In the context of decision-making, there is a growing demand for near real-time data that traditional solutions, like data warehousing based on long-running ETL processes, cannot fully meet. On the other hand, existing logical data integration solutions are challenging because users must focus on data location and distribution details rather than on data analytics and decision-making. EasyBDI is an open-source system that provides logical integration of data and high-level business-oriented abstractions. It uses schema matching, integration, and mapping techniques, to automatically identify partitioned data and propose a global schema. Users can then specify star schemas based on global entities and submit analytical queries to retrieve data from distributed data sources without knowing the organization and other technical details of the underlying systems. This work presents the algorithms and methods for global schema creation and query execution. Experimental results show that the overhead imposed by logical integration layers is relatively small compared to the execution times of distributed queries.

Despite data growth being a known problem for several years, there are more and more people, tools and devices to create and share data, and the need for tools to infer their provenance and quality is even more important than before. Research on data provenance focuses on W3C PROV and databases (where, why, how). However, in the particular case of spatial data, research has mainly focused on handling spatial data provenance from documents and workflows, but there is no literature approaching the topic of spatial data provenance in DBMS and queries.

The large volume of currently available data creates several opportunities for sciences and industry, especially with the application of data analytics. But also raises challenges that make unfeasible the use of batch-based ETL processes. Indeed, near real-time data analytics is a requirement in several domains as an alternative to traditional data warehouses. In the last years, big data platforms have been developed to enable query execution over distributed data sources. However, they do not deal with subject-oriented analysis, do not provide data distribution transparency, or do not assist with schema mapping and integration. In this demonstration, we present EasyBDI. It's a near real-time big data analytics prototype that enables users to run queries over heterogeneous data sources based on global logical abstractions created by the system and provides some usual concepts of data warehouse systems, like facts and dimensions. We use two motivating scenarios, one based on three years of real data on photovoltaic energy production and consumption, and the other based on the SSB+ benchmark. We will also present implementation challenges, issues, solutions, and insights.

Spatio-temporal data on the evolution of real-world phenomena are normally acquired as snapshots in discrete time. The continuous evolution of a phenomenon between observations can be approximated using interpolation methods capable of generating deformable moving regions. Several region interpolation methods have been proposed in the spatio-temporal databases literature, each one with its own characteristics that can be more suited to represent the evolution of specific physical phenomena.

In this work, we propose the use of a size-based strategy to estimate the execution time of database commands. Proposed strategy classifies database commands into small or regular and long-running according to their estimated sizes and applies diverse approaches to estimate command's execution time considering specified classes. We prototyped proposed strategy and made several tests. Obtained results prove that our strategy leads to good execution time estimations without affecting system's performance.

Database servers typically offer a best-effort model of service to submitted commands, that is, they try to process every command as fast as possible. Hence, they are not prepared to provide differentiation for quality of service. In this paper we consider the distributed Grid-DWPA architecture context, which fragments and replicates data into several sites to provide an efficient grid data warehouse solution. Instead of offering best-effort service to every query, we propose the use of a performance predict model that is used in conjunction with QoS oriented scheduling to enable the establishment of Service Level Agreements (SLA).