A Scalable Search Engine for Mass Storage Smart Objects

Nicolas Anciaux 1, 2 Saliha Lallali 1, 2 Iulian Sandu Popa 1, 2 Philippe Pucheral 2, 1
1 PRISM - Parallélisme, Réseaux, Systèmes, Modélisation
2 SMIS - Secured and Mobile Information Systems
PRISM - Parallélisme, Réseaux, Systèmes, Modélisation, UVSQ - Université de Versailles Saint-Quentin-en-Yvelines, Inria Paris-Rocquencourt, CNRS - Centre National de la Recherche Scientifique : UMR8144
Abstract : This paper presents a new embedded search engine designed for smart objects. Such devices are generally equipped with extremely low RAM and large Flash storage capacity. To tackle these conflicting hardware constraints, conventional search engines privilege either insertion or query scalability but cannot meet both requirements at the same time. Moreover, very few solutions support document deletions and updates in this context. In this paper, we introduce three design principles, namely Write-Once Partitioning, Linear Pipelining and Background Linear Merging, and show how they can be combined to produce an embedded search engine reconciling high insert/delete/update rate and query scalability. We have implemented our search engine on a development board having a hardware configuration representative for smart objects and have conducted extensive experiments using two representative datasets. The experimental results demonstrate the scalability of the approach and its superiority compared to state of the art methods.
Document type :
Conference papers
Complete list of metadatas
https://hal.inria.fr/hal-01176458
Contributor : Iulian Sandu Popa <>
Submitted on : Wednesday, July 15, 2015 - 2:28:39 PM
Last modification on : Tuesday, October 23, 2018 - 6:30:10 PM

Links full text

openaccess

Identifiers

Collections

UVSQ | PRISM | INRIA | UNIV-PARIS-SACLAY | INRIA-ROCQ

Citation

Nicolas Anciaux, Saliha Lallali, Iulian Sandu Popa, Philippe Pucheral. A Scalable Search Engine for Mass Storage Smart Objects. Proceedings of the 41th International Conference on Very Large Databases (VLDB), Aug 2015, Kohala Coast, Hawaii, United States. pp.910-921, ⟨10.14778/2777598.2777600⟩. ⟨hal-01176458⟩

Export

BibTeX TEI DC DCterms EndNote

Share

Metrics

Record views

306

Contact


archive-ouverte@inria.fr