Several application domains deal with the management of massive data volumes and thousands of OLTP transactions per second. Traditional relational databases cannot cope with these requirements. NewSQL is a new generation of databases that provides both high scalability and availability and ACID properties support. Besides, it is a promising solution to handle these application data management needs. Although data partitioning is an essential feature for tuning relational databases, stills an open issue for NewSQL systems. In this paper, we propose a hybrid partitioning approach for NewSQL databases that allows the user to define the vertical and horizontal data partitions. In order to determine what site will store each data fragment, we propose a hash function that considers schema information and data access statistics. Our experimental evaluation compares our hybrid VoltDB version against the standard VoltDB. The results highlight that our strategy increases the number of single-site transactions from 37% to 76%.

To integrate ontologies and relational databases, in this paper, we propose to build a common meta-schema upon an overall unified lifecycle. Our key idea is to consider that the database is a model for the domain ontology, and conversely. To this end, the ontology-based knowledge and the business-entities are merged with the same conceptual abstractions principles, according to the three-level ANSI/SPARC architecture. Finally, the goal is to show that ontologies' features can be managed by relational databases. A case of study outlines the applicability of our approach.

Record deduplication aims at identifying entities that are potentially the same in a data repository. A set of pairs that is manually labeled is generally used to tune the deduplication process, as each dataset has a particular dirtiness pattern. However, producing an informative set of pairs is a very costly task, especially in very large datasets (even for expert users). We propose a new sampling strategy that is able to select a very small and informative set of pairs from large datasets. Our results show that our approach reduces user effort substantially while achieving a competitive or superior matching quality.

The data deduplication task has attracted a considerable amount of attention from the research community in order to provide effective and efficient solutions. The information provided by the user to tune the deduplication process is usually represented by a set of manually labeled pairs. In very large datasets, producing this kind of labeled set is a daunting task since it requires an expert to select and label a large number of informative pairs. In this article, we propose a two-stage sampling selection strategy (T3S) that selects a reduced set of pairs to tune the deduplication process in large datasets. T3S selects the most representative pairs by following two stages. In the first stage, we propose a strategy to produce balanced subsets of candidate pairs for labeling. In the second stage, an active selection is incrementally invoked to remove the redundant pairs in the subsets created in the first stage in order to produce an even smaller and more informative training set. This training set is effectively used both to identify where the most ambiguous pairs lie and to configure the classification approaches. Our evaluation shows that T3S is able to reduce the labeling effort substantially while achieving a competitive or superior matching quality when compared with state-of-the-art deduplication methods in large datasets.

Deduplication is the task of identifying which objects are potentially the same in a data repository. It usually demands user intervention in several steps of the process, mainly to identify some pairs representing matchings and non-matchings. This information is then used to help in identifying other potentially duplicated records. When deduplication is applied to very large datasets, the performance and matching quality depends on expert users to configure the most important steps of the process (e.g., blocking and classification). In this paper, we propose a new framework called FS-Dedup able to help tuning the deduplication process on large datasets with a reduced effort from the user, who is only required to label a small, automatically selected, subset of pairs. FS-Dedup exploits Signature-Based Deduplication (Sig-Dedup) algorithms in its deduplication core. Sig-Dedup is characterized by high efficiency and scalability in large datasets but requires an expert user to tune several parameters. FS-Dedup helps in solving this drawback by providing a framework that does not demand specialized user knowledge about the dataset or thresholds to produce high effectiveness. Our evaluation over large real and synthetic datasets (containing millions of records) shows that FS-Dedup is able to reach or even surpass the maximal matching quality obtained by Sig-Dedup techniques with a reduced manual effort from the user.