Abstract
The General Data Protection Regulation 2016/679 (GDPR) is a set of legal rules to attain the privacy of people in the handling of their personal data and the movement of such data across countries. When those rules are considered in the operation of information systems, the one becomes attainable for legal approval within that scope. This paper presents a model we are developing to help enterprises do align their information system with the GDPR requirements. The model shall serve the purpose of analyzing the enterprises in what concerns the use of the subject’s personal data, allowing to capture and improve data protection capabilities placed in the GDPR. The main issue of our approach is to set a baseline to define the requirements for establishing, implementing, maintaining and continually improving data protection management system on organizations.

Abstract
We present a novel way to link business process model with data protection risk management. We use established body of knowledge regarding risk manager concepts and business process towards data protections. We try to contribute to the problems that today organizations should find a suitable data protection model that could be used in as a risk framework. The purpose of this document is to define a model to describe data protection in the context of risk. Our approach including the identification of the main concepts of data protection according to the scope of the with EU directive data protection regulation. We outline data protection model as a continuous way of protection valued organization information regarding personal identifiable information. Data protection encompass the preservation of personal data information from unauthorized access, use, modification, recording or destruction. Since this kind of service is offered in a continuous way, it is important to stablish a way to measure the effectiveness of awareness of data subject discloses regrading personal identifiable information.

Abstract
Sales forecasting in the presence of Missing Data poses significant challenges, particularly for short time series where limited observations amplify the impact of incomplete records. This study analyzes a real-world transactional dataset (2021-2024) to predict quantities and prices for 2025. We classify missingness patterns and mechanisms (MCAR, MAR, MNAR) to inform the selection of imputation strategies. We evaluate techniques including MICE, Mean, KNN, and Linear Regression under simulated missingness rates, with KNN emerging as the most robust for the MAR mechanism. Regarding very short-term series predictions, the naive forecast Max2 (maximum of the last two observed values) outperformed moving averages. The results highlight the importance of mechanismaware imputation and domain-tailored forecasting in sparse datasets. This work presents a practical framework for businesses to effectively utilize incomplete sales data.

Abstract
Relational databases are supported by very well established models. However, some neglected problems can occur with the join operator: semantic mistakes caused by the multiple access path problem and faults when connection traps arise. In this paper we intend to identify and overcome those problems and to establish rules for relational data denormalization. Two denormalization forms are proposed and a case study is presented. © 2019, Springer Nature Switzerland AG.

Abstract
A unified view of the Information System (IS) design is essential for dealing with complexity. However, the literature proposes many denominations, depending on the layer, methodology, framework, or tool. This multitude of approaches does not allow a holistic view of the system. Besides, in Information Systems, the search for good practices in design is still a relevant issue. A subset of essential Unified Modeling Language (UML) diagrams is chosen to create a broad view of the IS. CRUD matrix is one of the preferred approaches to articulate the sub-systems of applications and data. Axiomatic Design (AD) provides rules for the im-provement of the IS design. This work presents a method to create object-oriented elements based on the CRUD matrix aligned with the business strategy. An integrated student-based case study on logistics is provided. In the discussion, a new IS architect role is proposed supported by the CRUD/AD method.

Abstract
The dataset reports an up-to-date overview of the selective biowaste collection with a focus on food waste and organic kitchen waste across 308 municipalities in Portugal, to assess the compliance with the EU Waste Framework Directive that made biowaste collection mandatory from 1st January 2024. Data were collected through a structured survey sent to the totality of the municipalities, complemented by systematic research in secondary official sources such as municipal web-sites, reports and statistical data. The questionnaire covered aspects such as coverage, collection models (nearby bring points, door-to-door, co-collection), sector-specific deployment (household collection, non-domestic collection), operational characteristics, and performance indicators (capture rates, cost per tonne). The dataset was structured and validated through cross-checking the multiple sources assessed, prioritising direct municipal questionnaire responses. It includes disaggregated data at a municipality level, including detailed information on the characteristics and efficiency of the initiatives, when available. The database allows the cross-comparison across Portuguese regions and potentially with other international systems, in terms of biowaste collection strategies with focus on food waste and organic kitchen waste. Municipalities in Portugal have been carrying out pilot experiences within their territories, but there is no systematic assessment of what has been carried out nor the results obtained. Given the limited available data, this dataset provides a valuable resource for policy design and further research on biowaste management initiatives to further assess their efficiency and adaptability to different municipal realities at a national and even European level. (c) 2025 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)