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.

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.

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
Data intensive applications increasingly make use of multiple data stores in the cloud, providing a diversity of data and query models, as well as durability and scale trade-offs. However, this has a severe impact on reliability, as the key fault-tolerance mechanism for database systems, i.e. ACID transactions, is no longer available. Although it is possible to implement transactions without changes to the database servers, this either requires a proxy server, which compromises scale and availability, or a client-side layer that changes the data schema, excludes legacy applications, and adds significant overhead. We address this challenge with a proposal to delegate functionality from a client-side transactional layer to a server-side query engine such that compatibility with legacy applications is restored. We implemented a proof-of-concept and show that it significantly improves performance for analytical applications.

Abstract
Diversity is crucial in systems that tolerate Byzantine faults. Traditionally, system builders have relied on standardized interfaces (e.g., POSIX for operating systems) to obtain off-the-shelf components or on n-version programming for custom functionality. Unfortunately, standardized alternatives are rare, and the independent development of multiple versions of the same software is costly and justified only on the most critical applications. In this paper, we show that a limited and focused use of LLMs for translation opens up the possibility of leveraging the existing diversity in functionally equivalent but non-standardized components. Specifically, we show that LLMs can produce functionally correct database query translations with minimal guidance and adapt to diverse data models and query contexts, enabling the use of radically different database models, both SQL and NoSQL, together in a Byzantine fault-tolerant replicated system. We outline an approach to achieve this in practice and discuss future research directions.