Cyber-Physical Systems (CPS) are a prominent component of the modern digital transformation, which combines the dynamics of the physical processes with those of software and networks. Critical infrastructures have built-in CPS, and assessing its risk is crucial to avoid significant losses, both economic and social. As CPS are increasingly attached to the world’s main industries, these systems’ criticality depends not only on software efficiency and availability but also on cyber-security awareness. Given this, and because Failure Mode and Effect Analysis (FMEA) is one of the most effective methods to assess critical infrastructures’ risk, in this paper, we show how this method performs in the analysis of CPS threats, also exposing the main drawbacks concerning CPS risk assessment. We first propose a risk prevention analysis to the Communications-Based Train Control (CBTC) system, which involves exploiting cyber vulnerabilities, and we introduce a novel approach to the failure modes’ Risk Priority Number (RPN) estimation. We also propose how to adapt the FMEA method to the requirement of CPS risk evaluation. We applied the proposed procedure to the CBTC system use case since it is a CPS with a substantial cyber component and network data transfer.

Conceptual modeling describes the physical or social aspects of the world abstractly, encompassing the interpretation of data production, gathering, visualization, and analysis. The quality of the data analysis system will limit the excellence of any decision-making process. Thus, accurately specifying the physical data model is essential. The primary goal of our work is to compare tools that can create this physical model. We recognize several types of data models, but we only include the relational data model. We evaluate free and commercial data modeling tools. But it is challenging to decide how to compare them and which elements are crucial. We propose a new approach for software tools’ evaluation based on the Business Readiness Rating (BRR) model and the OSSpal evaluation methodology. In this work, we show that this new methodology can be tailored to the needs of each individual developer or team, thus providing proper and meaningful results. Also, by applying this hybrid approach to the evaluation of data modelling tools, we show it can robustly handle the bias from lesser relevant evaluation categories.

The exponential growth in data generation has become a ubiquitous phenomenon in today’s rapidly growing digital technology. Technological advances and the number of connected devices are the main drivers of this expansion. However, the exponential growth of data presents challenges across different architectures, particularly in terms of inefficient energy consumption, suboptimal bandwidth utilization, and the rapid increase in data stored in cloud environments. Therefore, data reduction techniques are crucial to reduce the amount of data transferred and stored. This paper provides a comprehensive review of various data reduction techniques and introduces a taxonomy to classify these methods based on the type of data loss. The experiments conducted in this study include distinct data types, assessing the performance and applicability of these techniques across different datasets.

Distributed databases and data transformation mechanisms are highly relevant to Business Intelligence and Data Analytics. Most enterprise systems today have multiple and distributed databases, and the growing dissemination of Edge-Oriented architectures is driving this trend across many industries and business domains. The Entity-Relationship (ER) model is fundamental to modeling complex enterprise systems, but it has shortcomings. In particular, the ER model cannot represent data transport between different locations (or databases) of a system, nor can it conceptually express data transformation operations, such as aggregate and line functions, that are standard in data analytics. Therefore, we propose ER+, an extension of the ER model, where data distribution, data transport, data transformation, and information generation for distributed operational and analytical systems can be visually identified. The new ER+ representation has another important benefit, it provides the basis for transforming conceptual models into physical implementations of distributed data-oriented systems. This work introduces new concepts in ER modeling and illustrates their application. The TPC-H use case is also used to demonstrate the the practicality of the ER+ model.