about higher education and research activities as a basis for constructing university rankings at the national level. In our case study, open data from the Ministry of Education, Science, and Technological Development of Serbia served as a foundation for deriving indicators of university performance and calculating ranks of universities from Serbia. In addition to reviewing notable international rankings of universities, we extracted the international standings of universities from Serbia and discussed the national university rankings that were generated during our investigation.

Contemporary tools aimed at information system (IS) development often use models to generate system implementation.Starting from an IS model, these tools commonly generate database implementation schema as well as code for generic CRUD operations of business applications.On the other hand, at the level of platform-independent models (PIMs) there is a lack of support for specification of more complex functionalities associated with events.In this paper, we present an approach aimed at specification of events at the level of PIMs.We introduce new concepts to describe context in which an event may occur, while we use our IIS*CFuncLang language to define event business logic.We also developed adequate transformations to generate executable program code from these specifications.

An important phase of a data-oriented software system reengineering is a database reengineering process and, in particular, its subprocess -a database reverse engineering process.In this paper we present one of the model-to-model transformations from a chain of transformations aimed at transformation of a generic relational database schema into a form type data model.The transformation is a step of the data structure conceptualization phase of a model-driven database reverse engineering process that is implemented in IIS*Studio development environment.

Reengineering of an existing information system can be carried out: to improve its maintainability, to migrate to a new technology, to improve quality or to prepare for functional enhancement. An important phase of a data-oriented software system reengineering is a database reengineering process and, in particular, its subprocess - a database reverse engineering process. The reverse engineering process contains two main phases: data structure extraction and data structure conceptualization. In the paper we present a blueprint of a model-driven approach to database reengineering process that is one of the results of our research project on model-driven intelligent systems for software system development, maintenance and evolution. Within that process hereinafter we focus on the data structure conceptualization process and propose a model-driven approach to data structure conceptualization. Proposed process is based on model-to model transformations implemented by means of Atlas Transformation Language.

In this paper we present a Multi-Paradigm Information System Modeling Tool (MIST) that supports Extended Entity-Relationship (EER) approach to database design.MIST components currently provide a formal specification of EER database schema specification and its transformation into the relational data model, or the class model.Also, MIST allows generation of Structured Query Language (SQL) code for 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 describe our solution in a competition that required performing data mining to identify key risk factors for the State Fire Service of Poland.The goal was to create an ensemble of Naive Bayes classifiers that could predict incidents involving firefighters, rescuers, children, or civilians.To this end, we first created a single Naive Bayes classifier and then partitioned the set of attributes used in that classifier.The attribute subsets were used to create new Naive Bayes classifiers that would form an ensemble, which generally performs better than both the single classifier and ensemble obtained by searching over all attributes considered when creating the single classifier.The application of our approach yielded a solution that ranked third in the competition.

Abstract In this paper we consider an approach to developing complex database schemas. Apart from the theoretical model of the approach, we also developed a CASE tool named Integrated Information Systems*Case, R.6.2 (IIS*Case) that supports the practical application of the approach. In this paper the basis of our approach to the design and integration of database schemas and ways of using IIS*Case is outlined. The main features of a new version of IIS*Case, developed in Java, are described. IIS*Case is based on the concept of ‘form type’ and supports the conceptual modelling of a database schema, generating subschemas and integrating them into a relational database schema in 3NF. IIS*Case provides an intelligent support for complex and highly formalized design and programming tasks. Having an advanced knowledge of information systems and database design is not a compulsory prerequisite for using IIS*Case. IIS*Case is based on a methodology of gradual integration of independently designed subschemas into a database schema. The process of independent subschema design may lead to collisions in expressing real‐world constraints. IIS*Case uses specialized algorithms for checking the consistency of constraints embedded in a database schema and its subschemas. This paper briefly outlines the application of the process of detecting collisions, and actions the designer may take to resolve them. Copyright © 2007 John Wiley & Sons, Ltd.