There are now multiple proposals for Conflict-free Replicated Data Types (CRDTs) in SQL databases aimed at distributed systems. Some, such as ElectricSQL, provide only relational tables as convergent replicated maps, but this omits semantics that would be useful for merging updates. Others, such as Pg\_crdt, provide access to a rich library of encapsulated column types. However, this puts merge and query processing outside the scope of the query optimizer and restricts the ability of an administrator to influence access paths with materialization and indexes. Our proposal, CRDV, overcomes this challenge by using two layers implemented as SQL views: The first provides a replicated relational table from an update history, while the second implements varied and rich types on top of the replicated table. This allows the definition of merge semantics, or even entire new data types, in SQL itself, and enables global optimization of user queries together with merge operations. Therefore, it naturally extends the scope of query optimization and local transactions to operations on replicated data, can be used to reproduce the functionality of common CRDTs with simple SQL idioms, and results in better performance than alternatives.

Performance of transactional systems is degraded by update hotspots as conflicts lead to waiting and wasted work. This is particularly challenging in emerging large-scale database systems, as latency increases the probability of conflicts, state-of-the-art lock-based mitigations are not available, and most alternatives provide only weak consistency and cannot enforce lower bound invariants. We address this challenge with Multi-Record Values (MRVs), a technique that can be layered on existing database systems and that uses randomization to split and access numeric values in multiple records such that the probability of conflict can be made arbitrarily small. The only coordination needed is the underlying transactional system, meaning it retains existing isolation guarantees. The proposal is tested on five different systems ranging from DBx1000 (scale-up) to MySQL GR and a cloud-native NewSQL system (scale-out). The experiments explore design and configuration trade-offs and, with the TPC-C and STAMP Vacation benchmarks, demonstrate improved throughput and reduced abort rates when compared to alternatives.

Transactions have been a key issue in database management for a long time and there are a plethora of architectures and algorithms to support and implement them. The current state-of-the-art is focused on storage management and is tightly coupled with its design, leading, for instance, to the need for completely new engines to support new features such as Hybrid Transactional Analytical Processing (HTAP). We address this challenge with a proposal to implement transactional logic in a query language such as SQL. This means that our approach can be layered on existing analytical systems but that the retrieval of a transactional snapshot and the validation of update transactions runs in the server and can take advantage of advanced query execution capabilities of an optimizing query engine. We demonstrate our proposal, TiQuE, on MonetDB and obtain an average 500x improvement in transactional throughput while retaining good performance on analytical queries, making it competitive with the state-of-the-art HTAP systems.

There is an increasing demand for stateful edge computing for both complex Virtual Network Functions (VNFs) and application services in emerging 5G networks. Managing a mutable persistent state in the edge does however bring new architectural, performance, and dependability challenges. Not only it has to be integrated with existing cloud-based systems, but also cope with both operational and analytical workloads and be compatible with a variety of SQL and NoSQL database management systems. We address these challenges with AIDA-DB, a polyglot data management architecture for the edge and cloud continuum. It leverages recent development in distributed transaction processing for a reliable mutable state in operational workloads, with a flexible synchronization mechanism for efficient data collection in cloud-based analytical workloads.

Oak decline is a complex syndrome that increasingly affects the survival of oak species worldwide. Spectral-based physiological plant traits (PTs) indicators have successfully quantified pigment degradation and vegetation structure. The specific response of PTs to decline diseases has been answered by using high spectral- and spatial-resolution hyperspectral and thermal sensors onboard airborne platforms in the context of oak disease detection and monitoring. However, the capacity for early detection using a limited number of spectral bands, with miniaturised sensors of lower sensitivity, is unknown, seeking outstanding operability through cost-effective platforms, which is critical to detect irreversible damage timely. We evaluate the use of multispectral and thermal imagery onboard a drone together with a 3-D radiative transfer model (RTM) approach to assess a predictive symbolic classification model of Phytophthora-infected holm and cork oak areas located in Ourique (southern Portugal). The field survey comprised more than 390 trees across disease severity classes with varying disease-incidence levels and species. The classification model showed up to 83% overall accuracy ($k=0.46$) for decline detection. The proposed model allowed us to efficiently identify the physiological state of the forest canopy so that disease progression can be detected and mapped rapidly, which is essential for fighting oak decline when silvicultural practices, such as tree removal and clearing, can still prevent the spread of dieback processes. Therefore, our study demonstrates that the tandem use of multispectral and thermal sensors, together with an RTM and AI approach, helps us predict the impact of this particularly damaging disease on oak trees.

One key issue to design parallel applications that scale on multicore systems is how to overcome the memory bottleneck. This paper presents a study of the impact of data structure layouts in locality of memory references, providing insights on strategies to ameliorate the memory bottleneck. The paper compares the performance of Java and C++ STL collections and presents the impact of locality of reference optimisations in a molecular dynamics simulation case study. The case study shows that the selected data structure layout has impact on single core performance, becoming a critical factor in the application scalability on multicore systems. Moreover, data collections provided in the Java language compromise performance due to pointer chasing and lack of spatial locality of memory references.