Ontology Versioning Driven by Instance Evolution in the τOWL Framework

Like other components of Semantic Web-based applications, ontologies are evolving over time to reflect changes in the real world. Several of these applications require keeping a full-fledged history of ontology changes so that both ontology instance versions and their corresponding ontology schema versions are maintained. Updates to an ontology instance could be non-conservative that is leading to a new ontology instance version no longer conforming to the current ontology schema version. If, for some reasons, a non-conservative update has to be executed, in spite of its consequence, it requires the production of a new ontology schema version to which the new ontology instance version is conformant so that the new ontology version produced by the update is globally consistent. In this paper, we first propose an approach that supports ontology schema changes which are triggered by non-conservative updates to ontology instances and, thus, gives rise to an ontology schema versioning driven by instance updates. Note that in an engineering perspective, such an approach can be used as an incremental ontology construction method driven by the modification of instance data, whose exact structure may not be completely known at the initial design time. After that, we apply our proposal to the already established [Formula: see text]OWL (Temporal OWL 2) framework, which allows defining and evolving temporal OWL 2 ontologies in an environment that supports temporal versioning of both ontology instances and ontology schemas, by extending it to also support the management of non-conservative updates to ontology instance versions. Last, we show the feasibility of our approach by dealing with its implementation within a new release of the [Formula: see text] OWL-Manager tool.

Comparing Open Data Repositories

Open Data is one of the main concepts of Open Science, which has the purpose to make scientific research artifacts accessible for everyone. Open data provides recommendations and practices to get access and use data from scientific researches, in a free, permanent, citable, auditable and interchangeable way. To facilitate the data management, it is important to store them in a repository. Considering this context, this paper provides a comparison among five known open data repositories. We performed the comparison taking into account a set of criteria, such as, data format constraints, digital identifier, versioning of published datasets, curators of data collections, metadata schema, versioning and exportation, storage limit, paid services, redundancy and preservation, access controls and APIs. We present results and discussions, in terms of such criteria.

Schema Versioning in Conventional and Emerging Databases

In information systems, not only do data change over time, but also database schemata evolve frequently as a response to evolving application requirements. In the literature, schema evolution and schema versioning are the two techniques that were proposed to support schema changes in a DBMS, without loss of extant data and with continued support of legacy applications. After applying schema changes, schema evolution keeps only the current schema version and retains the data which are adapted to such a schema. On the other hand, each time schema changes are applied, schema versioning creates a new schema version, while preserving old schema versions and their corresponding data. With schema versioning, data access through any schema version is supported, which avoids applications developed with past schemata to become obsolete. The main goal of this chapter is to present the recent research proposals that deal with schema versioning and to discuss the recent advances on schema versioning support in mainstream DBMSs.

Supporting Structural Evolution of Data in Web-Based Systems via Schema Versioning in the tXSchema Framework

τXSchema is a framework for creating and validating temporal XML documents, while using a temporal schema that consists of three components: a conventional XML schema document annotated with a set of temporal logical and physical annotations. Each one of these components can evolve over time to reflect changes in the real world. In addition, schema versioning has been long advocated to be the most efficient way to keep track of both data and schema evolution. Hence, in this chapter the authors complete τXSchema, which is predisposed from the origin to support schema versioning, by defining the operations that are necessary to exploit such a feature and make schema versioning functionalities available to end users. Precisely, the authors' approach provides a complete and sound set of change primitives and a set of high-level change operations, for the maintenance of each component of a τXSchema schema, and defines their operational semantics. Furthermore, they propose a new technique for schema versioning in τXSchema, allowing a complete, integrated, and safe management of schema changes.

Schema Versioning in Conventional and Emerging Databases

In information systems, not only do data change over time, but also database schemata evolve frequently as a response to evolving application requirements. In the literature, schema evolution and schema versioning are the two techniques that were proposed to support schema changes in a DBMS, without loss of extant data and with continued support of legacy applications. After applying schema changes, schema evolution keeps only the current schema version and retains the data which are adapted to such a schema. On the other hand, each time schema changes are applied, schema versioning creates a new schema version, while preserving old schema versions and their corresponding data. With schema versioning, data access through any schema version is supported, which avoids applications developed with past schemata to become obsolete. The main goal of this chapter is to present the recent research proposals that deal with schema versioning and to discuss the recent advances on schema versioning support in mainstream DBMSs.