Database evolution: the DB-MAIN approach

Lecture Notes in Computer Science, 2003

While recent data management technologies, e.g., object-oriented, address the problem of databases schema evolution, standard information systems currently in use raise challenging problems when evolution is concerned. This paper studies database evolution from the developer point of view. It shows how requirements changes are propagated to the database schemas, to the data and to the programs through a general strategy. This strategy requires the documentation of the database design. When absent, this documentation has to be rebuilt through reverse engineering techniques. The approach relies on a generic database model and on the transformational paradigm that states that database engineering processes can be modelled by schema transformations. Indeed, a transformation provides both structural and instance mappings that formally define how to modify database structures and contents. The paper then analyses the problem of program modification and describes a CASE tool that can assist developers in their task of system evolution.

Data & Knowledge Engineering, 2006

While recent data management technologies, such as object oriented techniques, address the problem of database schema evolution, standard information systems currently in use raise challenging evolution problems. This paper examines database evolution from the developer point of view. It shows how requirements changes are propagated to database schemas, to data and to programs through a general strategy. This strategy requires the documentation of database design. When absent, such documentation has to be rebuilt through reverse engineering techniques. Our approach, called DB-MAIN, relies on a generic database model and on transformational paradigm that states that database engineering processes can be modeled by schema transformations. Indeed, a transformation provides both structural and instance mappings that formally define how to modify database structures and contents. We describe both the complete and a simplified approaches, and compare their merits and drawbacks. We then analyze the problem of program modification and describe a CASE tool that can assist developers in their task of system evolution. We illustrate our approach with Biomaze, a biochemical knowledge-based the database of which is rapidly evolving.

Lecture Notes in Computer Science, 2003

This paper presents an architecture for managing database evolution when all the components of the database (conceptual schema, logical schema and extension) are available. The strategy of evolution in which our architecture is based is that of 'forward database maintenance', that is, changes are applied to the conceptual schema and propagated automatically down to the logical schema and to the extension. In order to put into practice this strategy, each component of a database is seen under this architecture as the information base of an information system. Furthermore, a translation information system is considered in order to manage the translation of conceptual elements into logical schema elements. A current Oracle implementation of this architecture is also presented.

Many research efforts have led to robust database initial design methodologies. However, database evolutions suffer from lack of structured approaches. Using high-level conceptualization models like ontologies, we describe an original approach to database evolution management. Our aim is to help the maintenance engineer in 1) characterizing the evolution, 2) finding the best techniques and tools to deal with this evolution. Our guidance approach provides this help by linking formally a change ontology and a maintenance method ontology through an algorithmic approach. A historization process of evolutions enriches the set of information delivered to the maintenance engineer. Our future work consists in prototyping this approach.

2000

This paper addresses the crucial problem of changes and evolution in the context of database systems. We argue that the importance of change depends primarily on its nature. The nature of change impacts one or several phases of the database life-cycle. We propose a typology of changes based on three dimensions, namely the nature, the significance, and the time frame.

2018 IEEE International Conference on Software Maintenance and Evolution (ICSME), 2018

Modern relational database management systems provide advanced features allowing, for example, to include behaviour directly inside the database (stored procedures). These features raise new difficulties when a database needs to evolve (e.g. adding a new table). To get a better understanding of these difficulties, we recorded and studied the actions of a database architect during a complex evolution of the database at the core of a software system. From our analysis, problems faced by the database architect are extracted, generalized and explored through the prism of software engineering. Six problems are identified: (1) difficulty in analysing and visualising dependencies between database's entities, (2) difficulty in evaluating the impact of a modification on the database, (3) replicating the evolution of the database schema on other instances of the database, (4) difficulty in testing database's functionalities, (5) lack of synchronization between the IDE's internal model of the database and the database actual state and (6) absence of an integrated tool enabling the architect to search for dependencies between entities, generate a patch or access up to date PostgreSQL documentation. We suggest that techniques developed by the software engineering community could be adapted to help in the development and evolution of relational databases.

Abstract. In client/server architectures it is common for software components to have SQL statements embedded in component's source language. The components submit queries to relational databases using such standards as Universal Data Access (UDA) and Open Database Connectivity (ODBC). Such programming practices are widespread and result in poor maintenance and reuse of the components.

2007

International Journal of Computer Applications, 2017

Since 90's, database has shown tremendous growth. This growth can be determined in different aspects. Different demands of every era give database a new bunch of challenges. To achieve that challenges, researchers come up with different ideas and combinations. These various combinations enhance features of database and this way database starts evolving from one period to another. Database that we had in 1960 is entirely, absolutely different from what we have now. This survey addresses the evolution of database from beginning to today. It also introduces that how different technologies, concepts and theories helped database to evolve, as just one good player in a team is not enough to win the match. The fact is still industries are in hope for something better in context to database technologies.

Journal of Documentation, 1999

This paper describes research work carried out with the aim to investigate dynamically evolving database environments and corresponding schemata, allowing storage and manipulation of variable length data, a variable number of fields per record, variable length records, manipulation of authority records and links between records and fields, and dynamically defined objects (relations in the traditional sense). The paper proposes a new framework for the definition of a unified schema that eliminates completely the need for reorganisation at both logical and internal levels. Retrieval of data is optimised through self‐contained storage chunks that also vary dynamically.