Fabio Grandi

Changes involving the database schema are unavoidable events that sometimes occur in data-centric systems, due to many reasons like evolution of user requirements, corrective maintenance, improvement of the current schema with the addition of new integrity constraints, implementation of new business rules, or compliance to new regulations. In order to manage schema changes, researchers have proposed a technique named “schema versioning”, in which all versions of the database schema are kept along with their underlying data, preventing loss of information and obsolescence of legacy applications. This technique has been widely studied in both conventional and emerging databases. Despite lot of interesting research efforts that have dealt with database schema versioning and the importance of this technique for database maintenance, current Database Management Systems (DBMSs) do not provide sufficient support for its implementation. This paper (i) gives an overview of state-of-the-art research approaches to schema versioning in databases, based on the study of more than 270 papers and providing a historical perspective on the development of the topic, (ii) illustrates the support of schema versioning in commercial DBMSs, and (iii) points out some possible future research directions in this area.

Changing a database schema is a fact of life in information systems, as a response to changes inside the enterprise (e.g., new users’ requirements, correction of errors in the current database schema) or outside it (e.g., new regulations, new partners’ requirements). In the database research field, a well-known technique has been proposed for managing schema changes, called schema evolution. It allows the database to survive schema changes by adapting existing data to conform to the new schema. A lot of research efforts addressed the topic of schema evolution, in both conventional (i.e., relational) and advanced (e.g., XML, stream, NoSQL) databases, providing a plethora of heterogeneous approaches and solutions making up a quite large literature. Since there is no research work that extensively deals with different proposals and compares them, the purpose of this paper is to fill this gap by reviewing the available schema evolution literature. For that, first we collected and summarized the contributions of research papers dealing with database schema evolution. Then we organized their presentation in a chronological order, also giving a historical perspective on the topic development. Finally, we defined a list of six comparison criteria (database model, implementation, schema change semantics, schema change propagation, integrity constraints, and software evolution) that have helped us to categorize and compare the different database schema evolution proposals. In sum, our paper (i) provides an overview of the state-of-the-art research approaches on database schema evolution, with tables that compare such approaches based on some proposed criteria, (ii) studies the support of schema evolution in commercial DataBase Management Systems (DBMSs), and (iii) points out some possible future research directions in this area.

Temporal ontologies allow to represent not only concepts, their properties, and their relationships, but also time-varying information through explicit versioning of definitions or through the four-dimensional perdurantist view. They are widely used to formally represent temporal data semantics in several applications belonging to different fields (e.g., Semantic Web, expert systems, knowledge bases, big data, and artificial intelligence). They facilitate temporal knowledge representation and discovery, with the support of temporal data querying and reasoning. However, there is no standard or consensual temporal ontology query language. In a previous work, we have proposed an approach named τJOWL (temporal OWL 2 from temporal JSON, where OWL 2 stands for “OWL 2 Web Ontology Language” and JSON stands for “JavaScript Object Notation”). τJOWL allows (1) to automatically build a temporal OWL 2 ontology of data, following the Closed World Assumption (CWA), from temporal JSON-based big data, and (2) to manage its incremental maintenance accommodating their evolution, in a temporal and multi-schema-version environment. In this paper, we propose a temporal ontology query language for rJOWL, named rSQWRL (temporal SQWRL), designed as a temporal extension of the ontology query language-Semantic Query-enhanced Web Rule Language (SQWRL). The new language has been inspired by the features of the consensual temporal query language TSQL2 (Temporal SQL2), well known in the temporal (relational) database community. The aim of the proposal is to enable and simplify the task of retrieving any desired ontology version or of specifying any (complex) temporal query on time-varying ontologies generated from time-varying big data. Some examples, in the Internet of Healthcare Things (IoHT) domain, are provided to motivate and illustrate our proposal.