Abstract
Low-code application development as proposed by the OutSystems Platform enables fast mobile and desktop application development and deployment. It hinges on visual development of the interface and business logic but also on easy integration with data stores and services while delivering robust applications that scale. Data integration increasingly means accessing a variety of NoSQL stores. Unfortunately, the diversity of data and processing models, that make them useful in the first place, is difficult to reconcile with the simplification of abstractions exposed to developers in a low-code platform. Moreover, NoSQL data stores also rely on a variety of general purpose and custom scripting languages as their main interfaces. In this paper we report on building a polyglot data access layer for the OutSystems Platform that uses SQL with optional embedded script snippets to bridge the gap between low-code and full access to NoSQL stores. © IFIP International Federation for Information Processing 2020.

Abstract
Transactional isolation is a challenge for polystores, as along with the limited capabilities of each datastore, we have to contend with their sheer diversity. However, transactional isolation is increasingly desirable as a variety of datastores are being sought after for roles that go beyond data lakes. Transactional guarantees are also relevant for reliability at scale. In this paper, we propose that transactional isolation in polystores can be achieved by leveraging the query engine, i.e., basing some of the responsibilities of a traditional transactional storage manager (TSM) on the query language itself. This has the key advantage of greatly simplifying design and implementation, as it doesn’t need to be re-invented for each datastore, and should increase performance, by taking advantage of dynamic query optimization where available. We demonstrate the feasibility of the proposal with a simple proof-of-concept and experiment. © 2021, Springer Nature Switzerland AG.

Abstract
This paper presents a data market aimed at trading energy forecasts data. The system architecture is built using blockchain as a service, allowing access to data streams and establishing a distributed settlement between stakeholders. Energy Forecasts data is presented as the commodity traded in the market, whose settlement is provided through the blockchain on the basis of the extracted value provided by market stakeholders. Our proposal allows market stakeholders to acquire energy forecasts and pay according to the data accuracy, solving the confidentiality problem of freely sharing data. A data quality reward is introduced, steering the compensation sent to market participants. The data market design is presented and an evaluation campaign is performed, showing that the data market produced functionally valid results in comparison with the results achieved with a central simulated approach. Moreover, results show that the data market architecture is able to scale.

Abstract
As blockchains go beyond cryptocurrencies into applications in multiple industries such as Insurance, Healthcare and Banking, handling personal or sensitive data, data access control becomes increasingly relevant. Access control mechanisms proposed so far are mostly based on requester identity, particularly for permissioned blockchain platforms, and are limited to binary, all-or-nothing access decisions. This is the case with Hyperledger Fabric's native access control mechanisms and, as permission updates require consensus, these fall short regarding the flexibility required to address GDPR-derived policies and client consent management. We propose SDAM, a novel access control mechanism for Fabric that enables fine-grained and dynamic control policies, using both contextual and resource attributes for decisions. Instead of binary results, decisions may also include mandatory data transformations as to conform with the expressed policy, all without modifications to Fabric. Results show that SDAM's overhead w.r.t baseline Fabric is acceptable. The scalability of the approach w.r.t to the number of concurrent clients is also evaluated and found to follow Fabric's.

Abstract
The increasing adoption of the Digital Twin concept and technology for managing complex physical assets has led to the emergence of Digital Twin Ecosystems, where interconnected digital twins generate additional value. However, ensuring seamless data sharing and interoperability among diverse systems presents significant challenges. Although research on digital twin architectures has advanced, gaps remain in addressing data governance, security, and stakeholders' trust. This study performs a comprehensive literature review to investigate architectural solutions to overcome challenges in digital twin ecosystems. The findings identify key requirements such as interoperability, governance, and data management, emphasizing the role of Data Spaces as enablers of secure data sharing. By structuring the requirements for digital twin ecosystem architectures, this paper identifies gaps suggesting future research on scalable and sustainable digital twin ecosystem implementations. These insights are expected to contribute to the development of frameworks that integrate technical advances with organizational and regulatory considerations, ultimately fostering the adoption of digital twin ecosystems across industries.

Abstract
A common pattern for enterprise applications, particularly in small and medium businesses, is the reliance on an integrated traditional relational database system that provides persistence and where the relational aspect underlies the core logic of the application. While several solutions are proposed for scaling out such applications, database replication is key if the relational aspect is to be preserved. However, it is worrisome that because proposed solutions for database replication have been evaluated using simple synthetic benchmarks, their applicability to enterprise applications is not straightforward: the performance of conservative solutions hinges on the ability to conveniently partition applications while optimistic solutions may experience unacceptable abort rates, compromising fairness, particularly considering long-running transactions. In this thesis, we address these challenges. First, by performing a detailed evaluation of the applicability of database replication protocols based on conservative concurrency control to enterprise applications. Results invalidate the common assumption that real-world databases can be easily partitioned. Then, we tackle the issue of unacceptable abort rates in optimistic solutions by proposing a novel transaction scheduler, AJITTS, which uses an adaptive mechanism that by reaching and maintaining the optimal level of concurrency in the system, minimizes aborts and improves throughput.;Um padrão comum no que toca a aplicações empresariais, particularmente em pequenas e médias empresas, é a dependência de um sistema de base dados relacional integrado que garante a persistência dos dados e no qual o aspeto relacional é parte integral da logica da aplicação. Embora várias soluções tenham sido propostas para dotar este tipo de aplicações de escalabilidade horizontal, a replicação de base de dados é a solução se o aspeto relacional deve ser preservado. No entanto, é preocupante que, dado que as soluções existentes para replicação de base de dados têm sido avaliadas utilizando testes de desempenho sintéticos e simples, a aplicabilidade destes a aplicações empresariais não é directa: o desempenho de soluções conservadoras está intimamente ligado à capacidade de particionar a aplicação convenientemente, enquanto que soluções optimistas podem sofrer de taxas de insucesso inaceitáveis o que compromete a equidade das mesmas, em particular no caso de transações especialmente longas. Nesta tese, abordamos estes desafios. Primeiro, através de uma avaliação detalhada da aplicabilidade de protocolos de replicação de base de dados baseados em controlo de concorrência conservador a aplicações empresariais. Os resultados obtidos invalidam o pressuposto comum de que bases de dados reais podem ser facilmente particionadas. Assim sendo, abordámos o problema das possíveis taxas de insucesso inaceitáveis em soluções optimistas propondo um novo escalonador de transações, o AJITTS, que utiliza um mecanismo adaptativo que ao atingir e manter o nível ótimo de concorrência no sistema, minimiza a taxa de insucesso e melhora o desempenho do mesmo.