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.

The provision of public data through various Database-as-a-Service (DBaaS) providers has recently emerged as a significant trend, backed by major organisations. This paper introduces Nebula, a non-profit middleware providing multi-cloud querying capabilities by fully outsourcing its users' queries to the involved DBaaS providers. First, we propose a quoting procedure for those queries, whose need stems from the pay-per-query policy of the providers. Those quotations contain monetary cost and response time estimations, and are computed using provider-generated tenders. Then, we present an agent-based dynamic optimisation engine that orchestrates the outsourced execution of the queries. Agents within this engine cooperate in order to meet the quoted values. We evaluated Nebula over simulated providers by using the Join Order Benchmark (JOB). Experimental results showed Nebula's approach is, in most cases, more competitive in terms of monetary cost and response time than existing work in the multi-cloud DBMS literature.

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.

In the cloud context, users are often called tenants. A cloud DBMS shared by many tenants is called a multi-tenant DBMS. The resource consolidation in such a DBMS allows the tenants to only pay for the resources that they consume, while providing the opportunity for the provider to increase its economic gain. For this, a Service Level Agreement (SLA) is usually established between the provider and a tenant. However, in the current systems, the SLA is often defined by the provider, while the tenant should agree with it before using the service. In addition, only the availability objective is described in the SLA, but not the performance objective. In this paper, an SLA negotiation framework is proposed, in which the provider and the tenant define the performance objective together in a fair way. To demonstrate the feasibility and the advantage of this framework, we evaluate its impact on query optimization. We formally define the problem by including the cost-efficiency aspect, we design a cost model and study the plan search space for this problem, we revise two search methods to adapt to the new context, and we propose a heuristic to solve the resource contention problem caused by concurrent queries of multiple tenants. We also conduct a performance evaluation to show that, our optimization approach (i.e., driven by the SLA) can be much more cost-effective than the traditional approach which always minimizes the query completion time.

We propose a new method for approximate k-NN search in large scale image databases, based on top-k multi-criteria search techniques. The method defines a simple index structure based on sorted lists, which provides a good compromise between fast retrieval, storage requirements and update cost. The search algorithm delivers approximate results with guarantees about false negatives, with fast emergence of good approximations, monotonically improved and leading if necessary to an exact result. Experiments with the on-disk implementation show that our method produces very good approximate results several times faster than the Baseline method.

Resource allocation is one of the principal stages of query processing in relational data grid systems. Specific characteristics of the data grid environment, such as dynamicity, heterogeneity and large scale, impose serious restrictions to the resource allocation process. Static resource allocation before the query execution may be far from optimal due to the dynamic changes of the system. One possible optimization is to adjust dynamically the allocation of resources during the query execution. Some methods of dynamic resource allocation have been proposed, however, most of them use centralized control mechanisms. In this study we argue that the decentralized approach meets better the requirements of the data grid systems. In this study we propose a decentralized method of dynamic resource allocation that is based on the mobile agent paradigm. We consider the participating nodes as autonomous and independent elements of the system, each of which can detect if it is overloaded and make the decision to react. Then we consider each relational operation as a mobile agent running on the allocated node, meaning that, it keeps track of its own status and can migrate to another node at any time. A two-level cooperation mechanism between such autonomous nodes and autonomous operations is described in detail. Performance evaluation proves the efficiency of the proposed method.

An increasing amount of personal data is automatically gathered on servers by administrations, hospitals and private companies while several security surveys highlight the failure of database servers to keep confidential data really private. The advent of powerful secure tokens, combining the security of smart card microcontrollers with the storage capacity of NAND Flash chips, introduces a credible alternative to the systematic centralization of personal data. By embedding a full-fledged database server in such device, an individual can now store her personal data in her own secure token, kept under her control, and never disclose in clear her private data to the outside untrusted world. This demonstration shows the benefit of the proposed approach in terms of privacy protection and pervasiveness through a healthcare scenario. This scenario is extracted from a field experiment where medical folders embedded in secure tokens are used to improve the coordination of medical care at home for elderly people. The demonstration also highlights interesting features of the embedded DBMS engine introduced to tackle the secure token's strong hardware constraints.

An increasing amount of personal data is automatically gathered and stored on servers by administrations, hospitals, insurance companies, etc. Citizen themselves often count on internet companies to store their data and make them reliable and highly available through the internet. However, these benefits must be weighed against privacy risks incurred by centralization. This paper suggests a radically different way of considering the management of personal data. It builds upon the emergence of new portable and secure devices combining the security of smart cards and the storage capacity of NAND Flash chips. By embedding a full-fledged Personal Data Server in such devices, user control of how her sensitive data is shared by others (by whom, for how long, according to which rule, for which purpose) can be fully reestablished and convincingly enforced. To give sense to this vision, Personal Data Servers must be able to interoperate with external servers and must provide traditional database services like durability, availability, query facilities, transactions. This paper proposes an initial design for the Personal Data Server approach, identifies the main technical challenges associated with it and sketches preliminary solutions. We expect that this paper will open exciting perspectives for future database research.

NAND Flash has become the most popular stable storage medium for embedded systems. As on-board storage capacity increases, the need for efficient indexing techniques arises. Such techniques are very challenging to design due to a combination of NAND Flash constraints (for example the block-erase-before-page-rewrite constraint and limited number of erase cycles) and embedded system constraints (for example tiny RAM and resource consumption predictability). Previous work adapted traditional indexing methods to cope with Flash constraints by deferring index updates using a log and batching them to decrease the number of rewrite operations in Flash memory. However, these methods were not designed with embedded system constraints in mind and do not address them. In this paper, we propose a new alternative for indexing Flash-resident data that specifically addresses the embedded context. This approach, called PBFilter, organizes the index structure in a purely sequential way. Key lookups are sped up thanks to two principles called Summarization and Partitioning. We instantiate these principles with data structures and algorithms based on Bloom Filters and show the effectiveness of this approach through a comprehensive performance study.

Paper-based folders have been widely used to coordinate cares in medical-social networks, but they introduce some burning issues (e.g. privacy protection, remote access to the folder). Replacing the paper-based folder system by a traditional Electronic Healthcare Record (EHR) introduces new drawbacks: forcing of the patient consent, unbounded data retention, no security guarantee outside the server domain and no disconnected access to the folder. To solve these problems, this paper proposes an experimental platform which combines an EHR system with medical-social folders embedded in a new hardware portable device. The objectives pursued are (1) to re-establish a natural and powerful way of protecting and sharing highly sensitive information among trusted parties and (2) to build a shared medical-social folder providing the highest degree of availability, whatever the mode of operation (disconnected or not).

NAND Flash has become the most popular persistent data storage medium for mobile and embedded devices. The hardware characteristics of NAND Flash (e.g. page granularity for read/write with a block-erase-before-rewrite constraint, limited number of erase cycles) preclude in-place updates. In this paper, we propose a new indexing scheme, called PBFilter, designed from the outset to exploit the peculiarities of NAND Flash.