Relational database reverse engineering : elicitation of generalization hierarchies

• Main Authors: Akoka, Jacky, Comyn-Wattiau, Isabelle, Lammari, Nadira
• Other Authors: Centre d'études et de recherche en informatique et communications (CEDRIC), Ecole Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise (ENSIIE)-Conservatoire National des Arts et Métiers CNAM (CNAM), HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM), Département Systèmes d'Information (DSI), Télécom Ecole de Management (TEM)-Institut Mines-Télécom Paris (IMT)-Institut Mines-Télécom Business School (IMT-BS), Institut Mines-Télécom Paris (IMT), Parallélisme, Réseaux, Systèmes, Modélisation (PRISM), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Centre National de la Recherche Scientifique (CNRS), ESSEC Business School, Essec Business School
• Format: Conference Object
• Language: English
• Published: HAL CCSD 1999
• Subjects: [INFO]Computer Science [cs]; [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB]; DML
• Online Access: https://hal.science/hal-01124843; https://doi.org/10.1007/3-540-48054-4_15

International audience This paper describes a method aiming at the extraction of generalization/specialization hierarchies contained in a relational database. This reverse engineering approach takes advantage of two major characteristics: first, we use DDL and DML specifications as well as data in a combined way, secondly, we provide not only generalization/specialization hierarchies but also integrity constraints allowing us to elicit the generalization/specialization links hidden in the structures and instances of the database. The result of the process consists of an enriched conceptual representation of the relational database. This approach is mainly based on heuristics. The heuristic rules map a relational meta-model onto a conceptual one. They are divided into three categories: semantics suspicion rules, reinforcement rules and confirmation rules. We illustrate our approach using a fairly complex example. Some extensions are discussed.