Toward Scalable Hybrid Stores

Francesca Bugiotti 1, 2, 3, 4 Damian Bursztyn 3, 1, 4 Alin Deutsch 5, 4 Ioana Ileana 5, 4 Ioana Manolescu 1, 3, 4
1 OAK - Database optimizations and architectures for complex large data
LRI - Laboratoire de Recherche en Informatique, UP11 - Université Paris-Sud - Paris 11, Inria Saclay - Ile de France, CNRS - Centre National de la Recherche Scientifique : UMR8623
Abstract : Data centric applications often use heterogeneous datasets: some very large while others of moderate size, some highly structured (e.g., relations) while others complex structured (e.g., graphs) or little structured (e.g., log data). Facing them is a variety of storage systems but none of which is the best for all, at all times. We present Estocada, an architecture we are currently developing to efficiently handle highly heterogeneous datasets based on a dynamic set of potentially very different data stores. Estocada provides to the ap- plication layer access to each dataset in its native format, while hosting them internally in a set of potentially overlapping fragments, possibly distributed across heterogeneous stores. At the core of Estocada lie powerful view-based rewriting and view selection algorithms to marry correctness with high performance.
Document type :
Conference papers
Complete list of metadatas

Cited literature [17 references]  Display  Hide  Download

https://hal.inria.fr/hal-01174301
Contributor : Francesca Bugiotti <>
Submitted on : Friday, July 10, 2015 - 10:57:27 AM
Last modification on : Wednesday, August 7, 2019 - 12:18:47 PM
Long-term archiving on : Monday, October 12, 2015 - 11:29:09 AM

File

paper42.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-01174301, version 1

Citation

Francesca Bugiotti, Damian Bursztyn, Alin Deutsch, Ioana Ileana, Ioana Manolescu. Toward Scalable Hybrid Stores. SEBD Italian Symposium on Advanced Database Systems, Jun 2015, Gaeta, Italy. ⟨hal-01174301⟩

Share

Metrics

Record views

971

Files downloads

262