Advances in Databases and Information Systems 23rd European Conference, ADBIS 2019, Bled, Slovenia, September 8–11, 2019, Proceedings: 23rd European Conference, ADBIS 2019, Bled, Slovenia, September 8–11, 2019, Proceedings
Abstract
This book constitutes the proceedings of the 23rd European Conference on Advances in Databases and Information Systems, ADBIS 2019, held in Bled, Slovenia, in September 2019.
The 27 full papers presented were carefully reviewed and selected from 103 submissions. The papers cover a wide range of topics from different areas of research in database and information systems technologies and their advanced applications from theoretical foundations to optimizing index structures. They focus on data mining and machine learning, data warehouses and big data technologies, semantic data processing, and data modeling. They are organized in the following topical sections: data mining; machine learning; document and text databases; big data; novel applications; ontologies and knowledge management; process mining and stream processing; data quality; optimization; theoretical foundation and new requirements; and data warehouses.
Cybercrimes are multiplying and spreading at an elusive speed commensurate with the emerging technologies of the fourth revolution. Their sophistication and users’ vulnerability to attacks catalyzes their success. Several surveys have been conducted to determine the factors favouring victimization. However, they can only be applied within a contextual framework since each ecosystem has particularities. Attempts in this direction are unavailable in Cameroon, where cybercrimes cost 12.2 billion CFA in 2021. This work consists of a semi-direct survey conducted in Cameroon in 2021 to provide the determinants explaining the most frequent cybercriminal techniques, the vulnerabilities left by the users, the most targeted population segments, and the socio-demographic and economic factors justifying this security fragility. The results relate to 370 questionnaires collected throughout the territory. According to descriptive statistics and the chi-square test, the explanatory variables of cybercrime victimization are gender, age, intellectual level, level of digital knowledge, level of Information and Communication Technology (ICT) proficiency, type of equipment used, the mobile and desktop operating system used, the possession of an anti-virus/anti-spam and the marital status. Within this work, we have identified threats and their drivers, and a theoretical framework has been provided with several stages that could be followed to contain cybercrime.
Big data processing is an urgent and unresolved challenge that originates from the intensive development of information technology. The recent techniques lose their effectiveness rapidly as the volumes of data increase. In this article, we will put down our vision of the basic approaches and models related to problem solving, based on processing large data volumes. This article introduces a two-stage decomposition of a problem, related to assessing management options. The first stage of our original approach implies a semantic analysis of textual information; the second stage is built around finding association rules in a database, processing them via mathematical statistics methods, and converting data and objectives to a vector. We suggest processing the collected news events by a semantic model, which describes their key features and interconnections between them in a specified subject area. The classification-based association rules allow assessing the likelihood of a particular event using a set chain of events. This approach can be applied through the analysis of online news in a specified market segment.
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