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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.

Abstract A data integration approach combines data from different sources and builds a unified view for the users. Big data integration inherently is a complex task, and the existing approaches are either potentially limited or invariably rely on manual inputs and interposition from experts or skilled users. SemLinker, an ontology-based data integration system, is part of a metadata management framework for personal data lake (PDL), a personal store-everything architecture. PDL is for casual and unskilled users, therefore SemLinker adopts an automated data integration workflow to minimize manual input requirements. To support the flat architecture of a lake, SemLinker builds and maintains a schema metadata level without involving any physical transformation of data during integration, preserving the data in their native formats while, at the same time, allowing them to be queried and analyzed. Scalability, heterogeneity, and schema evolution are big data integration challenges that are addressed by SemLinker. Large and real-world datasets of substantial heterogeneities are used in evaluating SemLinker. The results demonstrate and confirm the integration efficiency and robustness of SemLinker, especially regarding its capability in the automatic handling of data heterogeneities and schema evolutions.