Abstract
Relational database management systems (RDBMS) and
NoSQL database systems have been built in order to meet
dierent requirements. Organisations are therefore increasingly
using hybrid data persistence architectures, that we
call hybrid database systems or hybrid polystores. The evolution
of such systems is more complex as NoSQL technologies
imply new challenges such as handling data heterogeneity.
Existing work on NoSQL database evolution exposes the
dierent problems regarding data heterogeneity and mainly
provide technology-specic solutions. In this PhD research
we propose to apply a schema engineering approach to handle
hybrid database system evolution at a conceptual level.
Our starting point is an existing unied conceptual data
model for hybrid databases. Our thesis objectives include
(1) to enrich this conceptual data model with generic schema
evolution operators, (2) to specify the semantics of those
operators depending on the underlying data models, (3) to
design a generic evolution framework and (4) to develop its
proof-of-concept implementation. Our approach will help to
propagate conceptual schema changes to all impacted software
artefacts, namely native data structures, database contents,
queries and programs.
NoSQL database systems have been built in order to meet
dierent requirements. Organisations are therefore increasingly
using hybrid data persistence architectures, that we
call hybrid database systems or hybrid polystores. The evolution
of such systems is more complex as NoSQL technologies
imply new challenges such as handling data heterogeneity.
Existing work on NoSQL database evolution exposes the
dierent problems regarding data heterogeneity and mainly
provide technology-specic solutions. In this PhD research
we propose to apply a schema engineering approach to handle
hybrid database system evolution at a conceptual level.
Our starting point is an existing unied conceptual data
model for hybrid databases. Our thesis objectives include
(1) to enrich this conceptual data model with generic schema
evolution operators, (2) to specify the semantics of those
operators depending on the underlying data models, (3) to
design a generic evolution framework and (4) to develop its
proof-of-concept implementation. Our approach will help to
propagate conceptual schema changes to all impacted software
artefacts, namely native data structures, database contents,
queries and programs.
| Original language | English |
|---|---|
| Title of host publication | [Provisoire] Proceedings of the 46th International Conference on Very Large Data Bases (VLDB 2020) |
| Subtitle of host publication | PhD workshop track |
| Publisher | ACM Press |
| Publication status | Accepted/In press - 2020 |