Multi-tenant DBMSs are used by cloud providers for their Database-as-a-Service products. They could be single-node DBMSs installed in virtual machines, SQL-on-Hadoop systems or classic parallel relational DBMSs running on top of a shared-nothing or shared-disk architecture. For a cloud provider, it is interesting to measure these systems’ capability of dealing with multi-tenant workloads, i.e., taking advantage of the statistical multiplexing to obtain economic gain while being attractive by providing a good quality of service and a low bill to the tenants. In this paper, we present MTD-DS benchmark (with MTD for Multi-Tenant parallel DBMSs and DS for Decision Support). MTD-DS extends TPC-DS by adding a multi-tenant query workload generator, a performance Service Level Objectives generator, configurable Database-as-a-Service pricing models, and new metrics to measure the potential capability of a multi-tenant parallel DBMS in obtaining the best trade-off between the provider's benefit and the tenants’ satisfaction. Example experimental results have been produced to show the relevance and the feasibility of the MTD-DS benchmark.

A recent trend among major organisations is to release their datasets in the cloud over various Database-as-a-Service (DBaaS) providers’ premises, creating a use case for multi-cloud querying. As identified in the literature, middlewares with such capabilities should quote the monetary cost and the response time of the queries in order to gain the trust of their users, and also optimise the queries so as to avoid cost overruns and meet the quotations. Considering those requirements, this paper introduces an accurate cost model and an efficient execution plan search strategy for dealing with large-scale multi-cloud queries. The former is an ensemble learning stack leveraging online machine learning models, and the latter is a randomised method inspired by iterative improvement. We evaluated our middleware over simulated providers by using the Join Order Benchmark. Experiments showed that the cost model manages to correct the estimations from the providers. The randomised strategy can produce more efficiently execution plans that yield better performances and a lower monetary cost compared to an exhaustive approach from previous work.

This work is a companion reproducibility paper that presents a framework to reproduce our previous experiments and results reported in Werneck et al. (2021). In that previous paper, we introduced a systematic mapping process of points-of-interest (POI) recommendation methods and provided a uniform evaluation methodology based on metrics covering different aspects besides accuracy. Due to the lack of reproducible and extensible benchmarks, our work introduces a reproducibility framework for POI methods based on a collection of Python software libraries and a Docker image. Our proposal is composed of: (1) a package to perform a protocol that reproduces our systematic mapping process Werneck et al. (2021), containing all collected data, insightful views on current advances and opened challenges; and (2) an extensible benchmark to perform a protocol to reproduce experimental evaluations on POI recommendation, considering different datasets, metrics, and the strongest baselines in the literature. This work also demonstrates all processes required to instantiate its framework. Moreover, our work can be considered at least weakly reproducible, since we were able to reproduce the results of the previous paper, leading us to the same conclusions.

The problem of ontology matching consists of finding the semantic correspondences between two ontologies that, although belonging to the same domain, have been developed separately. Matching methods are of great importance since they allow us to find the pivot points from which an automatic data integration process can be established. Unlike the most recent developments based on deep learning, this study presents our research on the development of new methods for ontology matching that are accurate and interpretable at the same time. For this purpose, we rely on a symbolic regression model specifically trained to find the mathematical expression that can solve the ground truth accurately, with the possibility of being understood by a human operator and forcing the processor to consume as little energy as possible. The experimental evaluation results show that our approach seems to be promising.

The problem that we address here is given n job seekers and n job offers, where each job seeker has ranked all job offers in order of preference given by a suitability function, and vice versa; the goal is to compute the minimum set of skills to be offered to the job seekers, so that a) a global stable marriage between job seekers and potential employers can be reached, and b) the degree of satisfaction for that stable marriage might be maximum. To achieve this goal, we have designed an iterative algorithmic solution that can be solved in polynomial time. Additionally, we illustrate our solution with an use case based on a numerical example.

The current paper describes our research towards a cloud infrastructure for the universal access and interaction with a number of services implementing methods for enriching, matching and querying information about job offers and applicant profiles in the cloud. These methods exploit well-known recruitment knowledge bases in order to deliver valuable information to such organizations as public and private employment agencies that we assume to be geographically distributed. The rationale behind our approach is to offer an universal, yet inexpensive, distribution model able to reduce the cost of installing and maintaining the recruitment technology within the client's businesses.

Automatically providing suggestions for predicting the likely status of a mechanical component is a key challenge in a wide variety of industrial domains. Existing solutions based on ontological models have proven to be appropriate for fault diagnosis, but they fail when suggesting activities leading to a successful prognosis of mechanical components. The major reason is that fault prognosis is an activity that, unlike fault diagnosis, involves a lot of uncertainty and it is not always possible to envision a model for predicting possible faults. In this work, we propose a solution based on massive text mining for automatically suggesting prognosis activities concerning mechanical components. The great advantage of text mining is that it is possible to automatically analyze vast amounts of unstructured information in order to find strategies that have been successfully exploited, and formally or informally documented, in the past in any part of the world.